{
  "generated_at": "2026-05-20T12:35:29.187129+00:00",
  "type": "df-writes",
  "summary": {
    "total": 7522,
    "by_status": {
      "pass": 7520,
      "fail": 0,
      "skip": 1,
      "partial": 0,
      "pending": 1
    },
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        "fail": 0,
        "skip": 0,
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        "pending": 0
      },
      "iceberg": {
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        "fail": 0,
        "skip": 1,
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        "pending": 1
      }
    },
    "by_skip_cause": {
      "unknown": 1
    },
    "executable": {
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      "pass": 7520,
      "fail": 0,
      "pass_rate": 1.0
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  },
  "tests": [
    {
      "id": "df-writes/delta/01_basic_data_files_parquet",
      "num": 1,
      "name": "basic_data_files_parquet",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/01_basic_data_files_parquet.sql",
      "read_script": "generator/spark-reads-df/verify_01_basic_data_files_parquet.py",
      "description": "Validates the Delta table written by DeltaForge for test 01. No UPDATE or DELETE. 9 columns. 5 categories, 2 statuses, 3 priority levels.",
      "status": "pass",
      "duration_ms": 10243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:36:29.660300+00:00",
      "read_cold_ms": 8787,
      "read_warm_ms": 550,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 153,
      "tags": [
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/02_deletion_vector_files_external",
      "num": 2,
      "name": "deletion_vector_files_external",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/02_deletion_vector_files_external.sql",
      "read_script": "generator/spark-reads-df/verify_02_deletion_vector_files_external.py",
      "description": "- DELETE operations that create external deletion vector files (.bin) - Multiple deletion predicates demonstrating DV accumulation - Boolean, string, and numeric predicates - **Modulo operator in DELETE predicates** (`column % divisor = remainder`) - LIKE pattern matching in...",
      "status": "pass",
      "duration_ms": 3884,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:36:33.545152+00:00",
      "read_cold_ms": 2431,
      "read_warm_ms": 743,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 147,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/03_change_data_capture_files",
      "num": 3,
      "name": "change_data_capture_files",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/03_change_data_capture_files.sql",
      "read_script": "generator/spark-reads-df/verify_03_change_data_capture_files.py",
      "description": "- Change Data Capture (CDC) with delta.enableChangeDataFeed = true - Multiple UPDATE operations with arithmetic expressions (price * 0.85) - UPDATE with multiple assignments - UPDATE with compound predicates (AND) - DELETE with modulo operator - INSERT for new products - MERGE...",
      "status": "pass",
      "duration_ms": 3421,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:36:36.969987+00:00",
      "read_cold_ms": 2110,
      "read_warm_ms": 648,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 490,
      "write_warm_ms": 349,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/04_delta_log_json_entries",
      "num": 4,
      "name": "delta_log_json_entries",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/04_delta_log_json_entries.sql",
      "read_script": "generator/spark-reads-df/verify_04_delta_log_json_entries.py",
      "description": "- Version 0: CREATE TABLE + INSERT 1000 events (1-1000) - Version 1: INSERT 500 events (1001-1500) - Version 2: UPDATE mobile page_view -> mobile_page_view - Version 3: DELETE event_id IN (100,200,...,1500) OR (revenue IS NULL AND event_type='purchase') - Version 4: INSERT 500...",
      "status": "pass",
      "duration_ms": 3550,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:36:40.521110+00:00",
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      "read_warm_ms": 649,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 128,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/05_checkpoint_v1_classic_single",
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      "name": "checkpoint_v1_classic_single",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/05_checkpoint_v1_classic_single.sql",
      "read_script": "generator/spark-reads-df/verify_05_checkpoint_v1_classic_single.py",
      "description": "- Checkpoint V1 classic single-file format - Deletion vectors enabled - Multiple UPDATE operations with range predicates - DELETE operations with IN lists - OPTIMIZE for file compaction",
      "status": "pass",
      "duration_ms": 3737,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:36:44.259147+00:00",
      "read_cold_ms": 2367,
      "read_warm_ms": 690,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 871,
      "write_warm_ms": 524,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-v1",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/06_checkpoint_v2_spec_format",
      "num": 6,
      "name": "checkpoint_v2_spec_format",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/06_checkpoint_v2_spec_format.sql",
      "read_script": "generator/spark-reads-df/verify_06_checkpoint_v2_spec_format.py",
      "description": "- V2 checkpoint specification format - Deletion vectors enabled - Complex data types including Date32 and Timestamp - Boolean columns - Multiple UPDATE and DELETE operations - No-op operations (UPDATE/DELETE that affect 0 rows)",
      "status": "pass",
      "duration_ms": 6827,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:36:51.086783+00:00",
      "read_cold_ms": 2294,
      "read_warm_ms": 1012,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 610,
      "write_warm_ms": 571,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-v2",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/07_checkpoint_multipart_split",
      "num": 7,
      "name": "checkpoint_multipart_split",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/07_checkpoint_multipart_split.sql",
      "read_script": "generator/spark-reads-df/verify_07_checkpoint_multipart_split.py",
      "description": "- Multi-part checkpoint split across multiple files - Large table (100,000+ rows) with wide schema (26 columns) - Deletion vectors enabled - Multiple DECIMAL columns with various precision/scale - Complex fee calculation using integer arithmetic - Date32 and Timestamp columns -...",
      "status": "pass",
      "duration_ms": 9730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:37:00.818022+00:00",
      "read_cold_ms": 2604,
      "read_warm_ms": 694,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1422,
      "write_warm_ms": 1371,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-multipart",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/08_checkpoint_uuid_named_v2",
      "num": 8,
      "name": "checkpoint_uuid_named_v2",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/08_checkpoint_uuid_named_v2.sql",
      "read_script": "generator/spark-reads-df/verify_08_checkpoint_uuid_named_v2.py",
      "description": "- UUID-named V2 checkpoint files - Large table (30,000+ rows) with 23 columns - Deletion vectors enabled - DATE32 and TIMESTAMP columns - Multiple DECIMAL columns (weight_kg, shipping_cost, cost_per_kg) - Boolean columns (is_international, is_heavy_shipment) - Complex business...",
      "status": "pass",
      "duration_ms": 4917,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:37:05.736207+00:00",
      "read_cold_ms": 2423,
      "read_warm_ms": 617,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 870,
      "write_warm_ms": 767,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/09_checkpoint_with_sidecar_files",
      "num": 9,
      "name": "checkpoint_with_sidecar_files",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/09_checkpoint_with_sidecar_files.sql",
      "read_script": "generator/spark-reads-df/verify_09_checkpoint_with_sidecar_files.py",
      "description": "- V2 checkpoint with sidecar files - Large table (60,000+ rows) with 28 columns - Deletion vectors enabled - DATE32 and TIMESTAMP columns - Multiple Boolean computed columns - Complex business logic with event analytics - Multiple UPDATE and DELETE operations - OPTIMIZE...",
      "status": "pass",
      "duration_ms": 6197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:37:11.934119+00:00",
      "read_cold_ms": 1706,
      "read_warm_ms": 704,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 899,
      "write_warm_ms": 922,
      "tags": [
        "type:boolean",
        "type:date",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-sidecar",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1000_update_ultimate",
      "num": 1000,
      "name": "update_ultimate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1000_update_ultimate.sql",
      "read_script": "generator/spark-reads-df/verify_1000_update_ultimate.py",
      "description": "ULTIMATE UPDATE test combining all features:",
      "status": "pass",
      "duration_ms": 8209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:37.549763+00:00",
      "read_cold_ms": 6352,
      "read_warm_ms": 466,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 288,
      "write_warm_ms": 498,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1001_insert_int_types",
      "num": 1001,
      "name": "insert_int_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1001_insert_int_types.sql",
      "read_script": "generator/spark-reads-df/verify_1001_insert_int_types.py",
      "description": "INSERT with all integer types: INT, SMALLINT, TINYINT, BIGINT.",
      "status": "pass",
      "duration_ms": 6271,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:30.177278+00:00",
      "read_cold_ms": 4211,
      "read_warm_ms": 777,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 18,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1002_insert_float_double",
      "num": 1002,
      "name": "insert_float_double",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1002_insert_float_double.sql",
      "read_script": "generator/spark-reads-df/verify_1002_insert_float_double.py",
      "description": "INSERT FLOAT and DOUBLE precision values.",
      "status": "pass",
      "duration_ms": 4253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:34.431545+00:00",
      "read_cold_ms": 2803,
      "read_warm_ms": 791,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 15,
      "write_warm_ms": 15,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1003_insert_decimal_four_precisions",
      "num": 1003,
      "name": "insert_decimal_four_precisions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1003_insert_decimal_four_precisions.sql",
      "read_script": "generator/spark-reads-df/verify_1003_insert_decimal_four_precisions.py",
      "description": "INSERT DECIMAL at 4 different precision/scale combos.",
      "status": "pass",
      "duration_ms": 4511,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:38.944590+00:00",
      "read_cold_ms": 3388,
      "read_warm_ms": 602,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 30,
      "write_warm_ms": 18,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1004_insert_decimal_negative",
      "num": 1004,
      "name": "insert_decimal_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1004_insert_decimal_negative.sql",
      "read_script": "generator/spark-reads-df/verify_1004_insert_decimal_negative.py",
      "description": "INSERT negative DECIMAL values.",
      "status": "pass",
      "duration_ms": 4448,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:43.401226+00:00",
      "read_cold_ms": 3011,
      "read_warm_ms": 668,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 26,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1005_insert_decimal_zero",
      "num": 1005,
      "name": "insert_decimal_zero",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1005_insert_decimal_zero.sql",
      "read_script": "generator/spark-reads-df/verify_1005_insert_decimal_zero.py",
      "description": "INSERT DECIMAL with exact zeros and near-zeros.",
      "status": "pass",
      "duration_ms": 4089,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:47.542744+00:00",
      "read_cold_ms": 2611,
      "read_warm_ms": 753,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 16,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1006_insert_decimal_max_precision",
      "num": 1006,
      "name": "insert_decimal_max_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1006_insert_decimal_max_precision.sql",
      "read_script": "generator/spark-reads-df/verify_1006_insert_decimal_max_precision.py",
      "description": "INSERT DECIMAL(38,0) and DECIMAL(38,18) at maximum scale.",
      "status": "pass",
      "duration_ms": 5245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:52.789240+00:00",
      "read_cold_ms": 3026,
      "read_warm_ms": 1059,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 14,
      "write_warm_ms": 21,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1007_insert_timestamp_microsecond",
      "num": 1007,
      "name": "insert_timestamp_microsecond",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1007_insert_timestamp_microsecond.sql",
      "read_script": "generator/spark-reads-df/verify_1007_insert_timestamp_microsecond.py",
      "description": "INSERT TIMESTAMP with microsecond precision.",
      "status": "pass",
      "duration_ms": 5282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:58:58.072390+00:00",
      "read_cold_ms": 3158,
      "read_warm_ms": 1163,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 36,
      "tags": [
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      ],
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    },
    {
      "id": "df-writes/delta/1008_insert_timestamp_daily",
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    },
    {
      "id": "df-writes/delta/1009_insert_timestamp_fixed",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/100_multipart_checkpoint_missing_parts",
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      "status": "pass",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1010_insert_date_type",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1011_insert_boolean_patterns",
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      "description": "INSERT BOOLEAN with various distribution patterns.",
      "status": "pass",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1012_insert_string_patterns",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1013_insert_null_per_type",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1014_insert_all_null_row",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4919,
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      "verified_at": "2026-05-19T16:59:31.667258+00:00",
      "read_cold_ms": 2771,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1015_insert_struct_basic",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1016_insert_struct_nested",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1017_insert_struct_with_null",
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      "reader": "spark",
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      "description": "INSERT STRUCT where some fields are NULL.",
      "status": "pass",
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      "read_cold_ms": 2574,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1018_insert_values_clause",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4334,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1019_insert_multi_batch_same_schema",
      "num": 1019,
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      "reader": "spark",
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      "description": "10 separate INSERT batches into same table.",
      "status": "pass",
      "duration_ms": 6283,
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      "read_cold_ms": 3538,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/101_schema_evolution_column_drops",
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      "description": "Schema (16 columns) - PII columns already dropped for GDPR compliance 3 INSERT batches + 2 UPDATEs",
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    },
    {
      "id": "df-writes/delta/1020_insert_multi_batch_growing",
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    {
      "id": "df-writes/delta/1021_insert_overwrite_typed",
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      "id": "df-writes/delta/1022_insert_cte_complex",
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      "id": "df-writes/delta/1024_insert_cross_join_typed",
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    {
      "id": "df-writes/delta/1025_insert_large_typed",
      "num": 1025,
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      "description": "INSERT 5000 rows with multiple typed columns.",
      "status": "pass",
      "duration_ms": 4713,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1026_insert_decimal_cdc",
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      "reader": "spark",
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      "description": "INSERT DECIMAL values with CDC enabled.",
      "status": "pass",
      "duration_ms": 5283,
      "error": null,
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      "verified_at": "2026-05-19T17:00:34.657117+00:00",
      "read_cold_ms": 2977,
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      "tags": [
        "type:decimal",
        "type:integer",
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        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1027_insert_timestamp_cdc",
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      "read_script": "generator/spark-reads-df/verify_1027_insert_timestamp_cdc.py",
      "description": "INSERT TIMESTAMP values with CDC enabled.",
      "status": "pass",
      "duration_ms": 4687,
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      "read_cold_ms": 2566,
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        "type:integer",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1028_insert_typed_partition",
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      "status": "pass",
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    },
    {
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      "status": "pass",
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    },
    {
      "id": "df-writes/delta/102_partition_null_value_serialization",
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      "description": "NULL partition value serialization and handling.",
      "status": "pass",
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        "type:boolean",
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      "id": "df-writes/delta/1030_insert_constraint_decimal",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1031_insert_constraint_int",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1032_insert_colmap_typed",
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      "description": "INSERT, then ALTER ADD COLUMN DECIMAL(10,2), then INSERT more with the new column.",
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      "read_cold_ms": 2992,
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      "id": "df-writes/delta/1035_insert_evolve_boolean",
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    {
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      "read_cold_ms": 1859,
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      "id": "df-writes/delta/1037_insert_overwrite_decimal",
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      "verified_at": "2026-05-19T17:01:17.833789+00:00",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1038_insert_overwrite_timestamp",
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      "reader": "spark",
      "write_script": "generator/df-sql/1038_insert_overwrite_timestamp.sql",
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      "status": "pass",
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      "verified_at": "2026-05-19T17:01:27.525531+00:00",
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      "status": "pass",
      "duration_ms": 4093,
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      "reader": "spark",
      "write_script": "generator/df-sql/1041_insert_many_batches_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1041_insert_many_batches_timestamp.py",
      "description": "20 INSERT batches with TIMESTAMP values.",
      "status": "pass",
      "duration_ms": 5167,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:01:38.011614+00:00",
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        "scale:large-dataset",
        "scale:many-batches",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1042_insert_partial_columns",
      "num": 1042,
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      "reader": "spark",
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      "description": "INSERT specifying only a subset of columns.",
      "status": "pass",
      "duration_ms": 4695,
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        "type:integer",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1043_insert_int_boundary_values",
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      "status": "pass",
      "duration_ms": 4734,
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      "read_cold_ms": 2998,
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        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1044_insert_bigint_boundary",
      "num": 1044,
      "name": "insert_bigint_boundary",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4342,
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      "read_cold_ms": 2782,
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        "type:integer",
        "type:string",
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        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1045_insert_double_special",
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      "status": "pass",
      "duration_ms": 4143,
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      "read_cold_ms": 2800,
      "read_warm_ms": 700,
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        "type:floating",
        "type:integer",
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        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1046_insert_string_empty_null",
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      "duration_ms": 4880,
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1047_insert_mixed_nulls",
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      "status": "pass",
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        "type:floating",
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        "format:delta"
      ],
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    },
    {
      "id": "df-writes/delta/1048_insert_all_same_values",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1049_insert_monotonic_decimal",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1049_insert_monotonic_decimal.py",
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      "status": "pass",
      "duration_ms": 4626,
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      "verified_at": "2026-05-19T17:02:14.059966+00:00",
      "read_cold_ms": 3024,
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/104_concurrent_writer_conflict_detection",
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      "description": "Schema (24 columns) for global inventory management",
      "status": "pass",
      "duration_ms": 3439,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1050_insert_cdc_multi_batch",
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      "description": "INSERT multiple batches with CDC enabled.",
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        "type:integer",
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        "delta:change-data-feed",
        "delta:deletion-vectors",
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        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1051_insert_int_to_bigint",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1051_insert_int_to_bigint.py",
      "description": "INSERT INT-range values into BIGINT column.",
      "status": "pass",
      "duration_ms": 3887,
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      "verified_at": "2026-05-19T17:02:22.925748+00:00",
      "read_cold_ms": 1977,
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        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1052_insert_cast_expressions",
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      "description": "INSERT with various CAST expressions in SELECT.",
      "status": "pass",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1053_insert_round_expressions",
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      "read_script": "generator/spark-reads-df/verify_1053_insert_round_expressions.py",
      "description": "INSERT with ROUND at various decimal places (0, 1, 2, 4, 8).",
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      "duration_ms": 4089,
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    {
      "id": "df-writes/delta/1054_insert_case_to_typed",
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      "read_script": "generator/spark-reads-df/verify_1054_insert_case_to_typed.py",
      "description": "INSERT with CASE expressions producing typed values (STRING, DECIMAL, INT).",
      "status": "pass",
      "duration_ms": 3714,
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      "verified_at": "2026-05-19T17:02:35.227786+00:00",
      "read_cold_ms": 2051,
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    {
      "id": "df-writes/delta/1055_insert_concat_expressions",
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      "read_script": "generator/spark-reads-df/verify_1055_insert_concat_expressions.py",
      "description": "INSERT with complex CONCAT building typed strings.",
      "status": "pass",
      "duration_ms": 4027,
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      "read_cold_ms": 2526,
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    {
      "id": "df-writes/delta/1056_insert_arithmetic_expressions",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4615,
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      "read_cold_ms": 2739,
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    {
      "id": "df-writes/delta/1057_insert_boolean_expressions",
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      "duration_ms": 4441,
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      "read_cold_ms": 2290,
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    {
      "id": "df-writes/delta/1058_insert_timestamp_expressions",
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      "read_script": "generator/spark-reads-df/verify_1058_insert_timestamp_expressions.py",
      "description": "INSERT with computed TIMESTAMP expressions at different intervals.",
      "status": "pass",
      "duration_ms": 4345,
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      "verified_at": "2026-05-19T17:02:52.665484+00:00",
      "read_cold_ms": 2642,
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    {
      "id": "df-writes/delta/1059_insert_decimal_from_int",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1059_insert_decimal_from_int.py",
      "description": "INSERT DECIMAL columns computed from INT expressions.",
      "status": "pass",
      "duration_ms": 4572,
      "error": null,
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      "verified_at": "2026-05-19T17:02:57.241951+00:00",
      "read_cold_ms": 2676,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/105_log_file_corruption_recovery",
      "num": 105,
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      "type": "df-writes",
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      "reader": "spark",
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      "notes": null,
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        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "robust:log-corruption",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "name": "insert_struct_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1060_insert_struct_cdc.py",
      "description": "INSERT STRUCT with CDC enabled.",
      "status": "pass",
      "duration_ms": 5092,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:02.336525+00:00",
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      "skip_cause": null,
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        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1061_insert_struct_partition",
      "num": 1061,
      "name": "insert_struct_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT STRUCT into partitioned table.",
      "status": "pass",
      "duration_ms": 4413,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:06.751017+00:00",
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      "read_warm_ms": 779,
      "skip_feature": null,
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      "write_cold_ms": 81,
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      "tags": [
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        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1062_insert_struct_colmap",
      "num": 1062,
      "name": "insert_struct_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1062_insert_struct_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1062_insert_struct_colmap.py",
      "description": "INSERT STRUCT with column mapping mode=name.",
      "status": "pass",
      "duration_ms": 4588,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:11.339681+00:00",
      "read_cold_ms": 2842,
      "read_warm_ms": 888,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
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        "type:string",
        "type:struct",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1063_insert_decimal_colmap_cdc",
      "num": 1063,
      "name": "insert_decimal_colmap_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1063_insert_decimal_colmap_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1063_insert_decimal_colmap_cdc.py",
      "description": "INSERT DECIMAL with column mapping + CDC. Three-way feature combo.",
      "status": "pass",
      "duration_ms": 4765,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:16.106125+00:00",
      "read_cold_ms": 2894,
      "read_warm_ms": 668,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1064_insert_typed_partition_cdc",
      "num": 1064,
      "name": "insert_typed_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1064_insert_typed_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1064_insert_typed_partition_cdc.py",
      "description": "INSERT typed columns + partition + CDC. Three-way feature combo.",
      "status": "pass",
      "duration_ms": 5286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:21.393096+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 50,
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        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1065_insert_constraint_cdc",
      "num": 1065,
      "name": "insert_constraint_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1065_insert_constraint_cdc.py",
      "description": "INSERT with CHECK constraint + CDC. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 5078,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:26.472003+00:00",
      "read_cold_ms": 2807,
      "read_warm_ms": 680,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1066_insert_evolve_cdc",
      "num": 1066,
      "name": "insert_evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1066_insert_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1066_insert_evolve_cdc.py",
      "description": "INSERT + schema evolution + CDC. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 4124,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:30.597799+00:00",
      "read_cold_ms": 2528,
      "read_warm_ms": 561,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1067_insert_colmap_evolve",
      "num": 1067,
      "name": "insert_colmap_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1067_insert_colmap_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1067_insert_colmap_evolve.py",
      "description": "INSERT + column mapping + schema evolution. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 4401,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:34.999427+00:00",
      "read_cold_ms": 2710,
      "read_warm_ms": 857,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1068_insert_partition_evolve",
      "num": 1068,
      "name": "insert_partition_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1068_insert_partition_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1068_insert_partition_evolve.py",
      "description": "INSERT + partition + schema evolution. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 4452,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:39.452208+00:00",
      "read_cold_ms": 2839,
      "read_warm_ms": 941,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 186,
      "write_warm_ms": 133,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1069_insert_constraint_partition",
      "num": 1069,
      "name": "insert_constraint_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1069_insert_constraint_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1069_insert_constraint_partition.py",
      "description": "INSERT + CHECK constraint + partition. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 4718,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:44.170786+00:00",
      "read_cold_ms": 2819,
      "read_warm_ms": 815,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/106_parquet_rowgroup_vs_delta_stats",
      "num": 106,
      "name": "parquet_rowgroup_vs_delta_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/106_parquet_rowgroup_vs_delta_stats.sql",
      "read_script": "generator/spark-reads-df/verify_106_parquet_rowgroup_vs_delta_stats.py",
      "description": "Schema (26 columns) for scientific research data 100,000 initial rows + 5,000 high-temperature records",
      "status": "pass",
      "duration_ms": 7597,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:37:54.825217+00:00",
      "read_cold_ms": 2129,
      "read_warm_ms": 885,
      "skip_feature": null,
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      "write_cold_ms": 751,
      "write_warm_ms": 760,
      "tags": [
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        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "storage:rowgroup-stats",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 1070,
      "name": "insert_colmap_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1070_insert_colmap_partition.py",
      "description": "INSERT + column mapping + partition. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 4369,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:48.541133+00:00",
      "read_cold_ms": 2727,
      "read_warm_ms": 817,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 50,
      "tags": [
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1071_insert_optimize_cdc",
      "num": 1071,
      "name": "insert_optimize_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1071_insert_optimize_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1071_insert_optimize_cdc.py",
      "description": "INSERT batches + OPTIMIZE + CDC. Three-way feature combo.",
      "status": "pass",
      "duration_ms": 5302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:53.844870+00:00",
      "read_cold_ms": 3076,
      "read_warm_ms": 820,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 126,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1072_insert_overwrite_cdc",
      "num": 1072,
      "name": "insert_overwrite_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1072_insert_overwrite_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1072_insert_overwrite_cdc.py",
      "description": "INSERT OVERWRITE + CDC. Two-way feature combo.",
      "status": "pass",
      "duration_ms": 5043,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:58.888948+00:00",
      "read_cold_ms": 2781,
      "read_warm_ms": 869,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1073_insert_values_typed",
      "num": 1073,
      "name": "insert_values_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1073_insert_values_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1073_insert_values_typed.py",
      "description": "INSERT using VALUES clause with all types in single rows.",
      "status": "pass",
      "duration_ms": 4578,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:03.468755+00:00",
      "read_cold_ms": 3019,
      "read_warm_ms": 730,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 15,
      "write_warm_ms": 52,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1074_insert_values_boundary",
      "num": 1074,
      "name": "insert_values_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1074_insert_values_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_1074_insert_values_boundary.py",
      "description": "INSERT using VALUES with boundary values for each type.",
      "status": "pass",
      "duration_ms": 4326,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:07.796175+00:00",
      "read_cold_ms": 3050,
      "read_warm_ms": 643,
      "skip_feature": null,
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      "write_cold_ms": 15,
      "write_warm_ms": 20,
      "tags": [
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        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1075_insert_ten_batches_typed",
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      "name": "insert_ten_batches_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1075_insert_ten_batches_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1075_insert_ten_batches_typed.py",
      "description": "10 INSERT batches each with all 6 types.",
      "status": "pass",
      "duration_ms": 5782,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:13.579773+00:00",
      "read_cold_ms": 3815,
      "read_warm_ms": 719,
      "skip_feature": null,
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      "write_cold_ms": 233,
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      "tags": [
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        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
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        "scale:many-batches",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1076_insert_colmap_cdc_partition",
      "num": 1076,
      "name": "insert_colmap_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1076_insert_colmap_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1076_insert_colmap_cdc_partition.py",
      "description": "3-way combo: column mapping (name) + CDC + partitioning.",
      "status": "pass",
      "duration_ms": 4791,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:18.377731+00:00",
      "read_cold_ms": 2813,
      "read_warm_ms": 564,
      "skip_feature": null,
      "skip_cause": null,
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      ],
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    },
    {
      "id": "df-writes/delta/1093_insert_struct_cdc_partition",
      "num": 1093,
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      "description": "3-way combo: STRUCT type + CDC + partitioning.",
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      "duration_ms": 5513,
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        "delta:deletion-vectors",
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      ],
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    },
    {
      "id": "df-writes/delta/1094_insert_decimal_constraint_partition",
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      "duration_ms": 4766,
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      ],
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    },
    {
      "id": "df-writes/delta/1095_insert_timestamp_colmap_partition",
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    },
    {
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    },
    {
      "id": "df-writes/delta/1097_insert_decimal_negative_cdc",
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      "status": "pass",
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    {
      "id": "df-writes/delta/1098_insert_typed_constraint_colmap",
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      "id": "df-writes/delta/1099_insert_all_features",
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    {
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      "status": "pass",
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    {
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      "description": "The Rust generator does 30 individual UPDATE operations. This SQL generator pre-computes the FINAL state after all operations in a single INSERT.",
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    {
      "id": "df-writes/delta/1100_insert_ultimate",
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    {
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      "duration_ms": 5735,
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      "duration_ms": 6806,
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    {
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      "duration_ms": 6509,
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    {
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    {
      "id": "df-writes/delta/1109_delete_where_decimal_negative",
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      "description": "DELETE on negative DECIMAL values.",
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      "id": "df-writes/delta/1137_delete_expression_modular",
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    {
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      "id": "df-writes/delta/1143_delete_large_typed",
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      "description": "DELETE 90% of large typed table. Scale + types.",
      "status": "pass",
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      "error": null,
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        "type:decimal",
        "type:floating",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1144_delete_small_typed",
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      "reader": "spark",
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      "description": "DELETE 1 row from typed table. Tests minimum DV creation.",
      "status": "pass",
      "duration_ms": 5877,
      "error": null,
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      "read_cold_ms": 3407,
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        "type:integer",
        "type:timestamp",
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1145_delete_typed_then_insert",
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      "reader": "spark",
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      "description": "DELETE typed rows then INSERT new typed rows.",
      "status": "pass",
      "duration_ms": 6079,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 3776,
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        "type:decimal",
        "type:floating",
        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1146_delete_typed_where_cast",
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      "reader": "spark",
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      "description": "DELETE WHERE with explicit CAST in predicate.",
      "status": "pass",
      "duration_ms": 6273,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 3374,
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        "type:integer",
        "type:string",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1147_delete_decimal_max_precision",
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      "description": "DELETE on DECIMAL(38,18) predicate. Tests max precision in WHERE.",
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      "duration_ms": 5884,
      "error": null,
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      "read_cold_ms": 3118,
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      "write_warm_ms": 31,
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        "type:boundary",
        "type:decimal",
        "type:integer",
        "type:string",
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        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1148_delete_timestamp_microsecond",
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      "reader": "spark",
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      "description": "DELETE with microsecond-precision TIMESTAMP predicate.",
      "status": "pass",
      "duration_ms": 6044,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:01.894568+00:00",
      "read_cold_ms": 3397,
      "read_warm_ms": 1000,
      "skip_feature": null,
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        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
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        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1149_delete_int_boundary",
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      "name": "delete_int_boundary",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1149_delete_int_boundary.py",
      "description": "DELETE at INT boundary values.",
      "status": "pass",
      "duration_ms": 5901,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:07.798049+00:00",
      "read_cold_ms": 2926,
      "read_warm_ms": 1334,
      "skip_feature": null,
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        "type:boundary",
        "type:integer",
        "type:string",
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        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/114_deletion_vector_inline_bitmap",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_114_deletion_vector_inline_bitmap.py",
      "description": "Schema (19 columns) for real-time data corrections",
      "status": "pass",
      "duration_ms": 5640,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:38:40.318706+00:00",
      "read_cold_ms": 3157,
      "read_warm_ms": 988,
      "skip_feature": null,
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        "type:boolean",
        "type:decimal",
        "type:integer",
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        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1150_delete_double_extreme",
      "num": 1150,
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "DELETE on DOUBLE extreme values.",
      "status": "pass",
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      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:13.567636+00:00",
      "read_cold_ms": 3212,
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      "skip_feature": null,
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        "type:floating",
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        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1151_delete_decimal_cdc",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1151_delete_decimal_cdc.py",
      "description": "DELETE on DECIMAL predicate with CDC enabled.",
      "status": "pass",
      "duration_ms": 6460,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:20.028668+00:00",
      "read_cold_ms": 3300,
      "read_warm_ms": 1339,
      "skip_feature": null,
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      "write_cold_ms": 21,
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        "type:integer",
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        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1152_delete_timestamp_cdc",
      "num": 1152,
      "name": "delete_timestamp_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1152_delete_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1152_delete_timestamp_cdc.py",
      "description": "DELETE on TIMESTAMP predicate with CDC enabled.",
      "status": "pass",
      "duration_ms": 5730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:25.760130+00:00",
      "read_cold_ms": 2940,
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      "skip_feature": null,
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      "write_cold_ms": 21,
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        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1153_delete_boolean_cdc",
      "num": 1153,
      "name": "delete_boolean_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1153_delete_boolean_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1153_delete_boolean_cdc.py",
      "description": "DELETE on BOOLEAN predicate with CDC enabled.",
      "status": "pass",
      "duration_ms": 6100,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:31.861439+00:00",
      "read_cold_ms": 3603,
      "read_warm_ms": 1326,
      "skip_feature": null,
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      "write_cold_ms": 19,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1154_delete_int_cdc",
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      "name": "delete_int_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1154_delete_int_cdc.py",
      "description": "DELETE on INT predicate with CDC enabled.",
      "status": "pass",
      "duration_ms": 6224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:38.088310+00:00",
      "read_cold_ms": 3515,
      "read_warm_ms": 1362,
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      "write_cold_ms": 20,
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        "dml:cdc-write",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1155_delete_decimal_partition",
      "num": 1155,
      "name": "delete_decimal_partition",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1155_delete_decimal_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1155_delete_decimal_partition.py",
      "description": "DELETE on DECIMAL predicate within a specific partition.",
      "status": "pass",
      "duration_ms": 5485,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:43.575299+00:00",
      "read_cold_ms": 3475,
      "read_warm_ms": 1109,
      "skip_feature": null,
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        "type:string",
        "dml:delete",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1156_delete_timestamp_partition",
      "num": 1156,
      "name": "delete_timestamp_partition",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1156_delete_timestamp_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1156_delete_timestamp_partition.py",
      "description": "DELETE on TIMESTAMP predicate within a specific partition.",
      "status": "pass",
      "duration_ms": 5748,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:49.325708+00:00",
      "read_cold_ms": 3725,
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      "write_cold_ms": 78,
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        "dml:delete",
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        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1157_delete_typed_partition_all",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1157_delete_typed_partition_all.sql",
      "read_script": "generator/spark-reads-df/verify_1157_delete_typed_partition_all.py",
      "description": "DELETE with different typed predicates per partition.",
      "status": "pass",
      "duration_ms": 6023,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:11:55.351360+00:00",
      "read_cold_ms": 3426,
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        "delta:partitioning",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1158_delete_decimal_constraint",
      "num": 1158,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1158_delete_decimal_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1158_delete_decimal_constraint.py",
      "description": "DELETE on DECIMAL predicate with CHECK constraint.",
      "status": "pass",
      "duration_ms": 5744,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:12:01.096918+00:00",
      "read_cold_ms": 3063,
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        "dml:delete",
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        "direction:df-writes",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1159_delete_int_constraint",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1159_delete_int_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1159_delete_int_constraint.py",
      "description": "DELETE on INT predicate with CHECK constraint.",
      "status": "pass",
      "duration_ms": 5686,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:06.784410+00:00",
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        "dml:delete",
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        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/115_checkpoint_v1_vs_v2_migration",
      "num": 115,
      "name": "checkpoint_v1_vs_v2_migration",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/115_checkpoint_v1_vs_v2_migration.sql",
      "read_script": "generator/spark-reads-df/verify_115_checkpoint_v1_vs_v2_migration.py",
      "description": "Validates a checkpoint format migration table with V1->V2 progression. INSERT 2000, 15 UPDATEs, INSERT 500, 10 category UPDATEs, INSERT 1000, DELETE archived v1, 5 priority UPDATEs, OPTIMIZE.",
      "status": "pass",
      "duration_ms": 2782,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:38:43.101368+00:00",
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      "tags": [
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:checkpoint-v1",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1160_delete_decimal_colmap",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1160_delete_decimal_colmap.py",
      "description": "DELETE on DECIMAL predicate with column mapping mode=name.",
      "status": "pass",
      "duration_ms": 5704,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:12.490576+00:00",
      "read_cold_ms": 3366,
      "read_warm_ms": 1055,
      "skip_feature": null,
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      "write_cold_ms": 26,
      "write_warm_ms": 24,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1161_delete_timestamp_colmap",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1161_delete_timestamp_colmap.py",
      "description": "DELETE on TIMESTAMP predicate with column mapping mode=name.",
      "status": "pass",
      "duration_ms": 5569,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:18.060973+00:00",
      "read_cold_ms": 3282,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1162_delete_typed_evolve",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1162_delete_typed_evolve.py",
      "description": "DELETE after schema evolution on typed table.",
      "status": "pass",
      "duration_ms": 5627,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:23.689378+00:00",
      "read_cold_ms": 3541,
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      "write_cold_ms": 94,
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        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1163_delete_decimal_optimize.py",
      "description": "DELETE on DECIMAL predicate after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 6895,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:30.585191+00:00",
      "read_cold_ms": 3555,
      "read_warm_ms": 1079,
      "skip_feature": null,
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      "write_cold_ms": 128,
      "write_warm_ms": 141,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1164_delete_timestamp_optimize",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1164_delete_timestamp_optimize.py",
      "description": "DELETE on TIMESTAMP predicate after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 6795,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:37.381910+00:00",
      "read_cold_ms": 3931,
      "read_warm_ms": 1150,
      "skip_feature": null,
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      "write_cold_ms": 109,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
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        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1165_delete_typed_no_dv",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1165_delete_typed_no_dv.py",
      "description": "DELETE on typed table without deletion vectors (full rewrite path).",
      "status": "pass",
      "duration_ms": 3771,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:12:41.154195+00:00",
      "read_cold_ms": 2488,
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        "type:boolean",
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        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1166_delete_typed_not_null",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1166_delete_typed_not_null.py",
      "description": "DELETE on NOT NULL typed table.",
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      "duration_ms": 5537,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 3146,
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        "type:integer",
        "type:string",
        "dml:delete",
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        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1167_delete_decimal_cdc_partition",
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      "name": "delete_decimal_cdc_partition",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1167_delete_decimal_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1167_delete_decimal_cdc_partition.py",
      "description": "DELETE DECIMAL + CDC + partition (three-way combo).",
      "status": "pass",
      "duration_ms": 5776,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:12:52.469198+00:00",
      "read_cold_ms": 3151,
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      "write_cold_ms": 176,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1168_delete_typed_colmap_cdc",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1168_delete_typed_colmap_cdc.py",
      "description": "DELETE typed + colmap + CDC (three-way combo).",
      "status": "pass",
      "duration_ms": 6174,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 3247,
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        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
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        "delta:change-data-feed",
        "delta:column-mapping",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1169_delete_typed_constraint_evolve",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1169_delete_typed_constraint_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1169_delete_typed_constraint_evolve.py",
      "description": "DELETE typed + constraint + schema evolution (three-way combo).",
      "status": "pass",
      "duration_ms": 5988,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:13:04.636106+00:00",
      "read_cold_ms": 3311,
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        "dml:delete",
        "dml:insert",
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        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/116_unicode_column_names",
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      "reader": "spark",
      "write_script": "generator/df-sql/116_unicode_column_names.sql",
      "read_script": "generator/spark-reads-df/verify_116_unicode_column_names.py",
      "description": "Demonstrates Unicode and special characters in column names: - CJK characters (Japanese, Chinese, Korean) - Cyrillic (Russian) - Greek - Arabic - Emoji - Special characters (spaces, dots, brackets) - Mixed scripts",
      "status": "pass",
      "duration_ms": 2452,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 1260,
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      "tags": [
        "type:boolean",
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        "type:decimal",
        "type:floating",
        "type:integer",
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        "type:timestamp",
        "type:unicode",
        "dml:insert",
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        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1170_delete_decimal_colmap_partition",
      "num": 1170,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1170_delete_decimal_colmap_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1170_delete_decimal_colmap_partition.py",
      "description": "DELETE DECIMAL + colmap + partition (three-way combo).",
      "status": "pass",
      "duration_ms": 5785,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 2868,
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      "write_cold_ms": 161,
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      "tags": [
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1171_delete_typed_optimize_cdc",
      "num": 1171,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1171_delete_typed_optimize_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1171_delete_typed_optimize_cdc.py",
      "description": "DELETE typed + OPTIMIZE + CDC (three-way combo).",
      "status": "pass",
      "duration_ms": 6482,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 3091,
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        "dml:cdc-write",
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        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1172_delete_typed_partition_evolve",
      "num": 1172,
      "name": "delete_typed_partition_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1172_delete_typed_partition_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1172_delete_typed_partition_evolve.py",
      "description": "DELETE typed + partition + schema evolution.",
      "status": "pass",
      "duration_ms": 6558,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:13:23.471352+00:00",
      "read_cold_ms": 3489,
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      "skip_feature": null,
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      "write_cold_ms": 137,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1173_delete_struct_typed",
      "num": 1173,
      "name": "delete_struct_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1173_delete_struct_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1173_delete_struct_typed.py",
      "description": "DELETE on table with STRUCT column + typed DECIMAL predicate.",
      "status": "pass",
      "duration_ms": 5400,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:13:28.874139+00:00",
      "read_cold_ms": 3150,
      "read_warm_ms": 1127,
      "skip_feature": null,
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      "write_cold_ms": 20,
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      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1174_delete_four_way",
      "num": 1174,
      "name": "delete_four_way",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1174_delete_four_way.sql",
      "read_script": "generator/spark-reads-df/verify_1174_delete_four_way.py",
      "description": "DELETE typed + CDC + partition + constraint (four-way combo).",
      "status": "pass",
      "duration_ms": 5976,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 3347,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1175_delete_five_way",
      "num": 1175,
      "name": "delete_five_way",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1175_delete_five_way.sql",
      "read_script": "generator/spark-reads-df/verify_1175_delete_five_way.py",
      "description": "DELETE typed + CDC + colmap + partition + constraint (five-way combo).",
      "status": "pass",
      "duration_ms": 6001,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:13:40.856395+00:00",
      "read_cold_ms": 3463,
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      "skip_feature": null,
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      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1176_delete_then_insert_typed",
      "num": 1176,
      "name": "delete_then_insert_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1176_delete_then_insert_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1176_delete_then_insert_typed.py",
      "description": "DELETE typed rows then INSERT new typed rows.",
      "status": "pass",
      "duration_ms": 5641,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:13:46.499478+00:00",
      "read_cold_ms": 3350,
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      "tags": [
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1177_delete_then_update_typed",
      "num": 1177,
      "name": "delete_then_update_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1177_delete_then_update_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1177_delete_then_update_typed.py",
      "description": "DELETE then UPDATE on surviving typed rows.",
      "status": "pass",
      "duration_ms": 6486,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:13:52.986589+00:00",
      "read_cold_ms": 3425,
      "read_warm_ms": 1219,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 66,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1178_delete_then_merge_typed",
      "num": 1178,
      "name": "delete_then_merge_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1178_delete_then_merge_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1178_delete_then_merge_typed.py",
      "description": "DELETE then MERGE on typed table.",
      "status": "pass",
      "duration_ms": 5902,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:13:58.890984+00:00",
      "read_cold_ms": 3293,
      "read_warm_ms": 1308,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 78,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1179_delete_chain_three",
      "num": 1179,
      "name": "delete_chain_three",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1179_delete_chain_three.sql",
      "read_script": "generator/spark-reads-df/verify_1179_delete_chain_three.py",
      "description": "Three sequential typed DELETEs narrowing by different types each time.",
      "status": "pass",
      "duration_ms": 5983,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:04.877148+00:00",
      "read_cold_ms": 3356,
      "read_warm_ms": 1288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 54,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/117_deeply_nested_100_levels",
      "num": 117,
      "name": "deeply_nested_100_levels",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/117_deeply_nested_100_levels.sql",
      "read_script": "generator/spark-reads-df/verify_117_deeply_nested_100_levels.py",
      "description": "Demonstrates nested schema structures with 10 levels of depth: - Structs nested 10 levels deep (organizational hierarchy) - Arrays of nested structs (team members with skills) - Maps with nested value types (department budgets) - Mixed nesting patterns (project assignments)",
      "status": "pass",
      "duration_ms": 6829,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:38:52.385582+00:00",
      "read_cold_ms": 1500,
      "read_warm_ms": 343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 566,
      "write_warm_ms": 505,
      "tags": [
        "type:array",
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1180_delete_chain_same_decimal",
      "num": 1180,
      "name": "delete_chain_same_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1180_delete_chain_same_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1180_delete_chain_same_decimal.py",
      "description": "Three DELETEs all on DECIMAL column at different thresholds.",
      "status": "pass",
      "duration_ms": 6081,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:10.959841+00:00",
      "read_cold_ms": 3339,
      "read_warm_ms": 1302,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 89,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1181_delete_multi_type_schema",
      "num": 1181,
      "name": "delete_multi_type_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1181_delete_multi_type_schema.sql",
      "read_script": "generator/spark-reads-df/verify_1181_delete_multi_type_schema.py",
      "description": "DELETE on 8-column typed table.",
      "status": "pass",
      "duration_ms": 6299,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:17.259987+00:00",
      "read_cold_ms": 3674,
      "read_warm_ms": 1136,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 26,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1182_delete_decimal_then_decimal",
      "num": 1182,
      "name": "delete_decimal_then_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1182_delete_decimal_then_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1182_delete_decimal_then_decimal.py",
      "description": "Two DELETEs both on DECIMAL but different columns.",
      "status": "pass",
      "duration_ms": 6417,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:23.682228+00:00",
      "read_cold_ms": 3288,
      "read_warm_ms": 1622,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 25,
      "write_warm_ms": 71,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1183_delete_timestamp_then_boolean",
      "num": 1183,
      "name": "delete_timestamp_then_boolean",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1183_delete_timestamp_then_boolean.sql",
      "read_script": "generator/spark-reads-df/verify_1183_delete_timestamp_then_boolean.py",
      "description": "DELETE on TIMESTAMP then DELETE on BOOLEAN.",
      "status": "pass",
      "duration_ms": 6356,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:30.040935+00:00",
      "read_cold_ms": 3582,
      "read_warm_ms": 1305,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 25,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1184_delete_typed_all_match",
      "num": 1184,
      "name": "delete_typed_all_match",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1184_delete_typed_all_match.sql",
      "read_script": "generator/spark-reads-df/verify_1184_delete_typed_all_match.py",
      "description": "DELETE WHERE typed predicate matches ALL rows.",
      "status": "pass",
      "duration_ms": 5908,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:35.950208+00:00",
      "read_cold_ms": 3362,
      "read_warm_ms": 1363,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 18,
      "write_warm_ms": 52,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1185_delete_typed_no_match",
      "num": 1185,
      "name": "delete_typed_no_match",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1185_delete_typed_no_match.sql",
      "read_script": "generator/spark-reads-df/verify_1185_delete_typed_no_match.py",
      "description": "DELETE WHERE typed predicate matches NO rows.",
      "status": "pass",
      "duration_ms": 4433,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:40.386911+00:00",
      "read_cold_ms": 2794,
      "read_warm_ms": 777,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 49,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1186_delete_decimal_exact_value",
      "num": 1186,
      "name": "delete_decimal_exact_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1186_delete_decimal_exact_value.sql",
      "read_script": "generator/spark-reads-df/verify_1186_delete_decimal_exact_value.py",
      "description": "DELETE WHERE decimal = exact value.",
      "status": "pass",
      "duration_ms": 6314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:46.703732+00:00",
      "read_cold_ms": 3482,
      "read_warm_ms": 1353,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 20,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1187_delete_timestamp_exact",
      "num": 1187,
      "name": "delete_timestamp_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1187_delete_timestamp_exact.sql",
      "read_script": "generator/spark-reads-df/verify_1187_delete_timestamp_exact.py",
      "description": "DELETE WHERE timestamp = exact value.",
      "status": "pass",
      "duration_ms": 6172,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:52.878795+00:00",
      "read_cold_ms": 3380,
      "read_warm_ms": 1469,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 20,
      "write_warm_ms": 23,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1188_delete_boolean_only_true",
      "num": 1188,
      "name": "delete_boolean_only_true",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1188_delete_boolean_only_true.sql",
      "read_script": "generator/spark-reads-df/verify_1188_delete_boolean_only_true.py",
      "description": "Table with ALL true values, DELETE WHERE flag = true.",
      "status": "pass",
      "duration_ms": 5690,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:14:58.570355+00:00",
      "read_cold_ms": 3401,
      "read_warm_ms": 1267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 18,
      "write_warm_ms": 27,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1189_delete_string_pattern",
      "num": 1189,
      "name": "delete_string_pattern",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1189_delete_string_pattern.sql",
      "read_script": "generator/spark-reads-df/verify_1189_delete_string_pattern.py",
      "description": "DELETE WHERE string matches specific values via IN list.",
      "status": "pass",
      "duration_ms": 5982,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:15:04.553465+00:00",
      "read_cold_ms": 3298,
      "read_warm_ms": 1218,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 21,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/118_thousands_of_partitions",
      "num": 118,
      "name": "thousands_of_partitions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/118_thousands_of_partitions.sql",
      "read_script": "generator/spark-reads-df/verify_118_thousands_of_partitions.py",
      "description": "Schema (27 columns) with 4-column partitioning",
      "status": "pass",
      "duration_ms": 3130,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:38:55.516178+00:00",
      "read_cold_ms": 1955,
      "read_warm_ms": 539,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 288,
      "write_warm_ms": 311,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1190_delete_decimal_cdc_exact",
      "num": 1190,
      "name": "delete_decimal_cdc_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1190_delete_decimal_cdc_exact.sql",
      "read_script": "generator/spark-reads-df/verify_1190_delete_decimal_cdc_exact.py",
      "description": "DELETE DECIMAL + CDC with exact CDF counts.",
      "status": "pass",
      "duration_ms": 6296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:15:10.851442+00:00",
      "read_cold_ms": 3420,
      "read_warm_ms": 1163,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 21,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1191_delete_typed_cdc_partition_constraint",
      "num": 1191,
      "name": "delete_typed_cdc_partition_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1191_delete_typed_cdc_partition_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1191_delete_typed_cdc_partition_constraint.py",
      "description": "DELETE typed + CDC + partition + constraint (four-way).",
      "status": "pass",
      "duration_ms": 6213,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:15:17.065965+00:00",
      "read_cold_ms": 3188,
      "read_warm_ms": 1312,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 177,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1192_delete_typed_colmap_cdc_partition",
      "num": 1192,
      "name": "delete_typed_colmap_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1192_delete_typed_colmap_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1192_delete_typed_colmap_cdc_partition.py",
      "description": "DELETE typed + colmap + CDC + partition (four-way).",
      "status": "pass",
      "duration_ms": 5371,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:15:22.439269+00:00",
      "read_cold_ms": 3105,
      "read_warm_ms": 894,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 93,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1193_delete_typed_optimize_partition_cdc",
      "num": 1193,
      "name": "delete_typed_optimize_partition_cdc",
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      "reader": "spark",
      "write_script": "generator/df-sql/1193_delete_typed_optimize_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1193_delete_typed_optimize_partition_cdc.py",
      "description": "DELETE typed + OPTIMIZE + partition + CDC (four-way).",
      "status": "pass",
      "duration_ms": 6573,
      "error": null,
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      "verified_at": "2026-05-19T17:15:29.014773+00:00",
      "read_cold_ms": 3488,
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      "write_cold_ms": 249,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1194_delete_typed_evolve_cdc_partition",
      "num": 1194,
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      "reader": "spark",
      "write_script": "generator/df-sql/1194_delete_typed_evolve_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1194_delete_typed_evolve_cdc_partition.py",
      "description": "DELETE typed + evolve + CDC + partition (four-way).",
      "status": "pass",
      "duration_ms": 6387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:15:35.403318+00:00",
      "read_cold_ms": 3940,
      "read_warm_ms": 1085,
      "skip_feature": null,
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      "write_cold_ms": 119,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1195_delete_typed_five_way",
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      "reader": "spark",
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      "description": "DELETE typed + CDC + colmap + partition + constraint + evolve (five-way).",
      "status": "pass",
      "duration_ms": 6230,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:15:41.634633+00:00",
      "read_cold_ms": 3458,
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        "type:decimal",
        "type:integer",
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        "dml:cdc-write",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1196_delete_decimal_partition_multi_pred",
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      "name": "delete_decimal_partition_multi_pred",
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      "reader": "spark",
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      "description": "Multiple DECIMAL-predicated DELETEs on partitioned table.",
      "status": "pass",
      "duration_ms": 6522,
      "error": null,
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      "read_cold_ms": 3573,
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      "write_cold_ms": 50,
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        "type:decimal",
        "type:integer",
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        "dml:delete",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1197_delete_typed_colmap_evolve",
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      "writer": "delta-forge",
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      "status": "pass",
      "duration_ms": 7164,
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      "verified_at": "2026-05-19T17:15:55.326283+00:00",
      "read_cold_ms": 3715,
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        "type:decimal",
        "type:integer",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1198_delete_all_types_cdc",
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      "reader": "spark",
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      "duration_ms": 7429,
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      "read_cold_ms": 3808,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1199_delete_typed_colmap_partition_constraint_cdc",
      "num": 1199,
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      "reader": "spark",
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      "description": "DELETE + 5 features (CDC + colmap + partition + constraint + typed).",
      "status": "pass",
      "duration_ms": 6199,
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      "read_cold_ms": 3089,
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        "delta:deletion-vectors",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/119_wide_table_hundreds_columns",
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      "description": "Validates a wide analytics table with 255 columns. INSERT 1000, UPDATE record_id<100, INSERT 200, DELETE record_id>1150.",
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      "duration_ms": 3983,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/11_last_checkpoint_json_pointer",
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      "description": "The Rust generator pre-computes the FINAL state after all operations in a single INSERT. This SQL generator replicates the same logic.",
      "status": "pass",
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      "verified_at": "2026-05-20T00:39:02.465374+00:00",
      "read_cold_ms": 1444,
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      "tags": [
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1200_delete_ultimate",
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      "description": "ULTIMATE DELETE test -- every data type + every predicate pattern",
      "status": "pass",
      "duration_ms": 8022,
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      "verified_at": "2026-05-19T17:16:16.982315+00:00",
      "read_cold_ms": 4215,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1201_stats_int_after_insert",
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      "reader": "spark",
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      "description": "INT column statistics correctness after multi-batch INSERT.",
      "status": "pass",
      "duration_ms": 5976,
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      "extras": {},
      "verified_at": "2026-05-19T17:16:22.959982+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1202_stats_decimal_after_insert",
      "num": 1202,
      "name": "stats_decimal_after_insert",
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      "reader": "spark",
      "write_script": "generator/df-sql/1202_stats_decimal_after_insert.sql",
      "read_script": "generator/spark-reads-df/verify_1202_stats_decimal_after_insert.py",
      "description": "DECIMAL(10,2) column statistics correctness after multi-batch INSERT.",
      "status": "pass",
      "duration_ms": 7921,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:16:30.884745+00:00",
      "read_cold_ms": 2843,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1203_stats_timestamp_after_insert",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1203_stats_timestamp_after_insert.py",
      "description": "TIMESTAMP column statistics correctness after multi-batch INSERT.",
      "status": "pass",
      "duration_ms": 7730,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:16:38.619321+00:00",
      "read_cold_ms": 3778,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1204_stats_string_after_insert",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1204_stats_string_after_insert.py",
      "description": "STRING column statistics correctness after multi-batch INSERT.",
      "status": "pass",
      "duration_ms": 5800,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:16:44.424051+00:00",
      "read_cold_ms": 2503,
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      "tags": [
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1205_stats_boolean_after_insert",
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      "name": "stats_boolean_after_insert",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1205_stats_boolean_after_insert.sql",
      "read_script": "generator/spark-reads-df/verify_1205_stats_boolean_after_insert.py",
      "description": "BOOLEAN column statistics correctness after multi-batch INSERT.",
      "status": "pass",
      "duration_ms": 7897,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:16:52.322902+00:00",
      "read_cold_ms": 3123,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1206_stats_null_count_after_insert",
      "num": 1206,
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1206_stats_null_count_after_insert.sql",
      "read_script": "generator/spark-reads-df/verify_1206_stats_null_count_after_insert.py",
      "description": "nullCount statistics correctness after multi-batch INSERT.",
      "status": "pass",
      "duration_ms": 7654,
      "error": null,
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      "verified_at": "2026-05-19T17:16:59.980687+00:00",
      "read_cold_ms": 3358,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1207_stats_after_update",
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      "name": "stats_after_update",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1207_stats_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_1207_stats_after_update.py",
      "description": "INT statistics correctness after UPDATE rewrites files.",
      "status": "pass",
      "duration_ms": 7245,
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      "verified_at": "2026-05-19T17:17:07.230186+00:00",
      "read_cold_ms": 3108,
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      "tags": [
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1208_stats_after_delete",
      "num": 1208,
      "name": "stats_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1208_stats_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1208_stats_after_delete.py",
      "description": "Statistics correctness after DELETE with deletion vectors.",
      "status": "pass",
      "duration_ms": 9632,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:17:16.866010+00:00",
      "read_cold_ms": 3698,
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      "tags": [
        "type:integer",
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        "dml:delete",
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      "manual_tags": [],
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        "type:integer",
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        "dml:delete",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1243_partition_by_int_null",
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      "status": "pass",
      "duration_ms": 5720,
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        "type:integer",
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        "dml:delete",
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        "delta:partitioning",
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    },
    {
      "id": "df-writes/delta/1244_partition_by_int_nmbys",
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      "reader": "spark",
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      "description": "PARTITIONED BY (bucket INT) with MERGE using",
      "status": "pass",
      "duration_ms": 2782,
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      "verified_at": "2026-05-19T17:21:46.124543+00:00",
      "read_cold_ms": 1841,
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        "type:integer",
        "type:string",
        "dml:insert",
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        "delta:partitioning",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1245_partition_by_int_five_way",
      "num": 1245,
      "name": "partition_by_int_five_way",
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      "description": "Five-way feature combination with PARTITIONED BY (bucket INT):",
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        "type:integer",
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      "id": "df-writes/delta/1246_time_travel_insert_versions",
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      "description": "Time travel across multiple INSERT versions.",
      "status": "pass",
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      "extras": {},
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    },
    {
      "id": "df-writes/delta/1247_time_travel_update_versions",
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      "description": "Time travel to read pre-UPDATE and post-UPDATE versions.",
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        "dml:insert",
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        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1248_time_travel_delete_versions",
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      "type": "df-writes",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1248_time_travel_delete_versions.py",
      "description": "Time travel to read pre-DELETE version.",
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        "type:integer",
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        "dml:delete",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1249_time_travel_schema_evolve",
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      "reader": "spark",
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      "description": "Time travel across schema evolution boundary.",
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      "duration_ms": 3339,
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        "delta:time-travel",
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        "direction:df-writes",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/124_dbx_modify_roundtrip",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_124_dbx_modify_roundtrip.py",
      "description": "Download DBX table -> Modify locally with DeltaForge -> Upload back to DBX",
      "status": "pass",
      "duration_ms": 2339,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:40:25.618957+00:00",
      "read_cold_ms": 1391,
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        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
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        "dml:overwrite",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1250_time_travel_merge_versions",
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1250_time_travel_merge_versions.py",
      "description": "Time travel to read pre-MERGE version.",
      "status": "pass",
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      "error": null,
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      "read_cold_ms": 873,
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        "type:integer",
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1251_time_travel_multiple_updates",
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      "reader": "spark",
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      "description": "Multiple sequential UPDATEs creating 4 versions (V0-V3).",
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      "duration_ms": 3998,
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      "extras": {},
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      "read_cold_ms": 797,
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        "type:integer",
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        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1252_time_travel_delete_reinsert",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "DELETE then re-INSERT creating 3 versions.",
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      "duration_ms": 4488,
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    },
    {
      "id": "df-writes/delta/1253_time_travel_typed_values",
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      "read_script": "generator/spark-reads-df/verify_1253_time_travel_typed_values.py",
      "description": "Time travel with DECIMAL and TIMESTAMP typed columns.",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1254_time_travel_partition",
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      "description": "Time travel on a partitioned table with multiple versions.",
      "status": "pass",
      "duration_ms": 3784,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:22:19.705773+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1255_time_travel_cdc",
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      "reader": "spark",
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      "description": "CDC-enabled table with multiple versions and CDF availability.",
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      "error": null,
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      "extras": {},
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      "description": "OPTIMIZE does not change logical data, only file layout.",
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      "duration_ms": 5353,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:22:28.684872+00:00",
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      "id": "df-writes/delta/1257_time_travel_five_versions",
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      "reader": "spark",
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      "description": "5 sequential INSERTs creating V0-V4, each adding 20 rows.",
      "status": "pass",
      "duration_ms": 8943,
      "error": null,
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      "extras": {},
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      "tags": [
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      "manual_tags": [],
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      "id": "df-writes/delta/1258_time_travel_version_zero",
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      "description": "Reading VERSION AS OF 0 after many subsequent mutations.",
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      "error": null,
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      "extras": {},
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1259_time_travel_colmap.py",
      "description": "Column mapping (name mode) with time travel.",
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      "duration_ms": 5699,
      "error": null,
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      "id": "df-writes/delta/125_deltaforge_create_dbx_modify",
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      "description": "DeltaForge creates table locally -> Upload to DBX -> DBX modifies -> Download -> DeltaForge reads",
      "status": "pass",
      "duration_ms": 2970,
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    {
      "id": "df-writes/delta/1295_evolve_tt_with_constraint",
      "num": 1295,
      "name": "evolve_tt_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1295_evolve_tt_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1295_evolve_tt_with_constraint.py",
      "description": "ADD COLUMN + ADD CONSTRAINT with time travel.",
      "status": "pass",
      "duration_ms": 5572,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:29.382745+00:00",
      "read_cold_ms": 2176,
      "read_warm_ms": 714,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
      "write_warm_ms": 153,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1296_nullable_metadata_basic",
      "num": 1296,
      "name": "nullable_metadata_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1296_nullable_metadata_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1296_nullable_metadata_basic.py",
      "description": "Mix of NOT NULL and nullable columns. Verify Spark reads correct nullable flags.",
      "status": "pass",
      "duration_ms": 4412,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:33.796064+00:00",
      "read_cold_ms": 2611,
      "read_warm_ms": 1007,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 52,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1297_nullable_metadata_after_evolve",
      "num": 1297,
      "name": "nullable_metadata_after_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1297_nullable_metadata_after_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1297_nullable_metadata_after_evolve.py",
      "description": "NOT NULL columns + evolved nullable column. Evolved columns default to nullable.",
      "status": "pass",
      "duration_ms": 3366,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:37.163414+00:00",
      "read_cold_ms": 1935,
      "read_warm_ms": 583,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1298_nullable_metadata_all_not_null",
      "num": 1298,
      "name": "nullable_metadata_all_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1298_nullable_metadata_all_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_1298_nullable_metadata_all_not_null.py",
      "description": "All columns NOT NULL. Verify Spark reports correct schema.",
      "status": "pass",
      "duration_ms": 3903,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:41.068757+00:00",
      "read_cold_ms": 2341,
      "read_warm_ms": 678,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 28,
      "write_warm_ms": 29,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1299_nullable_metadata_with_dml",
      "num": 1299,
      "name": "nullable_metadata_with_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1299_nullable_metadata_with_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1299_nullable_metadata_with_dml.py",
      "description": "NOT NULL + nullable mix through UPDATE/DELETE/MERGE.",
      "status": "pass",
      "duration_ms": 4286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:45.356845+00:00",
      "read_cold_ms": 2441,
      "read_warm_ms": 748,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 252,
      "write_warm_ms": 178,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/129_concurrent_modifications",
      "num": 129,
      "name": "concurrent_modifications",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/129_concurrent_modifications.sql",
      "read_script": "generator/spark-reads-df/verify_129_concurrent_modifications.py",
      "description": "Multiple concurrent modifications to a table simulating",
      "status": "pass",
      "duration_ms": 3188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:40:39.379388+00:00",
      "read_cold_ms": 2055,
      "read_warm_ms": 546,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 419,
      "write_warm_ms": 340,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/12_version_checksum_with_histogram",
      "num": 12,
      "name": "version_checksum_with_histogram",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/12_version_checksum_with_histogram.sql",
      "read_script": "generator/spark-reads-df/verify_12_version_checksum_with_histogram.py",
      "description": "Validates the Delta table written by DeltaForge for test 12. Marketing campaign analytics table with 20 columns. Operations: - Op1: UPDATE roi<0 AND status='active' -> status='paused' - Op2: UPDATE roi>200 AND status='active' -> spend_usd*=1.5, performance_tier='excellent",
      "status": "pass",
      "duration_ms": 4157,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:40:43.537664+00:00",
      "read_cold_ms": 2062,
      "read_warm_ms": 466,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 585,
      "write_warm_ms": 807,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1300_nullable_metadata_partition",
      "num": 1300,
      "name": "nullable_metadata_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1300_nullable_metadata_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1300_nullable_metadata_partition.py",
      "description": "NOT NULL partition key. Partitioned table where partition column is NOT NULL.",
      "status": "pass",
      "duration_ms": 3817,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:49.176186+00:00",
      "read_cold_ms": 2093,
      "read_warm_ms": 868,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1301_append_only_basic",
      "num": 1301,
      "name": "append_only_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1301_append_only_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1301_append_only_basic.py",
      "description": "Append-only table (delta.appendOnly=true). Only INSERTs allowed.",
      "status": "pass",
      "duration_ms": 4048,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:53.225731+00:00",
      "read_cold_ms": 1979,
      "read_warm_ms": 689,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 122,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1302_append_only_typed",
      "num": 1302,
      "name": "append_only_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1302_append_only_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1302_append_only_typed.py",
      "description": "Append-only with DECIMAL+TIMESTAMP+BOOLEAN typed columns.",
      "status": "pass",
      "duration_ms": 3329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:25:56.557997+00:00",
      "read_cold_ms": 2014,
      "read_warm_ms": 652,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 113,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1303_append_only_cdc",
      "num": 1303,
      "name": "append_only_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1303_append_only_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1303_append_only_cdc.py",
      "description": "Append-only + CDC enabled. Only inserts, CDF should have 100 insert records.",
      "status": "pass",
      "duration_ms": 3890,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:00.449018+00:00",
      "read_cold_ms": 2136,
      "read_warm_ms": 793,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1304_enable_cdc_at_create",
      "num": 1304,
      "name": "enable_cdc_at_create",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1304_enable_cdc_at_create.sql",
      "read_script": "generator/spark-reads-df/verify_1304_enable_cdc_at_create.py",
      "description": "CDC enabled from table creation with full DML lifecycle.",
      "status": "pass",
      "duration_ms": 5006,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:05.456784+00:00",
      "read_cold_ms": 2631,
      "read_warm_ms": 953,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 352,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1305_enable_dv_at_create",
      "num": 1305,
      "name": "enable_dv_at_create",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1305_enable_dv_at_create.sql",
      "read_script": "generator/spark-reads-df/verify_1305_enable_dv_at_create.py",
      "description": "Deletion vectors enabled from creation with full DML lifecycle.",
      "status": "pass",
      "duration_ms": 4601,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:10.058532+00:00",
      "read_cold_ms": 2633,
      "read_warm_ms": 1111,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 157,
      "write_warm_ms": 192,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1306_colmap_from_start",
      "num": 1306,
      "name": "colmap_from_start",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1306_colmap_from_start.sql",
      "read_script": "generator/spark-reads-df/verify_1306_colmap_from_start.py",
      "description": "Column mapping from table creation with full DML lifecycle + RENAME.",
      "status": "pass",
      "duration_ms": 4854,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:14.913726+00:00",
      "read_cold_ms": 2466,
      "read_warm_ms": 1048,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
      "write_warm_ms": 203,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1307_all_properties_from_start",
      "num": 1307,
      "name": "all_properties_from_start",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1307_all_properties_from_start.sql",
      "read_script": "generator/spark-reads-df/verify_1307_all_properties_from_start.py",
      "description": "All properties enabled at CREATE (DV + CDC + colmap) with full DML.",
      "status": "pass",
      "duration_ms": 5056,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:19.971736+00:00",
      "read_cold_ms": 2770,
      "read_warm_ms": 1122,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 226,
      "write_warm_ms": 252,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1308_minimal_properties",
      "num": 1308,
      "name": "minimal_properties",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1308_minimal_properties.sql",
      "read_script": "generator/spark-reads-df/verify_1308_minimal_properties.py",
      "description": "Table with NO special properties (no DV, no CDC, no colmap).",
      "status": "pass",
      "duration_ms": 2486,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:22.458968+00:00",
      "read_cold_ms": 1739,
      "read_warm_ms": 337,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
      "write_warm_ms": 176,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1309_hundred_files",
      "num": 1309,
      "name": "hundred_files",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1309_hundred_files.sql",
      "read_script": "generator/spark-reads-df/verify_1309_hundred_files.py",
      "description": "100 INSERT batches of 5 rows each (500 total, 100 files).",
      "status": "pass",
      "duration_ms": 5502,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:26:27.962502+00:00",
      "read_cold_ms": 2718,
      "read_warm_ms": 1011,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 10530,
      "write_warm_ms": 9280,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/130_deltaforge_dbx_deltaforge",
      "num": 130,
      "name": "deltaforge_dbx_deltaforge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/130_deltaforge_dbx_deltaforge.sql",
      "read_script": "generator/spark-reads-df/verify_130_deltaforge_dbx_deltaforge.py",
      "description": "Complex multi-operation chaos scenario with sensor readings.",
      "status": "pass",
      "duration_ms": 2730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:40:46.268575+00:00",
      "read_cold_ms": 1635,
      "read_warm_ms": 498,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 242,
      "write_warm_ms": 139,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1310_hundred_files_delete",
      "num": 1310,
      "name": "hundred_files_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1310_hundred_files_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1310_hundred_files_delete.py",
      "description": "50 INSERT batches of 10 rows (500 total, 50 files).",
      "status": "pass",
      "duration_ms": 6052,
      "error": null,
      "notes": null,
      "extras": {},
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      "notes": null,
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        "type:boolean",
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      "concern_note": null
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      "error": null,
      "notes": null,
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      "skip_feature": null,
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        "type:string",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
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      "concern_note": null
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      "error": null,
      "notes": null,
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      "skip_feature": null,
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
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      "error": null,
      "notes": null,
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      "verified_at": "2026-05-19T17:26:52.656499+00:00",
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      "skip_feature": null,
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        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "type": "df-writes",
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      "notes": null,
      "extras": {},
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 4296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:27:03.687898+00:00",
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      "skip_feature": null,
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "delta",
      "writer": "delta-forge",
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      "duration_ms": 6768,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:27:10.457411+00:00",
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      "skip_feature": null,
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "reader": "spark",
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      "extras": {},
      "verified_at": "2026-05-19T17:27:15.007008+00:00",
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      "skip_feature": null,
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
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        "delta:partitioning",
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        "direction:df-writes",
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      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
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      "extras": {},
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        "dml:update",
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        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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      "reader": "spark",
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      "notes": null,
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        "type:string",
        "dml:cdc-write",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1321_stats_after_three_updates.sql",
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      "error": null,
      "notes": null,
      "extras": {},
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      "manual_tags": [],
      "concern_note": null
    },
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1322_pushdown_decimal_four_files.sql",
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      "duration_ms": 4840,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:27:37.309285+00:00",
      "read_cold_ms": 1884,
      "read_warm_ms": 751,
      "skip_feature": null,
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "10 INSERT batches (V0-V9). Read each version cumulatively.",
      "status": "pass",
      "duration_ms": 19193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:27:56.503812+00:00",
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      "skip_feature": null,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 1324,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1324_checkpoint_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1324_checkpoint_after_merge.py",
      "description": "INSERT + 10 MERGEs (V0-V10). Checkpoint at V10.",
      "status": "pass",
      "duration_ms": 5008,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:28:01.513838+00:00",
      "read_cold_ms": 2858,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 802,
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
    },
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      "num": 1325,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1325_stats_partition_typed_key.sql",
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      "description": "INT-partitioned table, 2 batches per partition. Predicate",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1326_pushdown_timestamp_three_files.sql",
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      "duration_ms": 4577,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:28:10.504042+00:00",
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        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1327_time_travel_after_optimize.sql",
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      "status": "pass",
      "duration_ms": 5773,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:28:16.278556+00:00",
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
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        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1328_checkpoint_with_dv",
      "num": 1328,
      "name": "checkpoint_with_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1328_checkpoint_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_1328_checkpoint_with_dv.py",
      "description": "12+ commits including DELETE with deletion vectors. Checkpoint",
      "status": "pass",
      "duration_ms": 5308,
      "error": null,
      "notes": null,
      "extras": {},
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      "skip_feature": null,
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      "tags": [
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        "type:string",
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    },
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    },
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    },
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    },
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      "manual_tags": [],
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    },
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      "manual_tags": [],
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      ],
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      "concern_note": null
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      "description": "CDC + colmap + partition + constraint + evolve. 12+ commits.",
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      "notes": null,
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        "type:integer",
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      "duration_ms": 6640,
      "error": null,
      "notes": null,
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        "type:decimal",
        "type:floating",
        "type:integer",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
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        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
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      "duration_ms": 23553,
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        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
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      "duration_ms": 1789,
      "error": null,
      "notes": null,
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        "type:integer",
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
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        "dml:insert",
        "dml:merge",
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        "delta:change-data-feed",
        "delta:column-mapping",
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        "delta:deletion-vectors",
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        "delta:partitioning",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/1351_zorder_basic",
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      "duration_ms": 3941,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:30:54.504973+00:00",
      "read_cold_ms": 1802,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1352_zorder_decimal",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3207,
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      "notes": null,
      "extras": {},
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      "read_cold_ms": 1970,
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      "skip_feature": null,
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      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1353_zorder_timestamp",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1353_zorder_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1353_zorder_timestamp.py",
      "description": "Z-ORDER on TIMESTAMP column. Tests TIMESTAMP values survive",
      "status": "pass",
      "duration_ms": 3751,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:01.466281+00:00",
      "read_cold_ms": 2032,
      "read_warm_ms": 626,
      "skip_feature": null,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1354_zorder_string",
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      "name": "zorder_string",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1354_zorder_string.sql",
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      "duration_ms": 4495,
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      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:05.962837+00:00",
      "read_cold_ms": 2221,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:deletion-vectors",
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        "delta:z-order",
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1355_zorder_boolean.sql",
      "read_script": "generator/spark-reads-df/verify_1355_zorder_boolean.py",
      "description": "Z-ORDER on BOOLEAN column. INSERT 200 rows in 4 batches,",
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      "duration_ms": 3097,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 1615,
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      "skip_feature": null,
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      "write_cold_ms": 213,
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        "type:boolean",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1356_zorder_two_columns",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1356_zorder_two_columns.sql",
      "read_script": "generator/spark-reads-df/verify_1356_zorder_two_columns.py",
      "description": "Z-ORDER on two columns simultaneously (INT + STRING).",
      "status": "pass",
      "duration_ms": 3211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:12.273768+00:00",
      "read_cold_ms": 1954,
      "read_warm_ms": 504,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 181,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1357_zorder_three_columns.sql",
      "read_script": "generator/spark-reads-df/verify_1357_zorder_three_columns.py",
      "description": "Z-ORDER on three columns (INT + DECIMAL + STRING).",
      "status": "pass",
      "duration_ms": 3391,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:15.665792+00:00",
      "read_cold_ms": 2177,
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      "skip_feature": null,
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      "write_cold_ms": 160,
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      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1358_zorder_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1358_zorder_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1358_zorder_cdc.py",
      "description": "Z-ORDER + CDC. ZORDER must NOT emit CDF rows. Critical interop test.",
      "status": "pass",
      "duration_ms": 4008,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:19.674898+00:00",
      "read_cold_ms": 1915,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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      "id": "df-writes/delta/1359_zorder_partition",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1359_zorder_partition.sql",
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      "description": "Z-ORDER on partitioned table. Z-ORDER should operate per-partition.",
      "status": "pass",
      "duration_ms": 3283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:22.958624+00:00",
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        "type:floating",
        "type:integer",
        "type:string",
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        "delta:partitioning",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/135_update_interop.sql",
      "read_script": "generator/spark-reads-df/verify_135_update_interop.py",
      "description": "- Chaos Update Interop table generation - 250 customers with deterministic data generation - Deletion vectors enabled - Various data types: Int64, Utf8, Timestamp",
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      "duration_ms": 1541,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1360_zorder_after_delete.sql",
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      "description": "Z-ORDER after DELETE. DELETE creates DVs, ZORDER materializes",
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      "duration_ms": 3390,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:26.349052+00:00",
      "read_cold_ms": 2256,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:delete",
        "dml:insert",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/1361_zorder_after_update",
      "num": 1361,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1361_zorder_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_1361_zorder_after_update.py",
      "description": "Z-ORDER after UPDATE. UPDATE creates DVs, ZORDER materializes",
      "status": "pass",
      "duration_ms": 3826,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:31:30.176104+00:00",
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      "tags": [
        "type:floating",
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        "dml:update",
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        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1362_zorder_after_merge",
      "num": 1362,
      "name": "zorder_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
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    },
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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      "reader": "spark",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1373_zorder_evolve_cdc.sql",
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      "duration_ms": 3793,
      "error": null,
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1374_zorder_four_way.sql",
      "read_script": "generator/spark-reads-df/verify_1374_zorder_four_way.py",
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      "duration_ms": 3986,
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      "extras": {},
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1375_zorder_five_way.sql",
      "read_script": "generator/spark-reads-df/verify_1375_zorder_five_way.py",
      "description": "Z-ORDER + CDC + colmap + partition + constraint. Five-way combo.",
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      "duration_ms": 3931,
      "error": null,
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        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1376_vacuum_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1376_vacuum_after_delete.py",
      "description": "VACUUM after DELETE with deletion vectors.",
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      "duration_ms": 5077,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
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      "reader": "spark",
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      "error": null,
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 3387,
      "error": null,
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      "manual_tags": [],
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      "id": "df-writes/delta/1379_vacuum_typed_data",
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 4434,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:32:43.741463+00:00",
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        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
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        "format:delta"
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    },
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      "id": "df-writes/delta/137_zorder_interop",
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      "description": "- Z-ORDER fragmented table creation - Multiple INSERT batches creating fragmentation - Deletion vectors enabled - Timestamp handling with microsecond precision",
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        "type:floating",
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    },
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        "type:decimal",
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      ],
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    },
    {
      "id": "df-writes/delta/1381_vacuum_timestamp",
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    },
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      "id": "df-writes/delta/1382_vacuum_cdc",
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        "type:integer",
        "type:string",
        "dml:cdc-write",
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        "direction:df-writes",
        "format:delta"
      ],
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      "id": "df-writes/delta/1383_vacuum_partition",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1384_vacuum_constraint",
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      ],
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    },
    {
      "id": "df-writes/delta/1385_vacuum_colmap",
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        "format:delta"
      ],
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    },
    {
      "id": "df-writes/delta/1386_vacuum_evolve",
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        "format:delta"
      ],
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      "id": "df-writes/delta/1387_vacuum_after_merge",
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        "format:delta"
      ],
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    },
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      "id": "df-writes/delta/1388_vacuum_cdc_partition",
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        "type:integer",
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        "dml:cdc-write",
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    {
      "id": "df-writes/delta/1389_vacuum_colmap_cdc",
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        "type:integer",
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        "dml:cdc-write",
        "dml:insert",
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        "delta:change-data-feed",
        "delta:column-mapping",
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        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/delta/138_analyze_interop",
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      "reader": "spark",
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      "id": "df-writes/delta/1390_vacuum_checkpoint",
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      "reader": "spark",
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      "duration_ms": 3883,
      "error": null,
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      "extras": {},
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        "format:delta"
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      "manual_tags": [],
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    },
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      "id": "df-writes/delta/1391_vacuum_then_dml",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1391_vacuum_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1391_vacuum_then_dml.py",
      "description": "DML operations after VACUUM.",
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      "duration_ms": 4593,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1392_vacuum_four_way.sql",
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      "duration_ms": 3558,
      "error": null,
      "notes": null,
      "extras": {},
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      "tags": [
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        "dml:insert",
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        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1393_vacuum_five_way",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1393_vacuum_five_way.sql",
      "read_script": "generator/spark-reads-df/verify_1393_vacuum_five_way.py",
      "description": "VACUUM + CDC + colmap + partition + constraint (five-way combo).",
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      "duration_ms": 3868,
      "error": null,
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        "delta:partitioning",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1394_restore_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1394_restore_basic.py",
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      "duration_ms": 5672,
      "error": null,
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      "extras": {},
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        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/1395_restore_after_delete",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
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      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:33:47.917940+00:00",
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        "dml:delete",
        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1396_restore_after_update",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1396_restore_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_1396_restore_after_update.py",
      "description": "RESTORE after UPDATE undoes the score change.",
      "status": "pass",
      "duration_ms": 4351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:33:52.270075+00:00",
      "read_cold_ms": 2573,
      "read_warm_ms": 824,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 191,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1397_restore_typed",
      "num": 1397,
      "name": "restore_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1397_restore_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1397_restore_typed.py",
      "description": "RESTORE preserves typed data (DECIMAL, TIMESTAMP, BOOLEAN).",
      "status": "pass",
      "duration_ms": 3417,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:33:55.687916+00:00",
      "read_cold_ms": 2018,
      "read_warm_ms": 616,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 104,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1398_restore_cdc",
      "num": 1398,
      "name": "restore_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1398_restore_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1398_restore_cdc.py",
      "description": "RESTORE with CDC enabled.",
      "status": "pass",
      "duration_ms": 4303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:33:59.991631+00:00",
      "read_cold_ms": 1941,
      "read_warm_ms": 611,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1399_restore_partition",
      "num": 1399,
      "name": "restore_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1399_restore_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1399_restore_partition.py",
      "description": "RESTORE on partitioned table.",
      "status": "pass",
      "duration_ms": 4032,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:04.026822+00:00",
      "read_cold_ms": 2293,
      "read_warm_ms": 608,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 191,
      "write_warm_ms": 264,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/139_checkpoint_interop",
      "num": 139,
      "name": "checkpoint_interop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/139_checkpoint_interop.sql",
      "read_script": "generator/spark-reads-df/verify_139_checkpoint_interop.py",
      "description": "- Multiple versions with inserts, updates, and deletes - Deletion vectors enabled - Checkpoint interoperability testing",
      "status": "pass",
      "duration_ms": 2282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:04.854514+00:00",
      "read_cold_ms": 1197,
      "read_warm_ms": 515,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 539,
      "write_warm_ms": 1057,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/13_action_metadata_change_schema",
      "num": 13,
      "name": "action_metadata_change_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/13_action_metadata_change_schema.sql",
      "read_script": "generator/spark-reads-df/verify_13_action_metadata_change_schema.py",
      "description": "Metadata action with schema changes.",
      "status": "pass",
      "duration_ms": 2527,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:07.382258+00:00",
      "read_cold_ms": 1160,
      "read_warm_ms": 437,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 811,
      "write_warm_ms": 482,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1400_restore_schema_evolve",
      "num": 1400,
      "name": "restore_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1400_restore_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1400_restore_schema_evolve.py",
      "description": "RESTORE to a version before schema evolution.",
      "status": "pass",
      "duration_ms": 3648,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:07.678153+00:00",
      "read_cold_ms": 2146,
      "read_warm_ms": 421,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 185,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1401_restore_after_merge",
      "num": 1401,
      "name": "restore_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1401_restore_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1401_restore_after_merge.py",
      "description": "RESTORE after MERGE. INSERT 100 (V0). MERGE 120-row CTE",
      "status": "pass",
      "duration_ms": 3828,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:11.507190+00:00",
      "read_cold_ms": 2302,
      "read_warm_ms": 604,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 84,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1402_restore_colmap",
      "num": 1402,
      "name": "restore_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1402_restore_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1402_restore_colmap.py",
      "description": "RESTORE with column mapping mode=name. INSERT 100 (V0).",
      "status": "pass",
      "duration_ms": 4102,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:15.609883+00:00",
      "read_cold_ms": 2315,
      "read_warm_ms": 759,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1403_restore_constraint",
      "num": 1403,
      "name": "restore_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1403_restore_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1403_restore_constraint.py",
      "description": "RESTORE removes constraint added after V0.",
      "status": "pass",
      "duration_ms": 4035,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:19.646095+00:00",
      "read_cold_ms": 2186,
      "read_warm_ms": 740,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1404_restore_multiple_dml",
      "num": 1404,
      "name": "restore_multiple_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1404_restore_multiple_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1404_restore_multiple_dml.py",
      "description": "RESTORE after multiple DML operations.",
      "status": "pass",
      "duration_ms": 3814,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:23.462434+00:00",
      "read_cold_ms": 1790,
      "read_warm_ms": 844,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 259,
      "write_warm_ms": 111,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1405_restore_decimal",
      "num": 1405,
      "name": "restore_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1405_restore_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1405_restore_decimal.py",
      "description": "RESTORE preserves DECIMAL precision.",
      "status": "pass",
      "duration_ms": 4443,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:27.906734+00:00",
      "read_cold_ms": 2283,
      "read_warm_ms": 801,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121,
      "write_warm_ms": 78,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1406_restore_timestamp",
      "num": 1406,
      "name": "restore_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1406_restore_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1406_restore_timestamp.py",
      "description": "RESTORE preserves TIMESTAMP microsecond precision.",
      "status": "pass",
      "duration_ms": 3955,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:31.863645+00:00",
      "read_cold_ms": 2299,
      "read_warm_ms": 730,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1407_restore_to_middle",
      "num": 1407,
      "name": "restore_to_middle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1407_restore_to_middle.sql",
      "read_script": "generator/spark-reads-df/verify_1407_restore_to_middle.py",
      "description": "RESTORE to a middle version (not V0).",
      "status": "pass",
      "duration_ms": 3610,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:35.475290+00:00",
      "read_cold_ms": 2069,
      "read_warm_ms": 421,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 400,
      "write_warm_ms": 375,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1408_restore_partition_typed",
      "num": 1408,
      "name": "restore_partition_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1408_restore_partition_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1408_restore_partition_typed.py",
      "description": "RESTORE with INT-typed partition column.",
      "status": "pass",
      "duration_ms": 3979,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:39.455611+00:00",
      "read_cold_ms": 2163,
      "read_warm_ms": 610,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 193,
      "write_warm_ms": 254,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1409_restore_cdc_partition",
      "num": 1409,
      "name": "restore_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1409_restore_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1409_restore_cdc_partition.py",
      "description": "RESTORE with CDC + partition. CDC captures RESTORE changes.",
      "status": "pass",
      "duration_ms": 4734,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:44.190579+00:00",
      "read_cold_ms": 3111,
      "read_warm_ms": 411,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/140_txn_log_interop",
      "num": 140,
      "name": "txn_log_interop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/140_txn_log_interop.sql",
      "read_script": "generator/spark-reads-df/verify_140_txn_log_interop.py",
      "description": "- Transaction log interoperability testing - Multiple versions (0-5) of data writes - Mixed engine history simulation - NO deletion vectors (no table properties)",
      "status": "pass",
      "duration_ms": 2454,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:09.837457+00:00",
      "read_cold_ms": 1579,
      "read_warm_ms": 435,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 235,
      "write_warm_ms": 341,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1410_restore_then_dml",
      "num": 1410,
      "name": "restore_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1410_restore_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1410_restore_then_dml.py",
      "description": "DML operations after RESTORE.",
      "status": "pass",
      "duration_ms": 4819,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:49.010634+00:00",
      "read_cold_ms": 2572,
      "read_warm_ms": 866,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 205,
      "write_warm_ms": 239,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1411_zorder_then_vacuum",
      "num": 1411,
      "name": "zorder_then_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1411_zorder_then_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1411_zorder_then_vacuum.py",
      "description": "Z-ORDER followed by VACUUM. INSERT 200 in 4 batches.",
      "status": "pass",
      "duration_ms": 4554,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:34:53.566280+00:00",
      "read_cold_ms": 2666,
      "read_warm_ms": 786,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 314,
      "write_warm_ms": 287,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1412_vacuum_then_zorder",
      "num": 1412,
      "name": "vacuum_then_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1412_vacuum_then_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1412_vacuum_then_zorder.py",
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    },
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        "type:integer",
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      "reader": "spark",
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      "error": null,
      "notes": null,
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      "reader": "spark",
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      "error": null,
      "notes": null,
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      "tags": [
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1428_restore_constraint_typed.py",
      "description": "RESTORE + constraint. RESTORE to pre-constraint state.",
      "status": "pass",
      "duration_ms": 6078,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 2460,
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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      "id": "df-writes/delta/1429_zorder_nmbys",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1429_zorder_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_1429_zorder_nmbys.py",
      "description": "Z-ORDER after MERGE NM-BY-SOURCE. Tests ZORDER on NM-BY-SOURCE-modified data.",
      "status": "pass",
      "duration_ms": 3828,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:07.912792+00:00",
      "read_cold_ms": 2587,
      "read_warm_ms": 601,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/142_dv_created_interop",
      "num": 142,
      "name": "dv_created_interop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/142_dv_created_interop.sql",
      "read_script": "generator/spark-reads-df/verify_142_dv_created_interop.py",
      "description": "- Deletion vectors enabled table creation - Initial data insertion (1000 rows) - Additional append insertion (100 rows) - Table prepared for DeltaForge to create deletion vectors",
      "status": "pass",
      "duration_ms": 3102,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:14.647968+00:00",
      "read_cold_ms": 1901,
      "read_warm_ms": 541,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 77,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1430_vacuum_nmbys",
      "num": 1430,
      "name": "vacuum_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1430_vacuum_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_1430_vacuum_nmbys.py",
      "description": "VACUUM after NM-BY-SOURCE. Cleans old files from NM-BY-SOURCE DELETE.",
      "status": "pass",
      "duration_ms": 3617,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:11.531431+00:00",
      "read_cold_ms": 2059,
      "read_warm_ms": 593,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 177,
      "write_warm_ms": 220,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1431_restore_nmbys",
      "num": 1431,
      "name": "restore_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1431_restore_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_1431_restore_nmbys.py",
      "description": "RESTORE after NM-BY-SOURCE to pre-MERGE state.",
      "status": "pass",
      "duration_ms": 6178,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:17.712642+00:00",
      "read_cold_ms": 2411,
      "read_warm_ms": 925,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 180,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1432_zorder_evolve_partition",
      "num": 1432,
      "name": "zorder_evolve_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1432_zorder_evolve_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1432_zorder_evolve_partition.py",
      "description": "Z-ORDER + schema evolution + partition. Three-way combo.",
      "status": "pass",
      "duration_ms": 3820,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:21.533994+00:00",
      "read_cold_ms": 2180,
      "read_warm_ms": 752,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
      "write_warm_ms": 205,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1433_vacuum_evolve_partition",
      "num": 1433,
      "name": "vacuum_evolve_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1433_vacuum_evolve_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1433_vacuum_evolve_partition.py",
      "description": "VACUUM + schema evolution + partition. Three-way combo.",
      "status": "pass",
      "duration_ms": 4287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:25.822219+00:00",
      "read_cold_ms": 2132,
      "read_warm_ms": 555,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 395,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1434_restore_evolve_partition",
      "num": 1434,
      "name": "restore_evolve_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1434_restore_evolve_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1434_restore_evolve_partition.py",
      "description": "RESTORE + schema evolution + partition to pre-evolution state.",
      "status": "pass",
      "duration_ms": 6065,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:31.888670+00:00",
      "read_cold_ms": 2833,
      "read_warm_ms": 761,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 273,
      "write_warm_ms": 269,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1435_zorder_checkpoint",
      "num": 1435,
      "name": "zorder_checkpoint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1435_zorder_checkpoint.sql",
      "read_script": "generator/spark-reads-df/verify_1435_zorder_checkpoint.py",
      "description": "Z-ORDER + checkpoint (12+ commits triggers checkpoint).",
      "status": "pass",
      "duration_ms": 4290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:36.179807+00:00",
      "read_cold_ms": 2447,
      "read_warm_ms": 816,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 848,
      "write_warm_ms": 787,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1436_vacuum_checkpoint",
      "num": 1436,
      "name": "vacuum_checkpoint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1436_vacuum_checkpoint.sql",
      "read_script": "generator/spark-reads-df/verify_1436_vacuum_checkpoint.py",
      "description": "VACUUM + checkpoint (12+ commits). VACUUM after checkpoint.",
      "status": "pass",
      "duration_ms": 5024,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:41.204874+00:00",
      "read_cold_ms": 3125,
      "read_warm_ms": 789,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 719,
      "write_warm_ms": 639,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1437_restore_checkpoint",
      "num": 1437,
      "name": "restore_checkpoint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1437_restore_checkpoint.sql",
      "read_script": "generator/spark-reads-df/verify_1437_restore_checkpoint.py",
      "description": "RESTORE after checkpoint to pre-checkpoint version.",
      "status": "pass",
      "duration_ms": 5589,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:46.794925+00:00",
      "read_cold_ms": 2395,
      "read_warm_ms": 758,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 588,
      "write_warm_ms": 735,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1438_zorder_stats",
      "num": 1438,
      "name": "zorder_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1438_zorder_stats.sql",
      "read_script": "generator/spark-reads-df/verify_1438_zorder_stats.py",
      "description": "Z-ORDER + verify stats correct after reorg. Multi-batch + ZORDER + predicate.",
      "status": "pass",
      "duration_ms": 10339,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:36:57.135607+00:00",
      "read_cold_ms": 2028,
      "read_warm_ms": 676,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 183,
      "write_warm_ms": 289,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1439_vacuum_stats",
      "num": 1439,
      "name": "vacuum_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1439_vacuum_stats.sql",
      "read_script": "generator/spark-reads-df/verify_1439_vacuum_stats.py",
      "description": "VACUUM + verify stats. After VACUUM old files gone, stats on",
      "status": "pass",
      "duration_ms": 6842,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:03.979805+00:00",
      "read_cold_ms": 2105,
      "read_warm_ms": 589,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 325,
      "write_warm_ms": 345,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/143_column_mapping_interop",
      "num": 143,
      "name": "column_mapping_interop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/143_column_mapping_interop.sql",
      "read_script": "generator/spark-reads-df/verify_143_column_mapping_interop.py",
      "description": "- Column mapping with \"name\" mode enabled - Initial data insertion (500 rows) - Additional append insertion (100 rows with NULL optional_field) - Protocol versions for column mapping (reader v2, writer v5)",
      "status": "pass",
      "duration_ms": 1900,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:16.552947+00:00",
      "read_cold_ms": 1181,
      "read_warm_ms": 308,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 232,
      "write_warm_ms": 113,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1440_restore_time_travel",
      "num": 1440,
      "name": "restore_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1440_restore_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_1440_restore_time_travel.py",
      "description": "RESTORE + time travel. After RESTORE, read the restored state + old versions.",
      "status": "pass",
      "duration_ms": 8228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:12.209406+00:00",
      "read_cold_ms": 2052,
      "read_warm_ms": 764,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 209,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1441_zorder_cdc_colmap_partition",
      "num": 1441,
      "name": "zorder_cdc_colmap_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1441_zorder_cdc_colmap_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1441_zorder_cdc_colmap_partition.py",
      "description": "Z-ORDER + CDC + colmap + partition. Four-way combo.",
      "status": "pass",
      "duration_ms": 4608,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:16.820324+00:00",
      "read_cold_ms": 2182,
      "read_warm_ms": 557,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 154,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1442_vacuum_cdc_colmap_partition",
      "num": 1442,
      "name": "vacuum_cdc_colmap_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1442_vacuum_cdc_colmap_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1442_vacuum_cdc_colmap_partition.py",
      "description": "VACUUM + CDC + colmap + partition. Four-way combo.",
      "status": "pass",
      "duration_ms": 4158,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:20.980577+00:00",
      "read_cold_ms": 2035,
      "read_warm_ms": 866,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1443_restore_cdc_colmap",
      "num": 1443,
      "name": "restore_cdc_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1443_restore_cdc_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1443_restore_cdc_colmap.py",
      "description": "RESTORE + CDC + colmap. Three-way combo.",
      "status": "pass",
      "duration_ms": 6317,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:27.299777+00:00",
      "read_cold_ms": 2147,
      "read_warm_ms": 505,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1444_zorder_then_update_delete",
      "num": 1444,
      "name": "zorder_then_update_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1444_zorder_then_update_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1444_zorder_then_update_delete.py",
      "description": "Z-ORDER then UPDATE then DELETE. Full lifecycle.",
      "status": "pass",
      "duration_ms": 5532,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:32.833074+00:00",
      "read_cold_ms": 3220,
      "read_warm_ms": 995,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 264,
      "write_warm_ms": 531,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1445_vacuum_then_merge",
      "num": 1445,
      "name": "vacuum_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1445_vacuum_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1445_vacuum_then_merge.py",
      "description": "VACUUM then MERGE. Tests MERGE after VACUUM cleaned old files.",
      "status": "pass",
      "duration_ms": 5324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:38.158147+00:00",
      "read_cold_ms": 2818,
      "read_warm_ms": 1161,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 310,
      "write_warm_ms": 329,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1446_restore_then_merge",
      "num": 1446,
      "name": "restore_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1446_restore_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1446_restore_then_merge.py",
      "description": "RESTORE then MERGE on restored data.",
      "status": "pass",
      "duration_ms": 7201,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:45.361558+00:00",
      "read_cold_ms": 2926,
      "read_warm_ms": 792,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 238,
      "write_warm_ms": 193,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1447_zorder_vacuum_restore_lifecycle",
      "num": 1447,
      "name": "zorder_vacuum_restore_lifecycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1447_zorder_vacuum_restore_lifecycle.sql",
      "read_script": "generator/spark-reads-df/verify_1447_zorder_vacuum_restore_lifecycle.py",
      "description": "Full lifecycle: INSERT, Z-ORDER, DML, VACUUM, more DML.",
      "status": "pass",
      "duration_ms": 4760,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:50.125332+00:00",
      "read_cold_ms": 2396,
      "read_warm_ms": 510,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 562,
      "write_warm_ms": 405,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:time-travel",
        "delta:vacuum",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1448_zorder_all_features",
      "num": 1448,
      "name": "zorder_all_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1448_zorder_all_features.sql",
      "read_script": "generator/spark-reads-df/verify_1448_zorder_all_features.py",
      "description": "Z-ORDER + all features: CDC+colmap+partition+constraint+evolve.",
      "status": "pass",
      "duration_ms": 4529,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:54.657820+00:00",
      "read_cold_ms": 2438,
      "read_warm_ms": 651,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 511,
      "write_warm_ms": 381,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1449_vacuum_all_features",
      "num": 1449,
      "name": "vacuum_all_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1449_vacuum_all_features.sql",
      "read_script": "generator/spark-reads-df/verify_1449_vacuum_all_features.py",
      "description": "VACUUM + all features: CDC+colmap+partition+constraint+evolve.",
      "status": "pass",
      "duration_ms": 3511,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:37:58.169664+00:00",
      "read_cold_ms": 1782,
      "read_warm_ms": 620,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 411,
      "write_warm_ms": 384,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/144_concurrent_writes_interop",
      "num": 144,
      "name": "concurrent_writes_interop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/144_concurrent_writes_interop.sql",
      "read_script": "generator/spark-reads-df/verify_144_concurrent_writes_interop.py",
      "description": "- Concurrent write scenarios with multiple batches - Deletion vectors enabled - Multiple versions (6 total: init + 5 batches) - Timestamp fields with NULL values",
      "status": "pass",
      "duration_ms": 3125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:19.681329+00:00",
      "read_cold_ms": 1856,
      "read_warm_ms": 637,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 256,
      "write_warm_ms": 191,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1450_zorder_vacuum_restore_ultimate",
      "num": 1450,
      "name": "zorder_vacuum_restore_ultimate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1450_zorder_vacuum_restore_ultimate.sql",
      "read_script": "generator/spark-reads-df/verify_1450_zorder_vacuum_restore_ultimate.py",
      "description": "ULTIMATE test. INSERT, Z-ORDER, DML, VACUUM, RESTORE lifecycle. All features.",
      "status": "pass",
      "duration_ms": 11412,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:09.583685+00:00",
      "read_cold_ms": 3130,
      "read_warm_ms": 783,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 911,
      "write_warm_ms": 485,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:time-travel",
        "delta:vacuum",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1451_partition_by_decimal",
      "num": 1451,
      "name": "partition_by_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1451_partition_by_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1451_partition_by_decimal.py",
      "description": "DECIMAL(5,2) partition key. 4 partitions with typed DECIMAL values.",
      "status": "pass",
      "duration_ms": 6599,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:16.184410+00:00",
      "read_cold_ms": 2795,
      "read_warm_ms": 880,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 219,
      "write_warm_ms": 86,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1452_partition_by_decimal_negative",
      "num": 1452,
      "name": "partition_by_decimal_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1452_partition_by_decimal_negative.sql",
      "read_script": "generator/spark-reads-df/verify_1452_partition_by_decimal_negative.py",
      "description": "DECIMAL(8,2) partition key with negative values. 3 partitions: -100.00, 0.00, 500.00.",
      "status": "pass",
      "duration_ms": 6857,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:23.044805+00:00",
      "read_cold_ms": 3545,
      "read_warm_ms": 976,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 226,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1453_partition_by_decimal_merge",
      "num": 1453,
      "name": "partition_by_decimal_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1453_partition_by_decimal_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1453_partition_by_decimal_merge.py",
      "description": "MERGE across DECIMAL(5,2) partitions. INSERT 80 base rows,",
      "status": "pass",
      "duration_ms": 8093,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:31.141392+00:00",
      "read_cold_ms": 3586,
      "read_warm_ms": 1259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 160,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1454_partition_by_decimal_cdc",
      "num": 1454,
      "name": "partition_by_decimal_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1454_partition_by_decimal_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1454_partition_by_decimal_cdc.py",
      "description": "DECIMAL(5,2) partition key + CDC. CDF must contain correct DECIMAL partition values.",
      "status": "pass",
      "duration_ms": 8066,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:39.209034+00:00",
      "read_cold_ms": 3771,
      "read_warm_ms": 1046,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 121,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1455_partition_by_date",
      "num": 1455,
      "name": "partition_by_date",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1455_partition_by_date.sql",
      "read_script": "generator/spark-reads-df/verify_1455_partition_by_date.py",
      "description": "DATE partition key. 4 DATE partitions, 30 days apart.",
      "status": "pass",
      "duration_ms": 7130,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:46.340007+00:00",
      "read_cold_ms": 3479,
      "read_warm_ms": 1160,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 271,
      "tags": [
        "type:date",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1456_partition_by_date_merge",
      "num": 1456,
      "name": "partition_by_date_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1456_partition_by_date_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1456_partition_by_date_merge.py",
      "description": "MERGE across DATE partitions. INSERT 80 base rows,",
      "status": "pass",
      "duration_ms": 6863,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:53.204345+00:00",
      "read_cold_ms": 3266,
      "read_warm_ms": 1110,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 261,
      "write_warm_ms": 151,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1457_partition_by_date_cdc",
      "num": 1457,
      "name": "partition_by_date_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1457_partition_by_date_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1457_partition_by_date_cdc.py",
      "description": "DATE partition key + CDC. CDF must contain correct DATE partition values.",
      "status": "pass",
      "duration_ms": 6470,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:38:59.675629+00:00",
      "read_cold_ms": 3272,
      "read_warm_ms": 1051,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 128,
      "tags": [
        "type:date",
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1458_partition_by_date_evolve",
      "num": 1458,
      "name": "partition_by_date_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1458_partition_by_date_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1458_partition_by_date_evolve.py",
      "description": "DATE partition key + schema evolution. INSERT 60 rows, ALTER ADD COLUMN,",
      "status": "pass",
      "duration_ms": 5647,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:05.325092+00:00",
      "read_cold_ms": 2654,
      "read_warm_ms": 864,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 76,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1459_partition_by_timestamp_string",
      "num": 1459,
      "name": "partition_by_timestamp_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1459_partition_by_timestamp_string.sql",
      "read_script": "generator/spark-reads-df/verify_1459_partition_by_timestamp_string.py",
      "description": "STRING partition key representing hourly timestamp buckets (production pattern).",
      "status": "pass",
      "duration_ms": 6672,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:11.999113+00:00",
      "read_cold_ms": 3870,
      "read_warm_ms": 802,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/145_cdf_write",
      "num": 145,
      "name": "cdf_write",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/145_cdf_write.sql",
      "read_script": "generator/spark-reads-df/verify_145_cdf_write.py",
      "description": "Download table with CDF enabled -> DeltaForge performs UPDATE/DELETE -> Verify _change_data/ files created",
      "status": "pass",
      "duration_ms": 3329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:23.011760+00:00",
      "read_cold_ms": 1841,
      "read_warm_ms": 853,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 83,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1460_partition_by_date_dml",
      "num": 1460,
      "name": "partition_by_date_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1460_partition_by_date_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1460_partition_by_date_dml.py",
      "description": "DATE partition key with targeted DML per partition.",
      "status": "pass",
      "duration_ms": 6275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:18.275622+00:00",
      "read_cold_ms": 2688,
      "read_warm_ms": 1023,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 137,
      "write_warm_ms": 118,
      "tags": [
        "type:date",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1461_partition_by_decimal_constraint",
      "num": 1461,
      "name": "partition_by_decimal_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1461_partition_by_decimal_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1461_partition_by_decimal_constraint.py",
      "description": "DECIMAL(5,2) partition key + CHECK constraint (score >= 0).",
      "status": "pass",
      "duration_ms": 6219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:24.497675+00:00",
      "read_cold_ms": 2650,
      "read_warm_ms": 973,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 184,
      "write_warm_ms": 425,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
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      "write_script": "generator/df-sql/1497_float_update.sql",
      "read_script": "generator/spark-reads-df/verify_1497_float_update.py",
      "description": "FLOAT (32-bit) column through UPDATE.",
      "status": "pass",
      "duration_ms": 5813,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:42:46.484752+00:00",
      "read_cold_ms": 3911,
      "read_warm_ms": 1030,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 204,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1498_float_merge",
      "num": 1498,
      "name": "float_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1498_float_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1498_float_merge.py",
      "description": "FLOAT (32-bit) column through MERGE.",
      "status": "pass",
      "duration_ms": 5139,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:42:51.624962+00:00",
      "read_cold_ms": 2872,
      "read_warm_ms": 1189,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 154,
      "write_warm_ms": 109,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1499_smallint_tinyint_dml",
      "num": 1499,
      "name": "smallint_tinyint_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1499_smallint_tinyint_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1499_smallint_tinyint_dml.py",
      "description": "SMALLINT + TINYINT through full DML chain.",
      "status": "pass",
      "duration_ms": 5639,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:42:57.265130+00:00",
      "read_cold_ms": 3371,
      "read_warm_ms": 995,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 413,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/149_identity_column",
      "num": 149,
      "name": "identity_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/149_identity_column.sql",
      "read_script": "generator/spark-reads-df/verify_149_identity_column.py",
      "description": "- IDENTITY column (auto_id with START WITH 1 INCREMENT BY 1) - Event data with deterministic formulas - Timestamp handling with microsecond precision",
      "status": "pass",
      "duration_ms": 1975,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:34.619351+00:00",
      "read_cold_ms": 1131,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 41,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/14_action_format_specification_parquet",
      "num": 14,
      "name": "action_format_specification_parquet",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/14_action_format_specification_parquet.sql",
      "read_script": "generator/spark-reads-df/verify_14_action_format_specification_parquet.py",
      "description": "Validates the Delta table written by DeltaForge for test 14. E-commerce product catalog with 22 columns. has_reviews, is_discounted.",
      "status": "pass",
      "duration_ms": 4604,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:39.224825+00:00",
      "read_cold_ms": 1744,
      "read_warm_ms": 861,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 82,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1500_smallint_partition",
      "num": 1500,
      "name": "smallint_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1500_smallint_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1500_smallint_partition.py",
      "description": "SMALLINT through a partitioned table.",
      "status": "pass",
      "duration_ms": 5109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:02.375251+00:00",
      "read_cold_ms": 3045,
      "read_warm_ms": 933,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 346,
      "write_warm_ms": 327,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1501_merge_coalesce_decimal",
      "num": 1501,
      "name": "merge_coalesce_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1501_merge_coalesce_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1501_merge_coalesce_decimal.py",
      "description": "MERGE with COALESCE on DECIMAL column.",
      "status": "pass",
      "duration_ms": 6687,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:09.063925+00:00",
      "read_cold_ms": 3988,
      "read_warm_ms": 1264,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 91,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1502_merge_coalesce_timestamp",
      "num": 1502,
      "name": "merge_coalesce_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1502_merge_coalesce_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1502_merge_coalesce_timestamp.py",
      "description": "MERGE with COALESCE on TIMESTAMP column.",
      "status": "pass",
      "duration_ms": 5743,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:14.808614+00:00",
      "read_cold_ms": 3701,
      "read_warm_ms": 1060,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 231,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1503_merge_nullif_decimal",
      "num": 1503,
      "name": "merge_nullif_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1503_merge_nullif_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1503_merge_nullif_decimal.py",
      "description": "MERGE with NULLIF on DECIMAL column.",
      "status": "pass",
      "duration_ms": 5027,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:19.836828+00:00",
      "read_cold_ms": 3282,
      "read_warm_ms": 950,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 81,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1504_merge_coalesce_multi_col",
      "num": 1504,
      "name": "merge_coalesce_multi_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1504_merge_coalesce_multi_col.sql",
      "read_script": "generator/spark-reads-df/verify_1504_merge_coalesce_multi_col.py",
      "description": "MERGE with COALESCE across 3 different typed columns simultaneously.",
      "status": "pass",
      "duration_ms": 5659,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:25.496929+00:00",
      "read_cold_ms": 3383,
      "read_warm_ms": 936,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 106,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1505_merge_coalesce_nmbys",
      "num": 1505,
      "name": "merge_coalesce_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1505_merge_coalesce_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_1505_merge_coalesce_nmbys.py",
      "description": "MERGE with COALESCE in NOT MATCHED BY SOURCE clause.",
      "status": "pass",
      "duration_ms": 4998,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:30.496555+00:00",
      "read_cold_ms": 2968,
      "read_warm_ms": 1064,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 113,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1506_delete_where_coalesce_decimal",
      "num": 1506,
      "name": "delete_where_coalesce_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1506_delete_where_coalesce_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1506_delete_where_coalesce_decimal.py",
      "description": "DELETE WHERE COALESCE(amount, 0) < threshold.",
      "status": "pass",
      "duration_ms": 5638,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:36.136911+00:00",
      "read_cold_ms": 2955,
      "read_warm_ms": 1277,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 100,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1507_update_where_abs_decimal",
      "num": 1507,
      "name": "update_where_abs_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1507_update_where_abs_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1507_update_where_abs_decimal.py",
      "description": "UPDATE WHERE ABS(CAST(balance AS DOUBLE)) > threshold.",
      "status": "pass",
      "duration_ms": 6097,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:42.235163+00:00",
      "read_cold_ms": 3732,
      "read_warm_ms": 1173,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 151,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1508_delete_where_floor_double",
      "num": 1508,
      "name": "delete_where_floor_double",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1508_delete_where_floor_double.sql",
      "read_script": "generator/spark-reads-df/verify_1508_delete_where_floor_double.py",
      "description": "DELETE WHERE FLOOR(value) > threshold.",
      "status": "pass",
      "duration_ms": 5381,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:47.621366+00:00",
      "read_cold_ms": 3145,
      "read_warm_ms": 988,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 233,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1509_update_where_length_concat",
      "num": 1509,
      "name": "update_where_length_concat",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1509_update_where_length_concat.sql",
      "read_script": "generator/spark-reads-df/verify_1509_update_where_length_concat.py",
      "description": "UPDATE WHERE LENGTH(CONCAT(first_name, last_name)) > threshold.",
      "status": "pass",
      "duration_ms": 5644,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:53.266859+00:00",
      "read_cold_ms": 3607,
      "read_warm_ms": 1096,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 306,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/150_check_constraints",
      "num": 150,
      "name": "check_constraints",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/150_check_constraints.sql",
      "read_script": "generator/spark-reads-df/verify_150_check_constraints.py",
      "description": "- CHECK constraints for data validation - Decimal precision handling - Float rounding for rating values",
      "status": "pass",
      "duration_ms": 3080,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:42.306180+00:00",
      "read_cold_ms": 1992,
      "read_warm_ms": 578,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 41,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1510_merge_where_coalesce_predicate",
      "num": 1510,
      "name": "merge_where_coalesce_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1510_merge_where_coalesce_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_1510_merge_where_coalesce_predicate.py",
      "description": "MERGE with COALESCE in WHEN MATCHED condition.",
      "status": "pass",
      "duration_ms": 4953,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:43:58.221482+00:00",
      "read_cold_ms": 2694,
      "read_warm_ms": 1152,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 168,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1511_delete_where_greatest",
      "num": 1511,
      "name": "delete_where_greatest",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1511_delete_where_greatest.sql",
      "read_script": "generator/spark-reads-df/verify_1511_delete_where_greatest.py",
      "description": "DELETE WHERE GREATEST(a, b, c) > threshold.",
      "status": "pass",
      "duration_ms": 6006,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:04.229146+00:00",
      "read_cold_ms": 3181,
      "read_warm_ms": 1182,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 21,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1512_update_where_least",
      "num": 1512,
      "name": "update_where_least",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1512_update_where_least.sql",
      "read_script": "generator/spark-reads-df/verify_1512_update_where_least.py",
      "description": "UPDATE WHERE LEAST(a, b, c) < threshold.",
      "status": "pass",
      "duration_ms": 6414,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:10.645036+00:00",
      "read_cold_ms": 4518,
      "read_warm_ms": 1173,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 79,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1513_merge_abs_predicate",
      "num": 1513,
      "name": "merge_abs_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1513_merge_abs_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_1513_merge_abs_predicate.py",
      "description": "MERGE with ABS in WHEN MATCHED AND condition.",
      "status": "pass",
      "duration_ms": 4517,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:15.163754+00:00",
      "read_cold_ms": 2328,
      "read_warm_ms": 940,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 164,
      "write_warm_ms": 144,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1514_delete_where_coalesce_or",
      "num": 1514,
      "name": "delete_where_coalesce_or",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1514_delete_where_coalesce_or.sql",
      "read_script": "generator/spark-reads-df/verify_1514_delete_where_coalesce_or.py",
      "description": "DELETE with COALESCE + OR compound predicate.",
      "status": "pass",
      "duration_ms": 5304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:20.469054+00:00",
      "read_cold_ms": 3099,
      "read_warm_ms": 1121,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 134,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1515_update_greatest_least_decimal",
      "num": 1515,
      "name": "update_greatest_least_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1515_update_greatest_least_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1515_update_greatest_least_decimal.py",
      "description": "UPDATE SET using GREATEST/LEAST on DECIMAL columns.",
      "status": "pass",
      "duration_ms": 4363,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:24.832826+00:00",
      "read_cold_ms": 2743,
      "read_warm_ms": 783,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 123,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1516_partition_dir_int_values",
      "num": 1516,
      "name": "partition_dir_int_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1516_partition_dir_int_values.sql",
      "read_script": "generator/spark-reads-df/verify_1516_partition_dir_int_values.py",
      "description": "Verify INT partition directory encoding.",
      "status": "pass",
      "duration_ms": 3371,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:28.205692+00:00",
      "read_cold_ms": 1941,
      "read_warm_ms": 671,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 200,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1517_partition_dir_boolean_values",
      "num": 1517,
      "name": "partition_dir_boolean_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1517_partition_dir_boolean_values.sql",
      "read_script": "generator/spark-reads-df/verify_1517_partition_dir_boolean_values.py",
      "description": "Verify BOOLEAN partition directory encoding.",
      "status": "pass",
      "duration_ms": 3706,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:31.921640+00:00",
      "read_cold_ms": 2224,
      "read_warm_ms": 550,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 63,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1518_partition_dir_decimal_values",
      "num": 1518,
      "name": "partition_dir_decimal_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1518_partition_dir_decimal_values.sql",
      "read_script": "generator/spark-reads-df/verify_1518_partition_dir_decimal_values.py",
      "description": "Verify DECIMAL partition directory encoding.",
      "status": "pass",
      "duration_ms": 5157,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:37.080334+00:00",
      "read_cold_ms": 3195,
      "read_warm_ms": 819,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 90,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1519_partition_dir_null_int",
      "num": 1519,
      "name": "partition_dir_null_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1519_partition_dir_null_int.sql",
      "read_script": "generator/spark-reads-df/verify_1519_partition_dir_null_int.py",
      "description": "Verify NULL INT partition directory encoding.",
      "status": "pass",
      "duration_ms": 6128,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:43.210506+00:00",
      "read_cold_ms": 3564,
      "read_warm_ms": 1531,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 77,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/151_protocol_block",
      "num": 151,
      "name": "protocol_block",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/151_protocol_block.sql",
      "read_script": "generator/spark-reads-df/verify_151_protocol_block.py",
      "description": "- Protocol version blocking with advanced features - Deletion vectors - Row tracking - Column mapping (name mode) - DELETE operations using IN predicate",
      "status": "pass",
      "duration_ms": 3654,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:45.960552+00:00",
      "read_cold_ms": 2153,
      "read_warm_ms": 619,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1520_partition_dir_special_string",
      "num": 1520,
      "name": "partition_dir_special_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1520_partition_dir_special_string.sql",
      "read_script": "generator/spark-reads-df/verify_1520_partition_dir_special_string.py",
      "description": "Verify partition directories with special characters in STRING values.",
      "status": "pass",
      "duration_ms": 3918,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:47.133074+00:00",
      "read_cold_ms": 2137,
      "read_warm_ms": 697,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1521_coalesce_cdc_partition",
      "num": 1521,
      "name": "coalesce_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1521_coalesce_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1521_coalesce_cdc_partition.py",
      "description": "COALESCE in UPDATE + CDC + partition. Three-way combination.",
      "status": "pass",
      "duration_ms": 6034,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:44:53.169321+00:00",
      "read_cold_ms": 3018,
      "read_warm_ms": 1000,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 174,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1522_functions_constraint",
      "num": 1522,
      "name": "functions_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1522_functions_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1522_functions_constraint.py",
      "description": "SQL functions + constraint combination.",
      "status": "pass",
      "duration_ms": 6996,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:00.166560+00:00",
      "read_cold_ms": 3232,
      "read_warm_ms": 1836,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 110,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1523_functions_colmap",
      "num": 1523,
      "name": "functions_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1523_functions_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1523_functions_colmap.py",
      "description": "SQL functions + column mapping (mode=name).",
      "status": "pass",
      "duration_ms": 4937,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:05.104765+00:00",
      "read_cold_ms": 3025,
      "read_warm_ms": 864,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 179,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1524_functions_evolve",
      "num": 1524,
      "name": "functions_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1524_functions_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1524_functions_evolve.py",
      "description": "SQL functions on evolved (newly added) column.",
      "status": "pass",
      "duration_ms": 4437,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:09.544537+00:00",
      "read_cold_ms": 2730,
      "read_warm_ms": 697,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 554,
      "write_warm_ms": 217,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1525_binary_struct_combo",
      "num": 1525,
      "name": "binary_struct_combo",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1525_binary_struct_combo.sql",
      "read_script": "generator/spark-reads-df/verify_1525_binary_struct_combo.py",
      "description": "BINARY + STRUCT in same table. Tests complex type combination",
      "status": "pass",
      "duration_ms": 4956,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:14.502876+00:00",
      "read_cold_ms": 2714,
      "read_warm_ms": 1039,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 159,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1526_coalesce_merge_cdc",
      "num": 1526,
      "name": "coalesce_merge_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1526_coalesce_merge_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1526_coalesce_merge_cdc.py",
      "description": "MERGE with COALESCE + CDC.",
      "status": "pass",
      "duration_ms": 5577,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:20.082825+00:00",
      "read_cold_ms": 2771,
      "read_warm_ms": 1108,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 241,
      "write_warm_ms": 348,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1527_functions_zorder",
      "num": 1527,
      "name": "functions_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1527_functions_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1527_functions_zorder.py",
      "description": "SQL functions in DML then Z-ORDER.",
      "status": "pass",
      "duration_ms": 3993,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:24.078896+00:00",
      "read_cold_ms": 2107,
      "read_warm_ms": 711,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 276,
      "write_warm_ms": 180,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1528_functions_vacuum",
      "num": 1528,
      "name": "functions_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1528_functions_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1528_functions_vacuum.py",
      "description": "SQL functions in DML then VACUUM.",
      "status": "pass",
      "duration_ms": 4368,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:28.448773+00:00",
      "read_cold_ms": 2740,
      "read_warm_ms": 576,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 234,
      "write_warm_ms": 306,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1529_functions_restore",
      "num": 1529,
      "name": "functions_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1529_functions_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1529_functions_restore.py",
      "description": "SQL functions in DML then RESTORE.",
      "status": "pass",
      "duration_ms": 6591,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:35.041321+00:00",
      "read_cold_ms": 2160,
      "read_warm_ms": 649,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 177,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/152_uniform_iceberg",
      "num": 152,
      "name": "uniform_iceberg",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/152_uniform_iceberg.sql",
      "read_script": "generator/spark-reads-df/verify_152_uniform_iceberg.py",
      "description": "- Simple table creation (Uniform/UniFormat requires Unity Catalog) - 100 rows with deterministic data - Placeholder for Uniform/Iceberg interop testing",
      "status": "pass",
      "duration_ms": 1919,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:47.880666+00:00",
      "read_cold_ms": 1592,
      "read_warm_ms": 155,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 28,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1530_decimal_partition_functions",
      "num": 1530,
      "name": "decimal_partition_functions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1530_decimal_partition_functions.sql",
      "read_script": "generator/spark-reads-df/verify_1530_decimal_partition_functions.py",
      "description": "DECIMAL partition + COALESCE function.",
      "status": "pass",
      "duration_ms": 3688,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:38.731681+00:00",
      "read_cold_ms": 2483,
      "read_warm_ms": 623,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 140,
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        "type:decimal",
        "type:integer",
        "type:string",
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        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1531_date_partition_functions",
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      "reader": "spark",
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      "description": "DATE partition + LENGTH/TRIM/ABS functions.",
      "status": "pass",
      "duration_ms": 3097,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:41.830399+00:00",
      "read_cold_ms": 1881,
      "read_warm_ms": 550,
      "skip_feature": null,
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      "write_cold_ms": 263,
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      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1532_float_cdc",
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      "name": "float_cdc",
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      "reader": "spark",
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      "description": "INSERT 100 rows. val = CAST(i * 1.5 AS FLOAT). FloatType must be preserved in both main table and CDF.",
      "status": "pass",
      "duration_ms": 1900,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:43.731733+00:00",
      "read_cold_ms": 1255,
      "read_warm_ms": 247,
      "skip_feature": null,
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      "write_cold_ms": 70,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1533_float_partition",
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      "name": "float_partition",
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      "read_script": "generator/spark-reads-df/verify_1533_float_partition.py",
      "description": "FLOAT in partitioned table.",
      "status": "pass",
      "duration_ms": 1128,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-19T17:45:44.860264+00:00",
      "read_cold_ms": 695,
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        "type:integer",
        "type:string",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1534_tinyint_dml_chain",
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      "name": "tinyint_dml_chain",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1534_tinyint_dml_chain.py",
      "description": "TINYINT through INSERT/UPDATE/DELETE chain.",
      "status": "pass",
      "duration_ms": 1112,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:45.973054+00:00",
      "read_cold_ms": 662,
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      "skip_feature": null,
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      "write_cold_ms": 160,
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        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1535_tinyint_merge",
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      "reader": "spark",
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      "description": "INSERT 80 rows. val = (i*3)%127. MERGE from 100-row CTE: MATCHED -> UPDATE val=source.val ((i*7)%127), name='merged'. NOT MATCHED -> INSERT ids 81-100.",
      "status": "pass",
      "duration_ms": 1205,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:47.178924+00:00",
      "read_cold_ms": 707,
      "read_warm_ms": 242,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1536_smallint_merge",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT 80 rows. val = (i*11)%30000. MERGE from 100-row CTE: MATCHED -> UPDATE val=source.val ((i*19)%30000), name='merged'. NOT MATCHED -> INSERT ids 81-100.",
      "status": "pass",
      "duration_ms": 1146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:48.326139+00:00",
      "read_cold_ms": 646,
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      "skip_feature": null,
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      "write_cold_ms": 86,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1537_smallint_cdc",
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      "name": "smallint_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT 100 rows. val = (i*13)%30000.",
      "status": "pass",
      "duration_ms": 1523,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 738,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1538_tinyint_partition",
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      "name": "tinyint_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1538_tinyint_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1538_tinyint_partition.py",
      "description": "TINYINT in partitioned table.",
      "status": "pass",
      "duration_ms": 1521,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:51.371850+00:00",
      "read_cold_ms": 866,
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      "skip_feature": null,
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      "write_cold_ms": 186,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/1539_binary_partition",
      "num": 1539,
      "name": "binary_partition",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1539_binary_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1539_binary_partition.py",
      "description": "BINARY in partitioned table.",
      "status": "pass",
      "duration_ms": 1606,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:52.978096+00:00",
      "read_cold_ms": 1161,
      "read_warm_ms": 200,
      "skip_feature": null,
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      "write_cold_ms": 220,
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        "type:binary",
        "type:integer",
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        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/153_int96_timestamp",
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      "name": "int96_timestamp",
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      "write_script": "generator/df-sql/153_int96_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_153_int96_timestamp.py",
      "description": "DBX writes table with INT96 timestamps -> DeltaForge reads correctly",
      "status": "pass",
      "duration_ms": 2662,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:50.543800+00:00",
      "read_cold_ms": 1842,
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      "skip_feature": null,
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      "write_cold_ms": 56,
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      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1540_struct_functions",
      "num": 1540,
      "name": "struct_functions",
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/1540_struct_functions.sql",
      "read_script": "generator/spark-reads-df/verify_1540_struct_functions.py",
      "description": "STRUCT + SQL functions on non-struct columns.",
      "status": "pass",
      "duration_ms": 1404,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:54.382912+00:00",
      "read_cold_ms": 716,
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      "skip_feature": null,
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      "write_cold_ms": 39,
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      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1541_functions_multi_type",
      "num": 1541,
      "name": "functions_multi_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1541_functions_multi_type.sql",
      "read_script": "generator/spark-reads-df/verify_1541_functions_multi_type.py",
      "description": "SQL functions across 4 types in single UPDATE.",
      "status": "pass",
      "duration_ms": 1434,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:55.817551+00:00",
      "read_cold_ms": 765,
      "read_warm_ms": 289,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 92,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1542_functions_delete_merge",
      "num": 1542,
      "name": "functions_delete_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1542_functions_delete_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1542_functions_delete_merge.py",
      "description": "DELETE with function predicate, then MERGE with function in SET.",
      "status": "pass",
      "duration_ms": 1572,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:57.389937+00:00",
      "read_cold_ms": 846,
      "read_warm_ms": 370,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 337,
      "write_warm_ms": 182,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1543_functions_checkpoint",
      "num": 1543,
      "name": "functions_checkpoint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1543_functions_checkpoint.sql",
      "read_script": "generator/spark-reads-df/verify_1543_functions_checkpoint.py",
      "description": "SQL functions in DML across 12+ commits with checkpoint.",
      "status": "pass",
      "duration_ms": 1780,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:45:59.170640+00:00",
      "read_cold_ms": 897,
      "read_warm_ms": 414,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 735,
      "write_warm_ms": 676,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1544_coalesce_nmbys_cdc",
      "num": 1544,
      "name": "coalesce_nmbys_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1544_coalesce_nmbys_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1544_coalesce_nmbys_cdc.py",
      "description": "COALESCE in NOT MATCHED BY SOURCE + CDC.",
      "status": "pass",
      "duration_ms": 1875,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:01.046181+00:00",
      "read_cold_ms": 874,
      "read_warm_ms": 385,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 247,
      "write_warm_ms": 325,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1545_decimal_partition_zorder",
      "num": 1545,
      "name": "decimal_partition_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1545_decimal_partition_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1545_decimal_partition_zorder.py",
      "description": "DECIMAL partition + Z-ORDER.",
      "status": "pass",
      "duration_ms": 1148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:02.195035+00:00",
      "read_cold_ms": 623,
      "read_warm_ms": 183,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 554,
      "write_warm_ms": 337,
      "tags": [
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/1546_date_partition_time_travel",
      "num": 1546,
      "name": "date_partition_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1546_date_partition_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_1546_date_partition_time_travel.py",
      "description": "DATE partition + time travel.",
      "status": "pass",
      "duration_ms": 2687,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:04.882315+00:00",
      "read_cold_ms": 889,
      "read_warm_ms": 205,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 334,
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      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/1547_functions_typed_partition_cdc",
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      "name": "functions_typed_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1547_functions_typed_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1547_functions_typed_partition_cdc.py",
      "description": "SQL functions + typed partition + CDC. Three-way combination.",
      "status": "pass",
      "duration_ms": 1257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:06.140011+00:00",
      "read_cold_ms": 734,
      "read_warm_ms": 213,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 161,
      "tags": [
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/1548_binary_struct_functions",
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      "name": "binary_struct_functions",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1548_binary_struct_functions.py",
      "description": "BINARY + STRUCT + SQL functions combo.",
      "status": "pass",
      "duration_ms": 1186,
      "error": null,
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        "type:binary",
        "type:integer",
        "type:string",
        "type:struct",
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        "dml:update",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
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      "error": null,
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        "type:decimal",
        "type:floating",
        "type:integer",
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        "dml:update",
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        "direction:df-writes",
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      "manual_tags": [],
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      "read_warm_ms": 416,
      "skip_feature": null,
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        "type:integer",
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        "scale:large-dataset",
        "direction:df-writes",
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    },
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        "format:delta"
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      "manual_tags": [],
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      "skip_feature": null,
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      "manual_tags": [],
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      "status": "pass",
      "duration_ms": 1056,
      "error": null,
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      "verified_at": "2026-05-19T17:46:13.131684+00:00",
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      "read_warm_ms": 181,
      "skip_feature": null,
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        "dml:update",
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        "format:delta"
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      "manual_tags": [],
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    },
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1553_delete_not_between_int.py",
      "description": "DELETE WHERE score NOT BETWEEN X AND Y. Tests the NOT BETWEEN",
      "status": "pass",
      "duration_ms": 1270,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:14.402647+00:00",
      "read_cold_ms": 697,
      "read_warm_ms": 324,
      "skip_feature": null,
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      "write_cold_ms": 88,
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        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
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      "reader": "spark",
      "write_script": "generator/df-sql/1554_update_not_between_int.sql",
      "read_script": "generator/spark-reads-df/verify_1554_update_not_between_int.py",
      "description": "UPDATE WHERE score NOT BETWEEN X AND Y. Tests NOT BETWEEN",
      "status": "pass",
      "duration_ms": 1133,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-19T17:46:15.535885+00:00",
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        "dml:insert",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1555_delete_not_between_decimal",
      "num": 1555,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1555_delete_not_between_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1555_delete_not_between_decimal.py",
      "description": "DELETE WHERE DECIMAL NOT BETWEEN. Tests NOT BETWEEN predicate",
      "status": "pass",
      "duration_ms": 1407,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:16.943350+00:00",
      "read_cold_ms": 802,
      "read_warm_ms": 255,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1556_update_not_between_decimal.py",
      "description": "UPDATE WHERE DECIMAL NOT BETWEEN. Tests NOT BETWEEN predicate",
      "status": "pass",
      "duration_ms": 1292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:18.236297+00:00",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
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      "id": "df-writes/delta/1557_delete_between_timestamp",
      "num": 1557,
      "name": "delete_between_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1557_delete_between_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1557_delete_between_timestamp.py",
      "description": "DELETE WHERE TIMESTAMP BETWEEN range. Tests BETWEEN predicate",
      "status": "pass",
      "duration_ms": 1324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:19.560679+00:00",
      "read_cold_ms": 760,
      "read_warm_ms": 199,
      "skip_feature": null,
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      "write_cold_ms": 31,
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        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1558_update_between_timestamp",
      "num": 1558,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1558_update_between_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1558_update_between_timestamp.py",
      "description": "UPDATE WHERE TIMESTAMP BETWEEN range. Tests BETWEEN predicate",
      "status": "pass",
      "duration_ms": 1408,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:20.969499+00:00",
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      "skip_feature": null,
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        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1559_delete_not_between_timestamp",
      "num": 1559,
      "name": "delete_not_between_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1559_delete_not_between_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1559_delete_not_between_timestamp.py",
      "description": "DELETE WHERE TIMESTAMP NOT BETWEEN. Tests NOT BETWEEN predicate",
      "status": "pass",
      "duration_ms": 1183,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:22.153147+00:00",
      "read_cold_ms": 663,
      "read_warm_ms": 188,
      "skip_feature": null,
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      "write_cold_ms": 31,
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      "tags": [
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        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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      "num": 155,
      "name": "vacuum_race",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/155_vacuum_race.sql",
      "read_script": "generator/spark-reads-df/verify_155_vacuum_race.py",
      "description": "- Vacuum race condition handling test table - UPDATE operations that create orphaned files - Deletion vectors enabled - Modulo operator in UPDATE predicates",
      "status": "pass",
      "duration_ms": 3311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:41:56.585724+00:00",
      "read_cold_ms": 2262,
      "read_warm_ms": 365,
      "skip_feature": null,
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      "write_cold_ms": 1298,
      "write_warm_ms": 830,
      "tags": [
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        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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      "num": 1560,
      "name": "delete_between_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1560_delete_between_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1560_delete_between_decimal.py",
      "description": "DELETE WHERE DECIMAL BETWEEN (straightforward version).",
      "status": "pass",
      "duration_ms": 1280,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:23.433750+00:00",
      "read_cold_ms": 713,
      "read_warm_ms": 317,
      "skip_feature": null,
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      "write_cold_ms": 28,
      "write_warm_ms": 131,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1561_update_power",
      "num": 1561,
      "name": "update_power",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1561_update_power.sql",
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      "status": "pass",
      "duration_ms": 1316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:24.750207+00:00",
      "read_cold_ms": 809,
      "read_warm_ms": 221,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 553,
      "write_warm_ms": 209,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1562_update_sqrt",
      "num": 1562,
      "name": "update_sqrt",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1562_update_sqrt.sql",
      "read_script": "generator/spark-reads-df/verify_1562_update_sqrt.py",
      "description": "UPDATE SET with SQRT function. Computes square roots of",
      "status": "pass",
      "duration_ms": 1011,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:25.762333+00:00",
      "read_cold_ms": 603,
      "read_warm_ms": 224,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 159,
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        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1563_update_sign",
      "num": 1563,
      "name": "update_sign",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1563_update_sign.sql",
      "read_script": "generator/spark-reads-df/verify_1563_update_sign.py",
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      "status": "pass",
      "duration_ms": 1425,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:27.187524+00:00",
      "read_cold_ms": 877,
      "read_warm_ms": 345,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 163,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1564_update_mod",
      "num": 1564,
      "name": "update_mod",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1564_update_mod.sql",
      "read_script": "generator/spark-reads-df/verify_1564_update_mod.py",
      "description": "UPDATE SET with modulo (% operator). Computes remainders",
      "status": "pass",
      "duration_ms": 1426,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:28.614102+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 385,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
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        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1565_update_upper_lower",
      "num": 1565,
      "name": "update_upper_lower",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1565_update_upper_lower.sql",
      "read_script": "generator/spark-reads-df/verify_1565_update_upper_lower.py",
      "description": "UPDATE SET with UPPER and LOWER string functions.",
      "status": "pass",
      "duration_ms": 1245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:29.859733+00:00",
      "read_cold_ms": 703,
      "read_warm_ms": 329,
      "skip_feature": null,
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      "write_cold_ms": 62,
      "write_warm_ms": 161,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1566_update_lpad_rpad",
      "num": 1566,
      "name": "update_lpad_rpad",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1566_update_lpad_rpad.py",
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      "duration_ms": 1094,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:30.954888+00:00",
      "read_cold_ms": 638,
      "read_warm_ms": 199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 148,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1567_update_reverse",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1567_update_reverse.sql",
      "read_script": "generator/spark-reads-df/verify_1567_update_reverse.py",
      "description": "UPDATE SET with REVERSE string function.",
      "status": "pass",
      "duration_ms": 1286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:32.241739+00:00",
      "read_cold_ms": 770,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1568_update_math_combo",
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      "reader": "spark",
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      "duration_ms": 1151,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:33.393580+00:00",
      "read_cold_ms": 687,
      "read_warm_ms": 222,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
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        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 1144,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:34.538663+00:00",
      "read_cold_ms": 696,
      "read_warm_ms": 226,
      "skip_feature": null,
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      "write_cold_ms": 138,
      "write_warm_ms": 65,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 3450,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:00.036826+00:00",
      "read_cold_ms": 1886,
      "read_warm_ms": 815,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "status": "pass",
      "duration_ms": 1150,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:35.689533+00:00",
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      "read_warm_ms": 215,
      "skip_feature": null,
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      "write_cold_ms": 145,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1571_delete_where_sqrt",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1571_delete_where_sqrt.py",
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      "duration_ms": 1126,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:36.816703+00:00",
      "read_cold_ms": 660,
      "read_warm_ms": 248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1572_merge_power_sqrt",
      "num": 1572,
      "name": "merge_power_sqrt",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1572_merge_power_sqrt.py",
      "description": "MERGE with POWER and SQRT in UPDATE SET clause. Tests math",
      "status": "pass",
      "duration_ms": 1131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:37.948516+00:00",
      "read_cold_ms": 662,
      "read_warm_ms": 196,
      "skip_feature": null,
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      "write_cold_ms": 125,
      "write_warm_ms": 75,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1573_update_sign_decimal",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "SIGN function on DECIMAL column. Tests SIGN applied to",
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      "duration_ms": 1207,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 734,
      "read_warm_ms": 213,
      "skip_feature": null,
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      "write_cold_ms": 100,
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      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/1574_functions_partition_cdc",
      "num": 1574,
      "name": "functions_partition_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1574_functions_partition_cdc.sql",
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      "status": "pass",
      "duration_ms": 1266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:40.423281+00:00",
      "read_cold_ms": 688,
      "read_warm_ms": 245,
      "skip_feature": null,
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      "write_cold_ms": 94,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1575_functions_negation_combo.py",
      "description": "Negation predicates combined with math/string functions in",
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      "duration_ms": 1374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:41.798389+00:00",
      "read_cold_ms": 753,
      "read_warm_ms": 362,
      "skip_feature": null,
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      "write_cold_ms": 259,
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      "tags": [
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 1576,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1576_overwrite_then_zorder.sql",
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      "description": "INSERT OVERWRITE then OPTIMIZE ZORDER BY.",
      "status": "pass",
      "duration_ms": 1100,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:42.898964+00:00",
      "read_cold_ms": 602,
      "read_warm_ms": 143,
      "skip_feature": null,
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      "write_cold_ms": 319,
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      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1577_overwrite_then_vacuum",
      "num": 1577,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1577_overwrite_then_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1577_overwrite_then_vacuum.py",
      "description": "INSERT OVERWRITE then OPTIMIZE then VACUUM.",
      "status": "pass",
      "duration_ms": 1257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:44.156211+00:00",
      "read_cold_ms": 658,
      "read_warm_ms": 205,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 530,
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      "tags": [
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1578_overwrite_after_merge",
      "num": 1578,
      "name": "overwrite_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1578_overwrite_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1578_overwrite_after_merge.py",
      "description": "MERGE then INSERT OVERWRITE. The OVERWRITE replaces all",
      "status": "pass",
      "duration_ms": 1380,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:45.536596+00:00",
      "read_cold_ms": 795,
      "read_warm_ms": 231,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 264,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1579_overwrite_restore",
      "num": 1579,
      "name": "overwrite_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1579_overwrite_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1579_overwrite_restore.py",
      "description": "INSERT OVERWRITE then RESTORE to pre-OVERWRITE version.",
      "status": "pass",
      "duration_ms": 1183,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:46.720326+00:00",
      "read_cold_ms": 754,
      "read_warm_ms": 147,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/157_occ_conflict",
      "num": 157,
      "name": "occ_conflict",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/157_occ_conflict.sql",
      "read_script": "generator/spark-reads-df/verify_157_occ_conflict.py",
      "description": "Optimistic Concurrency Control conflict handling test table",
      "status": "pass",
      "duration_ms": 2265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:02.303018+00:00",
      "read_cold_ms": 1407,
      "read_warm_ms": 267,
      "skip_feature": null,
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      "write_cold_ms": 67,
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      "tags": [
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        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1580_overwrite_typed_vacuum",
      "num": 1580,
      "name": "overwrite_typed_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1580_overwrite_typed_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1580_overwrite_typed_vacuum.py",
      "description": "INSERT OVERWRITE with DECIMAL+TIMESTAMP then VACUUM.",
      "status": "pass",
      "duration_ms": 912,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:47.633164+00:00",
      "read_cold_ms": 537,
      "read_warm_ms": 124,
      "skip_feature": null,
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      "write_cold_ms": 57,
      "write_warm_ms": 250,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1581_functions_in_merge_nmbys",
      "num": 1581,
      "name": "functions_in_merge_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1581_functions_in_merge_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_1581_functions_in_merge_nmbys.py",
      "description": "Functions in NOT MATCHED BY SOURCE UPDATE SET clause.",
      "status": "pass",
      "duration_ms": 1141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:48.775302+00:00",
      "read_cold_ms": 694,
      "read_warm_ms": 206,
      "skip_feature": null,
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      "write_cold_ms": 172,
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      "tags": [
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1582_functions_in_merge_delete",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1582_functions_in_merge_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1582_functions_in_merge_delete.py",
      "description": "Functions in MERGE DELETE condition.",
      "status": "pass",
      "duration_ms": 1416,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:46:50.191724+00:00",
      "read_cold_ms": 747,
      "read_warm_ms": 330,
      "skip_feature": null,
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      "write_cold_ms": 181,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "reader": "spark",
      "write_script": "generator/df-sql/15_action_add_file_with_stats.sql",
      "read_script": "generator/spark-reads-df/verify_15_action_add_file_with_stats.py",
      "description": "Validates the Delta table written by DeltaForge for test 15. Sales records table with 18 columns. sale_quarter, is_large_order, discount_applied, net_total.",
      "status": "pass",
      "duration_ms": 5140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:13.847187+00:00",
      "read_cold_ms": 2045,
      "read_warm_ms": 739,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 88,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1600_stress_all_features",
      "num": 1600,
      "name": "stress_all_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1600_stress_all_features.sql",
      "read_script": "generator/spark-reads-df/verify_1600_stress_all_features.py",
      "description": "Maximum stress test combining CDC, column mapping, partitions,",
      "status": "pass",
      "duration_ms": 1629,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:19.822884+00:00",
      "read_cold_ms": 873,
      "read_warm_ms": 227,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1470,
      "write_warm_ms": 2035,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1601_not_in_decimal",
      "num": 1601,
      "name": "not_in_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1601_not_in_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1601_not_in_decimal.py",
      "description": "DELETE WHERE amount NOT IN (specific DECIMAL values).",
      "status": "pass",
      "duration_ms": 1373,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:21.196301+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 285,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 249,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1602_not_between_decimal_cdc",
      "num": 1602,
      "name": "not_between_decimal_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1602_not_between_decimal_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1602_not_between_decimal_cdc.py",
      "description": "NOT BETWEEN DECIMAL + CDC.",
      "status": "pass",
      "duration_ms": 1349,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:22.545814+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 231,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 48,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1603_between_timestamp_cdc",
      "num": 1603,
      "name": "between_timestamp_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1603_between_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1603_between_timestamp_cdc.py",
      "description": "Removes rows i=1..50 => 50 rows deleted.",
      "status": "pass",
      "duration_ms": 1461,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:24.007043+00:00",
      "read_cold_ms": 852,
      "read_warm_ms": 220,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 130,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1604_negation_colmap",
      "num": 1604,
      "name": "negation_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1604_negation_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1604_negation_colmap.py",
      "description": "NOT BETWEEN + column mapping (name mode).",
      "status": "pass",
      "duration_ms": 1608,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:25.615316+00:00",
      "read_cold_ms": 946,
      "read_warm_ms": 261,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 190,
      "write_warm_ms": 104,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1605_negation_constraint",
      "num": 1605,
      "name": "negation_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1605_negation_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1605_negation_constraint.py",
      "description": "NOT BETWEEN + constraint. Constraint remains valid after delete.",
      "status": "pass",
      "duration_ms": 2102,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:27.717618+00:00",
      "read_cold_ms": 1124,
      "read_warm_ms": 261,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 186,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1606_negation_evolve",
      "num": 1606,
      "name": "negation_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1606_negation_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1606_negation_evolve.py",
      "description": "NOT BETWEEN + schema evolution.",
      "status": "pass",
      "duration_ms": 1573,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:29.290901+00:00",
      "read_cold_ms": 1017,
      "read_warm_ms": 206,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 218,
      "write_warm_ms": 314,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1607_between_decimal_partition",
      "num": 1607,
      "name": "between_decimal_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1607_between_decimal_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1607_between_decimal_partition.py",
      "description": "BETWEEN DECIMAL + partition.",
      "status": "pass",
      "duration_ms": 1278,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:30.569995+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 289,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 144,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1608_functions_not_between",
      "num": 1608,
      "name": "functions_not_between",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1608_functions_not_between.sql",
      "read_script": "generator/spark-reads-df/verify_1608_functions_not_between.py",
      "description": "ABS + NOT BETWEEN combo.",
      "status": "pass",
      "duration_ms": 1179,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:31.749502+00:00",
      "read_cold_ms": 712,
      "read_warm_ms": 239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 200,
      "write_warm_ms": 69,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1609_functions_between_decimal",
      "num": 1609,
      "name": "functions_between_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1609_functions_between_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1609_functions_between_decimal.py",
      "description": "COALESCE + BETWEEN DECIMAL combo.",
      "status": "pass",
      "duration_ms": 1318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:33.067742+00:00",
      "read_cold_ms": 819,
      "read_warm_ms": 200,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 51,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/160_symlink_manifest",
      "num": 160,
      "name": "symlink_manifest",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/160_symlink_manifest.sql",
      "read_script": "generator/spark-reads-df/verify_160_symlink_manifest.py",
      "description": "Partitioned table with deletion vectors enabled for symlink manifest testing",
      "status": "pass",
      "duration_ms": 3871,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:17.719393+00:00",
      "read_cold_ms": 2208,
      "read_warm_ms": 1084,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1610_power_sqrt_partition",
      "num": 1610,
      "name": "power_sqrt_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1610_power_sqrt_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1610_power_sqrt_partition.py",
      "description": "Effectively ABS(score) as DOUBLE for US rows.",
      "status": "pass",
      "duration_ms": 1152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:34.220540+00:00",
      "read_cold_ms": 692,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 295,
      "write_warm_ms": 186,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1611_sign_decimal_cdc",
      "num": 1611,
      "name": "sign_decimal_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1611_sign_decimal_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1611_sign_decimal_cdc.py",
      "description": "SIGN(DECIMAL) + CDC.",
      "status": "pass",
      "duration_ms": 1325,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:35.546642+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 86,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1612_reverse_upper_merge",
      "num": 1612,
      "name": "reverse_upper_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1612_reverse_upper_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1612_reverse_upper_merge.py",
      "description": "REVERSE + UPPER in MERGE UPDATE SET.",
      "status": "pass",
      "duration_ms": 1471,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:37.018152+00:00",
      "read_cold_ms": 848,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 158,
      "write_warm_ms": 112,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1613_lpad_rpad_partition",
      "num": 1613,
      "name": "lpad_rpad_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1613_lpad_rpad_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1613_lpad_rpad_partition.py",
      "description": "LPAD/RPAD + partition.",
      "status": "pass",
      "duration_ms": 1326,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:38.344880+00:00",
      "read_cold_ms": 768,
      "read_warm_ms": 304,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 234,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1614_functions_timestamp",
      "num": 1614,
      "name": "functions_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1614_functions_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1614_functions_timestamp.py",
      "description": "Functions on rows that include TIMESTAMP columns.",
      "status": "pass",
      "duration_ms": 1283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:39.628356+00:00",
      "read_cold_ms": 741,
      "read_warm_ms": 273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1615_not_in_cdc_partition",
      "num": 1615,
      "name": "not_in_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1615_not_in_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1615_not_in_cdc_partition.py",
      "description": "NOT IN + CDC + partition. Three-way combo.",
      "status": "pass",
      "duration_ms": 1262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:40.890667+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 221,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1616_between_decimal_colmap",
      "num": 1616,
      "name": "between_decimal_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1616_between_decimal_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1616_between_decimal_colmap.py",
      "description": "BETWEEN DECIMAL + column mapping (name mode).",
      "status": "pass",
      "duration_ms": 1229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:47:42.120045+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 185,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 25,
      "write_warm_ms": 38,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
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      "manual_tags": [],
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        "dml:insert",
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      "duration_ms": 3703,
      "error": null,
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        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1651_partdir_string_basic",
      "num": 1651,
      "name": "partdir_string_basic",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1651_partdir_string_basic.py",
      "description": "Basic STRING partition column with simple ASCII values.",
      "status": "pass",
      "duration_ms": 4214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:48:48.642281+00:00",
      "read_cold_ms": 1612,
      "read_warm_ms": 628,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1652_partdir_string_spaces",
      "num": 1652,
      "name": "partdir_string_spaces",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1652_partdir_string_spaces.sql",
      "read_script": "generator/spark-reads-df/verify_1652_partdir_string_spaces.py",
      "description": "STRING partition with spaces in values.",
      "status": "pass",
      "duration_ms": 2197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:48:50.841632+00:00",
      "read_cold_ms": 1110,
      "read_warm_ms": 186,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 236,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1653_partdir_string_special",
      "num": 1653,
      "name": "partdir_string_special",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1653_partdir_string_special.py",
      "description": "STRING partition with special characters: slash, equals,",
      "status": "pass",
      "duration_ms": 1638,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:48:52.480368+00:00",
      "read_cold_ms": 584,
      "read_warm_ms": 150,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 195,
      "write_warm_ms": 194,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1654_partdir_string_empty",
      "num": 1654,
      "name": "partdir_string_empty",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1654_partdir_string_empty.py",
      "description": "STRING partition with empty string value.",
      "status": "pass",
      "duration_ms": 1962,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:48:54.443407+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 212,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1655_partdir_string_null",
      "num": 1655,
      "name": "partdir_string_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1655_partdir_string_null.sql",
      "read_script": "generator/spark-reads-df/verify_1655_partdir_string_null.py",
      "description": "STRING partition with NULL value.",
      "status": "pass",
      "duration_ms": 2141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:48:56.584934+00:00",
      "read_cold_ms": 679,
      "read_warm_ms": 176,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 93,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1656_partdir_string_unicode",
      "num": 1656,
      "name": "partdir_string_unicode",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1656_partdir_string_unicode.py",
      "description": "STRING partition with characters that require URL encoding:",
      "status": "pass",
      "duration_ms": 4447,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:01.032673+00:00",
      "read_cold_ms": 1622,
      "read_warm_ms": 473,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 286,
      "write_warm_ms": 330,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1657_partdir_int_basic",
      "num": 1657,
      "name": "partdir_int_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1657_partdir_int_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1657_partdir_int_basic.py",
      "description": "Basic INT partition with 4 values (0, 1, 2, 3).",
      "status": "pass",
      "duration_ms": 4256,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:05.289551+00:00",
      "read_cold_ms": 1463,
      "read_warm_ms": 516,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 46,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1658_partdir_int_negative",
      "num": 1658,
      "name": "partdir_int_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1658_partdir_int_negative.sql",
      "read_script": "generator/spark-reads-df/verify_1658_partdir_int_negative.py",
      "description": "INT partition with negative values.",
      "status": "pass",
      "duration_ms": 5257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:10.549262+00:00",
      "read_cold_ms": 1537,
      "read_warm_ms": 503,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 163,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1659_partdir_int_large",
      "num": 1659,
      "name": "partdir_int_large",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1659_partdir_int_large.sql",
      "read_script": "generator/spark-reads-df/verify_1659_partdir_int_large.py",
      "description": "INT partition with large values (millions).",
      "status": "pass",
      "duration_ms": 4767,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:15.318161+00:00",
      "read_cold_ms": 2032,
      "read_warm_ms": 533,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/165_domain_metadata",
      "num": 165,
      "name": "domain_metadata",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/165_domain_metadata.sql",
      "read_script": "generator/spark-reads-df/verify_165_domain_metadata.py",
      "description": "- Domain metadata custom properties - Deletion vectors enabled - Custom domain properties for data governance",
      "status": "pass",
      "duration_ms": 3294,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:34.619907+00:00",
      "read_cold_ms": 1697,
      "read_warm_ms": 861,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 62,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1660_partdir_int_null",
      "num": 1660,
      "name": "partdir_int_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1660_partdir_int_null.sql",
      "read_script": "generator/spark-reads-df/verify_1660_partdir_int_null.py",
      "description": "INT partition with NULL values.",
      "status": "pass",
      "duration_ms": 7924,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:23.243580+00:00",
      "read_cold_ms": 1483,
      "read_warm_ms": 392,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1661_partdir_int_zero",
      "num": 1661,
      "name": "partdir_int_zero",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1661_partdir_int_zero.sql",
      "read_script": "generator/spark-reads-df/verify_1661_partdir_int_zero.py",
      "description": "INT partition where one value is 0.",
      "status": "pass",
      "duration_ms": 6838,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:30.082323+00:00",
      "read_cold_ms": 3243,
      "read_warm_ms": 925,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1662_partdir_int_dml",
      "num": 1662,
      "name": "partdir_int_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1662_partdir_int_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1662_partdir_int_dml.py",
      "description": "INT partition with UPDATE, DELETE, and MERGE targeting",
      "status": "pass",
      "duration_ms": 6867,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:36.950182+00:00",
      "read_cold_ms": 1978,
      "read_warm_ms": 617,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 285,
      "write_warm_ms": 124,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1663_partdir_boolean_basic",
      "num": 1663,
      "name": "partdir_boolean_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1663_partdir_boolean_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1663_partdir_boolean_basic.py",
      "description": "BOOLEAN partition with true/false values.",
      "status": "pass",
      "duration_ms": 3101,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:40.052722+00:00",
      "read_cold_ms": 1455,
      "read_warm_ms": 332,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 48,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1664_partdir_boolean_null",
      "num": 1664,
      "name": "partdir_boolean_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1664_partdir_boolean_null.sql",
      "read_script": "generator/spark-reads-df/verify_1664_partdir_boolean_null.py",
      "description": "BOOLEAN partition with NULL values.",
      "status": "pass",
      "duration_ms": 3506,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:43.559271+00:00",
      "read_cold_ms": 1561,
      "read_warm_ms": 377,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 46,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1665_partdir_boolean_dml",
      "num": 1665,
      "name": "partdir_boolean_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1665_partdir_boolean_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1665_partdir_boolean_dml.py",
      "description": "BOOLEAN partition with DML operations.",
      "status": "pass",
      "duration_ms": 4972,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:48.533336+00:00",
      "read_cold_ms": 1683,
      "read_warm_ms": 730,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 191,
      "write_warm_ms": 122,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1666_partdir_decimal_basic",
      "num": 1666,
      "name": "partdir_decimal_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1666_partdir_decimal_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1666_partdir_decimal_basic.py",
      "description": "DECIMAL(5,2) partition with known values.",
      "status": "pass",
      "duration_ms": 4039,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:52.573636+00:00",
      "read_cold_ms": 1622,
      "read_warm_ms": 377,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 154,
      "write_warm_ms": 71,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1667_partdir_decimal_negative",
      "num": 1667,
      "name": "partdir_decimal_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1667_partdir_decimal_negative.sql",
      "read_script": "generator/spark-reads-df/verify_1667_partdir_decimal_negative.py",
      "description": "DECIMAL(8,2) partition with negative values.",
      "status": "pass",
      "duration_ms": 3939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:49:56.514378+00:00",
      "read_cold_ms": 1328,
      "read_warm_ms": 339,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 35,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1668_partdir_decimal_zero",
      "num": 1668,
      "name": "partdir_decimal_zero",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1668_partdir_decimal_zero.sql",
      "read_script": "generator/spark-reads-df/verify_1668_partdir_decimal_zero.py",
      "description": "DECIMAL(6,2) partition with exact 0.00.",
      "status": "pass",
      "duration_ms": 3895,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:50:00.412550+00:00",
      "read_cold_ms": 1533,
      "read_warm_ms": 551,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 172,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1669_partdir_decimal_precision",
      "num": 1669,
      "name": "partdir_decimal_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1669_partdir_decimal_precision.sql",
      "read_script": "generator/spark-reads-df/verify_1669_partdir_decimal_precision.py",
      "description": "DECIMAL(10,4) partition to test precision in dir names.",
      "status": "pass",
      "duration_ms": 3915,
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        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
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    },
    {
      "id": "df-writes/delta/166_ecosystem_torture",
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      "status": "pass",
      "duration_ms": 4961,
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      "verified_at": "2026-05-20T00:42:39.582327+00:00",
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        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
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        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1670_partdir_decimal_dml",
      "num": 1670,
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 5896,
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      "notes": null,
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      "verified_at": "2026-05-19T17:50:10.226585+00:00",
      "read_cold_ms": 1729,
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      "skip_feature": null,
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        "type:decimal",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1671_partdir_date_basic",
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      "reader": "spark",
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      "duration_ms": 4649,
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        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1672_partdir_date_null",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4899,
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      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:50:19.777689+00:00",
      "read_cold_ms": 1339,
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      "skip_feature": null,
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        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1673_partdir_date_dml",
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      "reader": "spark",
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      "description": "DATE partition with DML per date partition.",
      "status": "pass",
      "duration_ms": 6214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:50:25.992888+00:00",
      "read_cold_ms": 1975,
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        "type:date",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1674_partdir_date_many",
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      "writer": "delta-forge",
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      "error": null,
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      "read_cold_ms": 1242,
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        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1675_partdir_two_string",
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      "reader": "spark",
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        "type:floating",
        "type:integer",
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        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1676_partdir_int_string",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 5651,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:50:47.383690+00:00",
      "read_cold_ms": 1876,
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        "type:integer",
        "type:string",
        "dml:insert",
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        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1677_partdir_boolean_int",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 5315,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-19T17:50:52.700458+00:00",
      "read_cold_ms": 1238,
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        "type:boolean",
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        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/1678_partdir_three_cols",
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      "reader": "spark",
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      "duration_ms": 8964,
      "error": null,
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        "type:integer",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1679_partdir_string_cdc",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 5366,
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      "verified_at": "2026-05-19T17:51:07.033598+00:00",
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        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
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        "delta:change-data-feed",
        "delta:deletion-vectors",
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      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/167_ecosystem_torture_interop",
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      "reader": "spark",
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      "description": "- Ultimate interoperability stress test with multiple features - Column Mapping (name mode) - Deletion Vectors enabled - Partitioning by region - Multiple data types including DECIMAL, FLOAT, BOOLEAN, TIMESTAMP - UPDATE and DELETE operations",
      "status": "pass",
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      "verified_at": "2026-05-20T00:42:44.173179+00:00",
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        "type:boolean",
        "type:decimal",
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        "delta:column-mapping",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1680_partdir_decimal_colmap",
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      "reader": "spark",
      "write_script": "generator/df-sql/1680_partdir_decimal_colmap.sql",
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      "duration_ms": 5262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:12.296755+00:00",
      "read_cold_ms": 1476,
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      "skip_feature": null,
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        "dml:delete",
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        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1681_stats_int_range_per_file",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1681_stats_int_range_per_file.sql",
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      "description": "3 INSERT batches with disjoint INT ranges per file.",
      "status": "pass",
      "duration_ms": 2475,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:14.772259+00:00",
      "read_cold_ms": 1601,
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      "skip_feature": null,
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        "dml:insert",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1682_stats_decimal_range_per_file",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1682_stats_decimal_range_per_file.sql",
      "read_script": "generator/spark-reads-df/verify_1682_stats_decimal_range_per_file.py",
      "description": "3 INSERT batches with disjoint DECIMAL(10,2) ranges.",
      "status": "pass",
      "duration_ms": 1922,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:16.695080+00:00",
      "read_cold_ms": 1166,
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1683_stats_timestamp_range_per_file",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1683_stats_timestamp_range_per_file.sql",
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      "description": "3 INSERT batches with disjoint TIMESTAMP ranges.",
      "status": "pass",
      "duration_ms": 2184,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:18.880299+00:00",
      "read_cold_ms": 1355,
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      "skip_feature": null,
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      "tags": [
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        "type:string",
        "type:timestamp",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1684_stats_string_range_per_file",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1684_stats_string_range_per_file.sql",
      "read_script": "generator/spark-reads-df/verify_1684_stats_string_range_per_file.py",
      "description": "3 INSERT batches with disjoint STRING prefix ranges.",
      "status": "pass",
      "duration_ms": 2245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:21.126122+00:00",
      "read_cold_ms": 1414,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1685_stats_boolean_per_file",
      "num": 1685,
      "name": "stats_boolean_per_file",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1685_stats_boolean_per_file.py",
      "description": "2 INSERT batches: one all-true, one all-false.",
      "status": "pass",
      "duration_ms": 2275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:23.401867+00:00",
      "read_cold_ms": 1448,
      "read_warm_ms": 386,
      "skip_feature": null,
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        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/1686_stats_double_range_per_file",
      "num": 1686,
      "name": "stats_double_range_per_file",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1686_stats_double_range_per_file.sql",
      "read_script": "generator/spark-reads-df/verify_1686_stats_double_range_per_file.py",
      "description": "3 INSERT batches with disjoint DOUBLE ranges.",
      "status": "pass",
      "duration_ms": 1872,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:25.274556+00:00",
      "read_cold_ms": 1084,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 87,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1687_stats_null_count_per_file",
      "num": 1687,
      "name": "stats_null_count_per_file",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1687_stats_null_count_per_file.sql",
      "read_script": "generator/spark-reads-df/verify_1687_stats_null_count_per_file.py",
      "description": "3 INSERT batches with varying NULL counts.",
      "status": "pass",
      "duration_ms": 1892,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:27.166953+00:00",
      "read_cold_ms": 1199,
      "read_warm_ms": 306,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 290,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1688_stats_after_update_min",
      "num": 1688,
      "name": "stats_after_update_min",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1688_stats_after_update_min.sql",
      "read_script": "generator/spark-reads-df/verify_1688_stats_after_update_min.py",
      "description": "2 INSERT batches, UPDATE raises the minimum in batch 1.",
      "status": "pass",
      "duration_ms": 6681,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:33.848967+00:00",
      "read_cold_ms": 5445,
      "read_warm_ms": 514,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 320,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1689_stats_after_update_max",
      "num": 1689,
      "name": "stats_after_update_max",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1689_stats_after_update_max.sql",
      "read_script": "generator/spark-reads-df/verify_1689_stats_after_update_max.py",
      "description": "2 INSERT batches, UPDATE lowers the maximum in batch 1.",
      "status": "pass",
      "duration_ms": 2971,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:36.822148+00:00",
      "read_cold_ms": 1696,
      "read_warm_ms": 687,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 326,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/168_dbx_insert_into_deltaflow",
      "num": 168,
      "name": "dbx_insert_into_deltaflow",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/168_dbx_insert_into_deltaflow.sql",
      "read_script": "generator/spark-reads-df/verify_168_dbx_insert_into_deltaflow.py",
      "description": "- Reverse interop: DBX INSERT into DeltaForge table - Simple product catalog schema - No partitioning",
      "status": "pass",
      "duration_ms": 1812,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:45.986330+00:00",
      "read_cold_ms": 1492,
      "read_warm_ms": 163,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 39,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1690_stats_after_delete_via_dv",
      "num": 1690,
      "name": "stats_after_delete_via_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1690_stats_after_delete_via_dv.sql",
      "read_script": "generator/spark-reads-df/verify_1690_stats_after_delete_via_dv.py",
      "description": "2 INSERT batches, DELETE max row via DV. Stats may be stale.",
      "status": "pass",
      "duration_ms": 2759,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:39.582074+00:00",
      "read_cold_ms": 1591,
      "read_warm_ms": 479,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 194,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1691_stats_after_optimize",
      "num": 1691,
      "name": "stats_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1691_stats_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_1691_stats_after_optimize.py",
      "description": "4 INSERT batches with overlapping ranges, then OPTIMIZE.",
      "status": "pass",
      "duration_ms": 2124,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:41.706972+00:00",
      "read_cold_ms": 1233,
      "read_warm_ms": 431,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 323,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1692_stats_after_merge",
      "num": 1692,
      "name": "stats_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1692_stats_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1692_stats_after_merge.py",
      "description": "2 INSERT batches, MERGE rewrites batch 1 scores to new range.",
      "status": "pass",
      "duration_ms": 3181,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:44.888621+00:00",
      "read_cold_ms": 1654,
      "read_warm_ms": 759,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
      "write_warm_ms": 172,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1693_stats_after_schema_evolve",
      "num": 1693,
      "name": "stats_after_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1693_stats_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_1693_stats_after_schema_evolve.py",
      "description": "2 INSERT batches, ADD COLUMN, 1 more batch.",
      "status": "pass",
      "duration_ms": 2074,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:46.963509+00:00",
      "read_cold_ms": 1204,
      "read_warm_ms": 309,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 212,
      "write_warm_ms": 124,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1694_stats_decimal_precision",
      "num": 1694,
      "name": "stats_decimal_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1694_stats_decimal_precision.sql",
      "read_script": "generator/spark-reads-df/verify_1694_stats_decimal_precision.py",
      "description": "2 INSERT batches with DECIMAL values differing at 4th decimal place.",
      "status": "pass",
      "duration_ms": 2048,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:49.013274+00:00",
      "read_cold_ms": 1352,
      "read_warm_ms": 333,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 123,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1695_stats_negative_int",
      "num": 1695,
      "name": "stats_negative_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1695_stats_negative_int.sql",
      "read_script": "generator/spark-reads-df/verify_1695_stats_negative_int.py",
      "description": "2 INSERT batches: negative range and positive range.",
      "status": "pass",
      "duration_ms": 2190,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:51.203921+00:00",
      "read_cold_ms": 1378,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1696_stats_negative_decimal",
      "num": 1696,
      "name": "stats_negative_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1696_stats_negative_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1696_stats_negative_decimal.py",
      "description": "2 INSERT batches: negative and positive DECIMAL.",
      "status": "pass",
      "duration_ms": 2516,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:53.720510+00:00",
      "read_cold_ms": 1387,
      "read_warm_ms": 502,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 63,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1697_stats_all_same_int",
      "num": 1697,
      "name": "stats_all_same_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1697_stats_all_same_int.sql",
      "read_script": "generator/spark-reads-df/verify_1697_stats_all_same_int.py",
      "description": "2 INSERT batches: batch1 all score=42, batch2 all score=99.",
      "status": "pass",
      "duration_ms": 1952,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:55.673488+00:00",
      "read_cold_ms": 1325,
      "read_warm_ms": 292,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1698_stats_all_null",
      "num": 1698,
      "name": "stats_all_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1698_stats_all_null.sql",
      "read_script": "generator/spark-reads-df/verify_1698_stats_all_null.py",
      "description": "2 INSERT batches: batch1 non-NULL score, batch2 all NULL score.",
      "status": "pass",
      "duration_ms": 2227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:51:57.901615+00:00",
      "read_cold_ms": 1561,
      "read_warm_ms": 323,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1699_stats_wide_range",
      "num": 1699,
      "name": "stats_wide_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1699_stats_wide_range.sql",
      "read_script": "generator/spark-reads-df/verify_1699_stats_wide_range.py",
      "description": "2 INSERT batches with BIGINT at extreme ranges.",
      "status": "pass",
      "duration_ms": 2541,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:00.444260+00:00",
      "read_cold_ms": 1494,
      "read_warm_ms": 487,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 84,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/169_dbx_optimize_deltaflow",
      "num": 169,
      "name": "dbx_optimize_deltaflow",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/169_dbx_optimize_deltaflow.sql",
      "read_script": "generator/spark-reads-df/verify_169_dbx_optimize_deltaflow.py",
      "description": "- Creating file fragmentation with multiple INSERT statements - Multiple small batches for OPTIMIZE testing - No partitioning",
      "status": "pass",
      "duration_ms": 2280,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:48.267732+00:00",
      "read_cold_ms": 1911,
      "read_warm_ms": 175,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 942,
      "write_warm_ms": 909,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/16_action_remove_file_with_tombstone",
      "num": 16,
      "name": "action_remove_file_with_tombstone",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/16_action_remove_file_with_tombstone.sql",
      "read_script": "generator/spark-reads-df/verify_16_action_remove_file_with_tombstone.py",
      "description": "Remove actions track deleted files for audit trail while respecting the right to be forgotten. Tombstones ensure proper cleanup of user data.",
      "status": "pass",
      "duration_ms": 3660,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:51.928772+00:00",
      "read_cold_ms": 1797,
      "read_warm_ms": 606,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 366,
      "write_warm_ms": 274,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1700_stats_after_zorder",
      "num": 1700,
      "name": "stats_after_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1700_stats_after_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1700_stats_after_zorder.py",
      "description": "4 INSERT batches then Z-ORDER BY (score).",
      "status": "pass",
      "duration_ms": 2355,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:02.800030+00:00",
      "read_cold_ms": 1427,
      "read_warm_ms": 402,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 181,
      "write_warm_ms": 236,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1701_stats_after_vacuum",
      "num": 1701,
      "name": "stats_after_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1701_stats_after_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1701_stats_after_vacuum.py",
      "description": "4 INSERT batches, OPTIMIZE, VACUUM 0 HOURS.",
      "status": "pass",
      "duration_ms": 2114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:04.915486+00:00",
      "read_cold_ms": 1439,
      "read_warm_ms": 326,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 481,
      "write_warm_ms": 159,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1702_stats_partition",
      "num": 1702,
      "name": "stats_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1702_stats_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1702_stats_partition.py",
      "description": "Partitioned table with 2 batches per partition.",
      "status": "pass",
      "duration_ms": 2202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:07.118750+00:00",
      "read_cold_ms": 1399,
      "read_warm_ms": 408,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 289,
      "write_warm_ms": 282,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1703_stats_cdc",
      "num": 1703,
      "name": "stats_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1703_stats_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1703_stats_cdc.py",
      "description": "CDC table with 2 INSERT batches. Stats on data files (not CDC files).",
      "status": "pass",
      "duration_ms": 2076,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:09.195831+00:00",
      "read_cold_ms": 1454,
      "read_warm_ms": 297,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1704_stats_decimal_multi_precision",
      "num": 1704,
      "name": "stats_decimal_multi_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1704_stats_decimal_multi_precision.sql",
      "read_script": "generator/spark-reads-df/verify_1704_stats_decimal_multi_precision.py",
      "description": "2 INSERT batches with DECIMAL(10,2) + DECIMAL(18,8). Both column stats correct.",
      "status": "pass",
      "duration_ms": 1987,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:11.183578+00:00",
      "read_cold_ms": 1062,
      "read_warm_ms": 388,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 47,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1705_stats_after_multiple_dml",
      "num": 1705,
      "name": "stats_after_multiple_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1705_stats_after_multiple_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1705_stats_after_multiple_dml.py",
      "description": "INSERT 2 batches, UPDATE, DELETE, INSERT 1 more.",
      "status": "pass",
      "duration_ms": 3749,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:14.933477+00:00",
      "read_cold_ms": 1850,
      "read_warm_ms": 706,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 269,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1706_pushdown_int_eq",
      "num": 1706,
      "name": "pushdown_int_eq",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1706_pushdown_int_eq.sql",
      "read_script": "generator/spark-reads-df/verify_1706_pushdown_int_eq.py",
      "description": "5 non-overlapping INT batches. WHERE score=42 should only scan 1 file.",
      "status": "pass",
      "duration_ms": 3393,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:18.328417+00:00",
      "read_cold_ms": 1753,
      "read_warm_ms": 399,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 328,
      "write_warm_ms": 411,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1707_pushdown_int_gt",
      "num": 1707,
      "name": "pushdown_int_gt",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1707_pushdown_int_gt.sql",
      "read_script": "generator/spark-reads-df/verify_1707_pushdown_int_gt.py",
      "description": "5 non-overlapping INT batches. WHERE score > 60 should skip batches 1-3.",
      "status": "pass",
      "duration_ms": 3034,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:21.364180+00:00",
      "read_cold_ms": 1017,
      "read_warm_ms": 399,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 397,
      "write_warm_ms": 225,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1708_pushdown_int_lt",
      "num": 1708,
      "name": "pushdown_int_lt",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1708_pushdown_int_lt.sql",
      "read_script": "generator/spark-reads-df/verify_1708_pushdown_int_lt.py",
      "description": "5 non-overlapping INT batches. WHERE score < 40 should skip batches 3-5.",
      "status": "pass",
      "duration_ms": 3428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:24.793252+00:00",
      "read_cold_ms": 1401,
      "read_warm_ms": 372,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 307,
      "write_warm_ms": 300,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1709_pushdown_decimal_eq",
      "num": 1709,
      "name": "pushdown_decimal_eq",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1709_pushdown_decimal_eq.sql",
      "read_script": "generator/spark-reads-df/verify_1709_pushdown_decimal_eq.py",
      "description": "3 non-overlapping DECIMAL batches. WHERE amount = CAST(250.00 AS DECIMAL(10,2)).",
      "status": "pass",
      "duration_ms": 2963,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:27.757404+00:00",
      "read_cold_ms": 1279,
      "read_warm_ms": 321,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 192,
      "write_warm_ms": 148,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/170_dbx_delete_from_deltaflow",
      "num": 170,
      "name": "dbx_delete_from_deltaflow",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/170_dbx_delete_from_deltaflow.sql",
      "read_script": "generator/spark-reads-df/verify_170_dbx_delete_from_deltaflow.py",
      "description": "- Table with status field for DELETE testing - 25% of rows marked as 'inactive' for deletion - No partitioning",
      "status": "pass",
      "duration_ms": 1874,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:53.803661+00:00",
      "read_cold_ms": 1392,
      "read_warm_ms": 213,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1710_pushdown_decimal_gt",
      "num": 1710,
      "name": "pushdown_decimal_gt",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1710_pushdown_decimal_gt.sql",
      "read_script": "generator/spark-reads-df/verify_1710_pushdown_decimal_gt.py",
      "description": "3 non-overlapping DECIMAL batches. WHERE amount > CAST(300.00 AS DECIMAL(10,2)).",
      "status": "pass",
      "duration_ms": 2959,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:30.716890+00:00",
      "read_cold_ms": 1054,
      "read_warm_ms": 317,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 206,
      "write_warm_ms": 113,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1711_pushdown_timestamp_range",
      "num": 1711,
      "name": "pushdown_timestamp_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1711_pushdown_timestamp_range.sql",
      "read_script": "generator/spark-reads-df/verify_1711_pushdown_timestamp_range.py",
      "description": "3 batches (Jan/Feb/Mar 2024). WHERE ts BETWEEN Feb boundaries.",
      "status": "pass",
      "duration_ms": 3148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:33.869484+00:00",
      "read_cold_ms": 1244,
      "read_warm_ms": 350,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 194,
      "write_warm_ms": 70,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1712_pushdown_string_range",
      "num": 1712,
      "name": "pushdown_string_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1712_pushdown_string_range.sql",
      "read_script": "generator/spark-reads-df/verify_1712_pushdown_string_range.py",
      "description": "3 batches (a_*/b_*/c_*). WHERE name >= 'b_' AND name < 'c_'.",
      "status": "pass",
      "duration_ms": 2726,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:36.602429+00:00",
      "read_cold_ms": 1122,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 321,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1713_pushdown_boolean",
      "num": 1713,
      "name": "pushdown_boolean",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1713_pushdown_boolean.sql",
      "read_script": "generator/spark-reads-df/verify_1713_pushdown_boolean.py",
      "description": "2 batches (all true / all false). WHERE flag = true.",
      "status": "pass",
      "duration_ms": 3394,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:40.003506+00:00",
      "read_cold_ms": 1076,
      "read_warm_ms": 497,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 91,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1714_pushdown_null",
      "num": 1714,
      "name": "pushdown_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1714_pushdown_null.sql",
      "read_script": "generator/spark-reads-df/verify_1714_pushdown_null.py",
      "description": "2 batches (non-NULL / 50% NULL). WHERE score IS NOT NULL.",
      "status": "pass",
      "duration_ms": 3033,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:43.041504+00:00",
      "read_cold_ms": 1289,
      "read_warm_ms": 376,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1715_pushdown_compound",
      "num": 1715,
      "name": "pushdown_compound",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1715_pushdown_compound.sql",
      "read_script": "generator/spark-reads-df/verify_1715_pushdown_compound.py",
      "description": "3 batches. WHERE score > 60 AND amount > CAST(400 AS DECIMAL(10,2)).",
      "status": "pass",
      "duration_ms": 2675,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:45.720493+00:00",
      "read_cold_ms": 1087,
      "read_warm_ms": 384,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 245,
      "write_warm_ms": 90,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1716_pushdown_after_update",
      "num": 1716,
      "name": "pushdown_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1716_pushdown_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_1716_pushdown_after_update.py",
      "description": "5 batches, UPDATE shifts batch 3 scores +500. WHERE score > 500.",
      "status": "pass",
      "duration_ms": 4405,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:50.130697+00:00",
      "read_cold_ms": 1752,
      "read_warm_ms": 588,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 260,
      "write_warm_ms": 341,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1717_pushdown_after_delete",
      "num": 1717,
      "name": "pushdown_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1717_pushdown_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1717_pushdown_after_delete.py",
      "description": "5 batches, DELETE all of batch 3 via DV.",
      "status": "pass",
      "duration_ms": 4231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:54.365974+00:00",
      "read_cold_ms": 1756,
      "read_warm_ms": 565,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 360,
      "write_warm_ms": 320,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1718_pushdown_negative",
      "num": 1718,
      "name": "pushdown_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1718_pushdown_negative.sql",
      "read_script": "generator/spark-reads-df/verify_1718_pushdown_negative.py",
      "description": "2 batches (negative / positive). WHERE val > 0.",
      "status": "pass",
      "duration_ms": 3465,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:52:57.833726+00:00",
      "read_cold_ms": 1179,
      "read_warm_ms": 398,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 111,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1719_pushdown_decimal_between",
      "num": 1719,
      "name": "pushdown_decimal_between",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1719_pushdown_decimal_between.sql",
      "read_script": "generator/spark-reads-df/verify_1719_pushdown_decimal_between.py",
      "description": "3 disjoint DECIMAL batches. WHERE amount BETWEEN 200 AND 300.",
      "status": "pass",
      "duration_ms": 3161,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:53:00.996997+00:00",
      "read_cold_ms": 1456,
      "read_warm_ms": 385,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 317,
      "write_warm_ms": 214,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/171_reverse_dbx_delete_from_deltaflow",
      "num": 171,
      "name": "reverse_dbx_delete_from_deltaflow",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/171_reverse_dbx_delete_from_deltaflow.sql",
      "read_script": "generator/spark-reads-df/verify_171_reverse_dbx_delete_from_deltaflow.py",
      "description": "because it expects this DeltaForge-created table to exist first!",
      "status": "pass",
      "duration_ms": 1990,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:42:55.796022+00:00",
      "read_cold_ms": 1314,
      "read_warm_ms": 363,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1720_pushdown_partition_plus_stats",
      "num": 1720,
      "name": "pushdown_partition_plus_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1720_pushdown_partition_plus_stats.sql",
      "read_script": "generator/spark-reads-df/verify_1720_pushdown_partition_plus_stats.py",
      "description": "Partitioned table + 2 batches per partition.",
      "status": "pass",
      "duration_ms": 3344,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:53:04.343761+00:00",
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      "id": "df-writes/delta/1739_constant_value_file",
      "num": 1739,
      "name": "constant_value_file",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1739_constant_value_file.sql",
      "read_script": "generator/spark-reads-df/verify_1739_constant_value_file.py",
      "description": "2 batches with constant score values.",
      "status": "pass",
      "duration_ms": 4916,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:07.941720+00:00",
      "read_cold_ms": 1338,
      "read_warm_ms": 345,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 194,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/173_update_execute",
      "num": 173,
      "name": "update_execute",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/173_update_execute.sql",
      "read_script": "generator/spark-reads-df/verify_173_update_execute.py",
      "description": "- UPDATE target table with transaction data - Deletion vectors enabled - Initial 200 transactions for UPDATE testing",
      "status": "pass",
      "duration_ms": 2811,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:01.193780+00:00",
      "read_cold_ms": 1294,
      "read_warm_ms": 815,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 25,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1740_single_value_delete",
      "num": 1740,
      "name": "single_value_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1740_single_value_delete.py",
      "description": "INSERT 100, DELETE leaves only rows with score=42.",
      "status": "pass",
      "duration_ms": 5986,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:13.930698+00:00",
      "read_cold_ms": 1860,
      "read_warm_ms": 644,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 182,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1741_gap_in_ids",
      "num": 1741,
      "name": "gap_in_ids",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1741_gap_in_ids.py",
      "description": "INSERT with gaps in id sequence (non-contiguous ids).",
      "status": "pass",
      "duration_ms": 6635,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:20.568228+00:00",
      "read_cold_ms": 1915,
      "read_warm_ms": 846,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1742_extreme_int_values",
      "num": 1742,
      "name": "extreme_int_values",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1742_extreme_int_values.py",
      "description": "INSERT rows with INT at extremes: -2147483648, 0, 2147483647.",
      "status": "pass",
      "duration_ms": 6173,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:26.742569+00:00",
      "read_cold_ms": 1884,
      "read_warm_ms": 443,
      "skip_feature": null,
      "skip_cause": null,
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        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1743_extreme_bigint_values",
      "num": 1743,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1743_extreme_bigint_values.py",
      "description": "INSERT with BIGINT extremes.",
      "status": "pass",
      "duration_ms": 6138,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:32.883397+00:00",
      "read_cold_ms": 1717,
      "read_warm_ms": 484,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
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      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1744_extreme_decimal_values",
      "num": 1744,
      "name": "extreme_decimal_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1744_extreme_decimal_values.py",
      "description": "INSERT with DECIMAL at precision limits.",
      "status": "pass",
      "duration_ms": 4516,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:37.401133+00:00",
      "read_cold_ms": 1943,
      "read_warm_ms": 773,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 245,
      "write_warm_ms": 342,
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        "type:boundary",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1745_extreme_double_values",
      "num": 1745,
      "name": "extreme_double_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1745_extreme_double_values.py",
      "description": "INSERT with DOUBLE extremes.",
      "status": "pass",
      "duration_ms": 7502,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:44.906554+00:00",
      "read_cold_ms": 1983,
      "read_warm_ms": 485,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 272,
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      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1746_all_null_column",
      "num": 1746,
      "name": "all_null_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1746_all_null_column.sql",
      "read_script": "generator/spark-reads-df/verify_1746_all_null_column.py",
      "description": "INSERT where one column is always NULL for all rows.",
      "status": "pass",
      "duration_ms": 4777,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:49.692720+00:00",
      "read_cold_ms": 2239,
      "read_warm_ms": 630,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
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        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1747_constant_string",
      "num": 1747,
      "name": "constant_string",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1747_constant_string.sql",
      "read_script": "generator/spark-reads-df/verify_1747_constant_string.py",
      "description": "INSERT where string column is same value for all rows.",
      "status": "pass",
      "duration_ms": 5415,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:54:55.109053+00:00",
      "read_cold_ms": 1789,
      "read_warm_ms": 576,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
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        "type:integer",
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        "dml:insert",
        "delta:deletion-vectors",
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1748_sparse_then_dense",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1748_sparse_then_dense.py",
      "description": "Batch 1: 90% NULL in score column. Batch 2: 0% NULL.",
      "status": "pass",
      "duration_ms": 5770,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:00.880602+00:00",
      "read_cold_ms": 1733,
      "read_warm_ms": 538,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1749_wide_range_narrow_file",
      "num": 1749,
      "name": "wide_range_narrow_file",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1749_wide_range_narrow_file.sql",
      "read_script": "generator/spark-reads-df/verify_1749_wide_range_narrow_file.py",
      "description": "2 batches with very different range widths.",
      "status": "pass",
      "duration_ms": 6585,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:07.467087+00:00",
      "read_cold_ms": 1747,
      "read_warm_ms": 528,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 202,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/174_delete_dv",
      "num": 174,
      "name": "delete_dv",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/174_delete_dv.sql",
      "read_script": "generator/spark-reads-df/verify_174_delete_dv.py",
      "description": "- Deletion vectors enabled table - Employee records with status distribution - Conditional termination_date based on status",
      "status": "pass",
      "duration_ms": 3447,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:04.642560+00:00",
      "read_cold_ms": 1910,
      "read_warm_ms": 489,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1750_ordering_stats_ultimate",
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      "name": "ordering_stats_ultimate",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1750_ordering_stats_ultimate.sql",
      "read_script": "generator/spark-reads-df/verify_1750_ordering_stats_ultimate.py",
      "description": "Ultimate ordering/stats test: 5 batches with overlapping ranges,",
      "status": "pass",
      "duration_ms": 10026,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:17.494851+00:00",
      "read_cold_ms": 2088,
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      "tags": [
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1751_insert_group_by_sum",
      "num": 1751,
      "name": "insert_group_by_sum",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1751_insert_group_by_sum.sql",
      "read_script": "generator/spark-reads-df/verify_1751_insert_group_by_sum.py",
      "description": "INSERT INTO ... SELECT with GROUP BY and SUM aggregation.",
      "status": "pass",
      "duration_ms": 3148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:20.644801+00:00",
      "read_cold_ms": 1683,
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      "skip_feature": null,
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      "write_cold_ms": 75,
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      "tags": [
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1752_insert_group_by_count",
      "num": 1752,
      "name": "insert_group_by_count",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1752_insert_group_by_count.sql",
      "read_script": "generator/spark-reads-df/verify_1752_insert_group_by_count.py",
      "description": "INSERT INTO ... SELECT with GROUP BY and COUNT(*).",
      "status": "pass",
      "duration_ms": 3918,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:24.564741+00:00",
      "read_cold_ms": 2536,
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      "skip_feature": null,
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1753_insert_group_by_avg",
      "num": 1753,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1753_insert_group_by_avg.sql",
      "read_script": "generator/spark-reads-df/verify_1753_insert_group_by_avg.py",
      "description": "INSERT INTO ... SELECT with GROUP BY and AVG aggregation.",
      "status": "pass",
      "duration_ms": 4766,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:29.332176+00:00",
      "read_cold_ms": 3262,
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        "dml:insert",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1754_insert_group_by_min_max",
      "num": 1754,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1754_insert_group_by_min_max.sql",
      "read_script": "generator/spark-reads-df/verify_1754_insert_group_by_min_max.py",
      "description": "INSERT INTO ... SELECT with GROUP BY and MIN/MAX aggregations.",
      "status": "pass",
      "duration_ms": 3440,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:55:32.773347+00:00",
      "read_cold_ms": 2313,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1755_insert_group_by_decimal",
      "num": 1755,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1755_insert_group_by_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1755_insert_group_by_decimal.py",
      "description": "INSERT INTO ... SELECT with GROUP BY SUM on a DECIMAL column.",
      "status": "pass",
      "duration_ms": 3415,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:55:36.189470+00:00",
      "read_cold_ms": 1829,
      "read_warm_ms": 936,
      "skip_feature": null,
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      "tags": [
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        "dml:insert",
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1756_insert_group_by_having",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1756_insert_group_by_having.py",
      "description": "INSERT INTO ... SELECT with GROUP BY and HAVING filter.",
      "status": "pass",
      "duration_ms": 3972,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:55:40.162889+00:00",
      "read_cold_ms": 2426,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
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    {
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      "description": "INSERT INTO ... SELECT with GROUP BY on two columns.",
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      "duration_ms": 4376,
      "error": null,
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    },
    {
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        "type:string",
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        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
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      "duration_ms": 3708,
      "error": null,
      "notes": null,
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        "type:integer",
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        "delta:change-data-feed",
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    },
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      "id": "df-writes/delta/1760_insert_group_by_then_dml",
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    },
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    {
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    },
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      "id": "df-writes/delta/1763_insert_distinct_multi_col",
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      "duration_ms": 4581,
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      "duration_ms": 3879,
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      "duration_ms": 2948,
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      "manual_tags": [],
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1775_insert_lag_lead",
      "num": 1775,
      "name": "insert_lag_lead",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1775_insert_lag_lead.py",
      "description": "INSERT INTO ... SELECT with LAG and LEAD window functions.",
      "status": "pass",
      "duration_ms": 3396,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:56:47.187778+00:00",
      "read_cold_ms": 2008,
      "read_warm_ms": 708,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 73,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1776_insert_window_partition",
      "num": 1776,
      "name": "insert_window_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1776_insert_window_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1776_insert_window_partition.py",
      "description": "INSERT INTO ... SELECT with SUM() OVER (PARTITION BY ...).",
      "status": "pass",
      "duration_ms": 2948,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:56:50.136778+00:00",
      "read_cold_ms": 1763,
      "read_warm_ms": 593,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1777_insert_dense_rank",
      "num": 1777,
      "name": "insert_dense_rank",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1777_insert_dense_rank.sql",
      "read_script": "generator/spark-reads-df/verify_1777_insert_dense_rank.py",
      "description": "INSERT INTO ... SELECT with DENSE_RANK() OVER (ORDER BY ... DESC).",
      "status": "pass",
      "duration_ms": 2976,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:56:53.113990+00:00",
      "read_cold_ms": 1916,
      "read_warm_ms": 387,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 38,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1778_insert_window_decimal",
      "num": 1778,
      "name": "insert_window_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1778_insert_window_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_1778_insert_window_decimal.py",
      "description": "INSERT INTO ... SELECT with SUM() OVER (PARTITION BY ...) on a DECIMAL.",
      "status": "pass",
      "duration_ms": 2708,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:56:55.823408+00:00",
      "read_cold_ms": 1730,
      "read_warm_ms": 456,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 31,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1779_insert_window_then_dml",
      "num": 1779,
      "name": "insert_window_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1779_insert_window_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1779_insert_window_then_dml.py",
      "description": "INSERT with ROW_NUMBER() then UPDATE the top-10 rows.",
      "status": "pass",
      "duration_ms": 3790,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:56:59.614182+00:00",
      "read_cold_ms": 2340,
      "read_warm_ms": 885,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 142,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/177_generated_compute",
      "num": 177,
      "name": "generated_compute",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/177_generated_compute.sql",
      "read_script": "generator/spark-reads-df/verify_177_generated_compute.py",
      "description": "- Generated/computed columns (full_name, total, year) - DECIMAL type handling for currency values - DATE type handling with interval arithmetic - Deletion vectors enabled",
      "status": "pass",
      "duration_ms": 3692,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:12.732217+00:00",
      "read_cold_ms": 2137,
      "read_warm_ms": 737,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 74,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1780_insert_aggregation_partition_cdc",
      "num": 1780,
      "name": "insert_aggregation_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1780_insert_aggregation_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1780_insert_aggregation_partition_cdc.py",
      "description": "INSERT INTO ... SELECT with GROUP BY into a CDC-enabled, PARTITIONED table.",
      "status": "pass",
      "duration_ms": 3467,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:03.082111+00:00",
      "read_cold_ms": 1865,
      "read_warm_ms": 699,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1781_struct_zorder",
      "num": 1781,
      "name": "struct_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1781_struct_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1781_struct_zorder.py",
      "description": "STRUCT column survives Z-ORDER reorganization.",
      "status": "pass",
      "duration_ms": 4980,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:08.063097+00:00",
      "read_cold_ms": 2373,
      "read_warm_ms": 795,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 205,
      "write_warm_ms": 225,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1782_struct_vacuum",
      "num": 1782,
      "name": "struct_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1782_struct_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1782_struct_vacuum.py",
      "description": "STRUCT survives OPTIMIZE + VACUUM cleanup.",
      "status": "pass",
      "duration_ms": 4025,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:12.089379+00:00",
      "read_cold_ms": 2518,
      "read_warm_ms": 450,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 243,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1783_struct_restore",
      "num": 1783,
      "name": "struct_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1783_struct_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1783_struct_restore.py",
      "description": "RESTORE undoes UPDATE on table containing STRUCT column.",
      "status": "pass",
      "duration_ms": 2839,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:14.930513+00:00",
      "read_cold_ms": 2032,
      "read_warm_ms": 360,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 192,
      "write_warm_ms": 126,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1784_date_zorder",
      "num": 1784,
      "name": "date_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1784_date_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1784_date_zorder.py",
      "description": "DATE column with Z-ORDER BY (event_date).",
      "status": "pass",
      "duration_ms": 2343,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:17.273867+00:00",
      "read_cold_ms": 1018,
      "read_warm_ms": 531,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 274,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1785_date_vacuum",
      "num": 1785,
      "name": "date_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1785_date_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1785_date_vacuum.py",
      "description": "DATE column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 3853,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:21.128693+00:00",
      "read_cold_ms": 2048,
      "read_warm_ms": 583,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 657,
      "write_warm_ms": 272,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1786_date_restore",
      "num": 1786,
      "name": "date_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1786_date_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1786_date_restore.py",
      "description": "RESTORE undoes DELETE on table containing DATE column.",
      "status": "pass",
      "duration_ms": 6061,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:27.191535+00:00",
      "read_cold_ms": 3572,
      "read_warm_ms": 875,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 96,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1787_binary_colmap",
      "num": 1787,
      "name": "binary_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1787_binary_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1787_binary_colmap.py",
      "description": "BINARY column on table with column-mapping mode = name.",
      "status": "pass",
      "duration_ms": 4668,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:31.862052+00:00",
      "read_cold_ms": 2485,
      "read_warm_ms": 973,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 115,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1788_binary_constraint",
      "num": 1788,
      "name": "binary_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1788_binary_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1788_binary_constraint.py",
      "description": "BINARY column with CHECK constraint on a sibling column.",
      "status": "pass",
      "duration_ms": 3239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:35.103490+00:00",
      "read_cold_ms": 2225,
      "read_warm_ms": 558,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 48,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1789_binary_zorder",
      "num": 1789,
      "name": "binary_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1789_binary_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1789_binary_zorder.py",
      "description": "BINARY column survives Z-ORDER on a sibling column.",
      "status": "pass",
      "duration_ms": 3700,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:38.805452+00:00",
      "read_cold_ms": 1830,
      "read_warm_ms": 677,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 201,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/178_v2_checkpoints",
      "num": 178,
      "name": "v2_checkpoints",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/178_v2_checkpoints.sql",
      "read_script": "generator/spark-reads-df/verify_178_v2_checkpoints.py",
      "description": "- V2 checkpoint format compatibility across engines - Multiple data operations: INSERT, UPDATE, DELETE, OPTIMIZE - Deletion vectors enabled",
      "status": "pass",
      "duration_ms": 2067,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:14.800057+00:00",
      "read_cold_ms": 1516,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 353,
      "write_warm_ms": 388,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1790_binary_vacuum",
      "num": 1790,
      "name": "binary_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1790_binary_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1790_binary_vacuum.py",
      "description": "BINARY column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 4518,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:43.325551+00:00",
      "read_cold_ms": 2430,
      "read_warm_ms": 819,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 220,
      "write_warm_ms": 280,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1791_binary_restore",
      "num": 1791,
      "name": "binary_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1791_binary_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1791_binary_restore.py",
      "description": "RESTORE undoes UPDATE on table containing BINARY column.",
      "status": "pass",
      "duration_ms": 3635,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:46.961516+00:00",
      "read_cold_ms": 2363,
      "read_warm_ms": 524,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 234,
      "write_warm_ms": 158,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1792_tinyint_cdc",
      "num": 1792,
      "name": "tinyint_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1792_tinyint_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1792_tinyint_cdc.py",
      "description": "TINYINT values captured by Change Data Feed across INSERT/UPDATE/DELETE.",
      "status": "pass",
      "duration_ms": 9283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:57:56.245540+00:00",
      "read_cold_ms": 7260,
      "read_warm_ms": 985,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 141,
      "write_warm_ms": 183,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1793_tinyint_zorder",
      "num": 1793,
      "name": "tinyint_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1793_tinyint_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1793_tinyint_zorder.py",
      "description": "TINYINT column with Z-ORDER BY (val).",
      "status": "pass",
      "duration_ms": 4521,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:00.767184+00:00",
      "read_cold_ms": 2629,
      "read_warm_ms": 860,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 337,
      "write_warm_ms": 157,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1794_tinyint_vacuum",
      "num": 1794,
      "name": "tinyint_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1794_tinyint_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1794_tinyint_vacuum.py",
      "description": "TINYINT column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 3775,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:04.543194+00:00",
      "read_cold_ms": 2302,
      "read_warm_ms": 531,
      "skip_feature": null,
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      "write_cold_ms": 175,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1795_smallint_cdc_partition",
      "num": 1795,
      "name": "smallint_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1795_smallint_cdc_partition.py",
      "description": "SMALLINT column on CDF + PARTITIONED table.",
      "status": "pass",
      "duration_ms": 4445,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:08.989992+00:00",
      "read_cold_ms": 2659,
      "read_warm_ms": 790,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 210,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1796_float_zorder",
      "num": 1796,
      "name": "float_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1796_float_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1796_float_zorder.py",
      "description": "FLOAT column with Z-ORDER BY (val).",
      "status": "pass",
      "duration_ms": 3815,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:12.807316+00:00",
      "read_cold_ms": 1971,
      "read_warm_ms": 537,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 326,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
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        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1797_float_vacuum",
      "num": 1797,
      "name": "float_vacuum",
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      "reader": "spark",
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      "description": "FLOAT column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 4641,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:17.450358+00:00",
      "read_cold_ms": 2488,
      "read_warm_ms": 753,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 181,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1798_float_restore",
      "num": 1798,
      "name": "float_restore",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1798_float_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1798_float_restore.py",
      "description": "RESTORE undoes UPDATE on FLOAT values.",
      "status": "pass",
      "duration_ms": 3623,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:21.075575+00:00",
      "read_cold_ms": 2254,
      "read_warm_ms": 648,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 143,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1799_struct_cdc_zorder",
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      "name": "struct_cdc_zorder",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1799_struct_cdc_zorder.py",
      "description": "STRUCT + CDC + Z-ORDER three-way combination.",
      "status": "pass",
      "duration_ms": 3535,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:24.612480+00:00",
      "read_cold_ms": 1822,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/179_type_widening_interop",
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      "read_script": "generator/spark-reads-df/verify_179_type_widening_interop.py",
      "description": "- Type widening schema evolution compatibility - SHORT (INT16) -> INT (INT32) widening - INT (INT32) -> LONG (INT64) widening - Type widening table property",
      "status": "pass",
      "duration_ms": 3330,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:18.130829+00:00",
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      "skip_feature": null,
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        "type:integer",
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        "dml:insert",
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        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/17_action_cdc_file_partitioned",
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      "status": "pass",
      "duration_ms": 2740,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:20.872382+00:00",
      "read_cold_ms": 1581,
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      "skip_feature": null,
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      "write_cold_ms": 158,
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      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1800_complex_types_all_maintenance",
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      "name": "complex_types_all_maintenance",
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      "reader": "spark",
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      "description": "All four complex/non-trivial types (STRUCT, BINARY, DATE, FLOAT)",
      "status": "pass",
      "duration_ms": 3344,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 1901,
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      "tags": [
        "type:binary",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1801_delete_where_sign",
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      "name": "delete_where_sign",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1801_delete_where_sign.sql",
      "read_script": "generator/spark-reads-df/verify_1801_delete_where_sign.py",
      "description": "DELETE WHERE SIGN(value) = -1.",
      "status": "pass",
      "duration_ms": 3770,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:31.729939+00:00",
      "read_cold_ms": 2213,
      "read_warm_ms": 735,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 35,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1802_delete_where_upper",
      "num": 1802,
      "name": "delete_where_upper",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1802_delete_where_upper.sql",
      "read_script": "generator/spark-reads-df/verify_1802_delete_where_upper.py",
      "description": "DELETE WHERE UPPER(name) = 'ITEM_50'.",
      "status": "pass",
      "duration_ms": 4061,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:35.793961+00:00",
      "read_cold_ms": 2376,
      "read_warm_ms": 821,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1803_delete_where_length",
      "num": 1803,
      "name": "delete_where_length",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1803_delete_where_length.sql",
      "read_script": "generator/spark-reads-df/verify_1803_delete_where_length.py",
      "description": "DELETE WHERE LENGTH(name) = 6.",
      "status": "pass",
      "duration_ms": 4249,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:40.044426+00:00",
      "read_cold_ms": 2534,
      "read_warm_ms": 894,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1804_delete_where_reverse",
      "num": 1804,
      "name": "delete_where_reverse",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1804_delete_where_reverse.sql",
      "read_script": "generator/spark-reads-df/verify_1804_delete_where_reverse.py",
      "description": "DELETE WHERE REVERSE(name) = 'cificeps_esrever'.",
      "status": "pass",
      "duration_ms": 3742,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:43.787258+00:00",
      "read_cold_ms": 2024,
      "read_warm_ms": 987,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1805_merge_where_length",
      "num": 1805,
      "name": "merge_where_length",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1805_merge_where_length.sql",
      "read_script": "generator/spark-reads-df/verify_1805_merge_where_length.py",
      "description": "MERGE with LENGTH() in WHEN MATCHED AND condition.",
      "status": "pass",
      "duration_ms": 4405,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:48.193928+00:00",
      "read_cold_ms": 2507,
      "read_warm_ms": 941,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 180,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1806_merge_where_upper",
      "num": 1806,
      "name": "merge_where_upper",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1806_merge_where_upper.sql",
      "read_script": "generator/spark-reads-df/verify_1806_merge_where_upper.py",
      "description": "MERGE with UPPER() applied to both target and source in MATCHED condition.",
      "status": "pass",
      "duration_ms": 3842,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:52.037463+00:00",
      "read_cold_ms": 2333,
      "read_warm_ms": 678,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1807_merge_where_sign",
      "num": 1807,
      "name": "merge_where_sign",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1807_merge_where_sign.sql",
      "read_script": "generator/spark-reads-df/verify_1807_merge_where_sign.py",
      "description": "MERGE with SIGN() in WHEN MATCHED AND condition.",
      "status": "pass",
      "duration_ms": 4127,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:58:56.165852+00:00",
      "read_cold_ms": 2710,
      "read_warm_ms": 759,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 106,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1808_update_sqrt_between",
      "num": 1808,
      "name": "update_sqrt_between",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1808_update_sqrt_between.sql",
      "read_script": "generator/spark-reads-df/verify_1808_update_sqrt_between.py",
      "description": "UPDATE with SQRT() + BETWEEN composite predicate.",
      "status": "pass",
      "duration_ms": 4175,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:00.342159+00:00",
      "read_cold_ms": 2331,
      "read_warm_ms": 650,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1809_delete_compound_functions",
      "num": 1809,
      "name": "delete_compound_functions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1809_delete_compound_functions.sql",
      "read_script": "generator/spark-reads-df/verify_1809_delete_compound_functions.py",
      "description": "DELETE with multiple function predicates AND-combined.",
      "status": "pass",
      "duration_ms": 3998,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:04.341511+00:00",
      "read_cold_ms": 2046,
      "read_warm_ms": 972,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 222,
      "write_warm_ms": 116,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/180_variant_type",
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      "name": "variant_type",
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      "description": "- VARIANT semi-structured data type compatibility - JSON data stored in STRING column - Deletion vectors enabled - Update operations creating DVs",
      "status": "pass",
      "duration_ms": 2314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:23.190491+00:00",
      "read_cold_ms": 1355,
      "read_warm_ms": 390,
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      "skip_cause": null,
      "write_cold_ms": 103,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1810_nmbys_with_function_condition",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "NOT MATCHED BY SOURCE branch with ABS() function condition.",
      "status": "pass",
      "duration_ms": 3942,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:08.284305+00:00",
      "read_cold_ms": 2361,
      "read_warm_ms": 822,
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      "write_cold_ms": 127,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1811_update_function_chain",
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      "name": "update_function_chain",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3817,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:12.102201+00:00",
      "read_cold_ms": 2125,
      "read_warm_ms": 968,
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        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1812_delete_function_in_or",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3472,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:15.576550+00:00",
      "read_cold_ms": 1976,
      "read_warm_ms": 797,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1813_merge_functions_set",
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      "name": "merge_functions_set",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1813_merge_functions_set.py",
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      "status": "pass",
      "duration_ms": 4066,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:19.644651+00:00",
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      "read_warm_ms": 1038,
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      "skip_cause": null,
      "write_cold_ms": 216,
      "write_warm_ms": 53,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1814_update_function_partition",
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      "description": "Function calls in UPDATE SET on a partitioned table, scoped by partition predicate.",
      "status": "pass",
      "duration_ms": 4377,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:24.024801+00:00",
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1815_function_dml_lifecycle",
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      "description": "Functions used across the full DML lifecycle:",
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      "duration_ms": 4143,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:28.168591+00:00",
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        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/1816_protocol_v1_writer",
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      "description": "Minimal Delta protocol (minReaderVersion=1, minWriterVersion=1).",
      "status": "pass",
      "duration_ms": 2439,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:30.610144+00:00",
      "read_cold_ms": 1954,
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        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/1817_protocol_v2_reader_v3_writer",
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      "reader": "spark",
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      "duration_ms": 2460,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:33.071897+00:00",
      "read_cold_ms": 1901,
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        "type:integer",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1818_protocol_v2_v5_full",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Protocol minReaderVersion=2, minWriterVersion=5 (typical for",
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      "duration_ms": 2152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:35.224942+00:00",
      "read_cold_ms": 1496,
      "read_warm_ms": 369,
      "skip_feature": null,
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      "write_cold_ms": 198,
      "write_warm_ms": 136,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1819_protocol_v3_v7_dv",
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      "name": "protocol_v3_v7_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1819_protocol_v3_v7_dv.sql",
      "read_script": "generator/spark-reads-df/verify_1819_protocol_v3_v7_dv.py",
      "description": "Protocol minReaderVersion=3, minWriterVersion=7 (table features),",
      "status": "pass",
      "duration_ms": 4055,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:39.281840+00:00",
      "read_cold_ms": 2529,
      "read_warm_ms": 684,
      "skip_feature": null,
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      "write_cold_ms": 46,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/181_timestamp_ntz",
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      "reader": "spark",
      "write_script": "generator/df-sql/181_timestamp_ntz.sql",
      "read_script": "generator/spark-reads-df/verify_181_timestamp_ntz.py",
      "description": "- TIMESTAMP_NTZ (No Time Zone) compatibility - Timezone-less timestamps - No TZ conversion on read/write - Business time semantics",
      "status": "pass",
      "duration_ms": 3037,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:43:26.233889+00:00",
      "read_cold_ms": 1975,
      "read_warm_ms": 334,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1820_protocol_explicit_features",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1820_protocol_explicit_features.py",
      "description": "Auto-promoted protocol from explicit deletionVectors feature.",
      "status": "pass",
      "duration_ms": 4000,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:43.283485+00:00",
      "read_cold_ms": 2422,
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      "write_cold_ms": 164,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1821_protocol_appendonly_feature",
      "num": 1821,
      "name": "protocol_appendonly_feature",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1821_protocol_appendonly_feature.sql",
      "read_script": "generator/spark-reads-df/verify_1821_protocol_appendonly_feature.py",
      "description": "appendOnly table feature -- DELETE/UPDATE forbidden.",
      "status": "pass",
      "duration_ms": 2231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:45.516193+00:00",
      "read_cold_ms": 1433,
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        "type:integer",
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        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1822_protocol_invariants_feature",
      "num": 1822,
      "name": "protocol_invariants_feature",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1822_protocol_invariants_feature.sql",
      "read_script": "generator/spark-reads-df/verify_1822_protocol_invariants_feature.py",
      "description": "CHECK constraint backed by invariants writer feature.",
      "status": "pass",
      "duration_ms": 3343,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:48.861694+00:00",
      "read_cold_ms": 2146,
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      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1823_protocol_columnmapping_id",
      "num": 1823,
      "name": "protocol_columnmapping_id",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1823_protocol_columnmapping_id.sql",
      "read_script": "generator/spark-reads-df/verify_1823_protocol_columnmapping_id.py",
      "description": "Column mapping in 'id' mode (vs the more common 'name' mode).",
      "status": "pass",
      "duration_ms": 3914,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:59:52.782189+00:00",
      "read_cold_ms": 2207,
      "read_warm_ms": 764,
      "skip_feature": null,
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      "write_cold_ms": 126,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1824_protocol_cdc_feature",
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      "name": "protocol_cdc_feature",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1824_protocol_cdc_feature.sql",
      "read_script": "generator/spark-reads-df/verify_1824_protocol_cdc_feature.py",
      "description": "Change Data Feed writer feature with explicit protocol promotion.",
      "status": "pass",
      "duration_ms": 4035,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:59:56.818631+00:00",
      "read_cold_ms": 2266,
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      "write_cold_ms": 103,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1825_protocol_check_constraints",
      "num": 1825,
      "name": "protocol_check_constraints",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "checkConstraints writer feature.",
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      "duration_ms": 2959,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:59:59.779095+00:00",
      "read_cold_ms": 2019,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1826_protocol_multiple_features",
      "num": 1826,
      "name": "protocol_multiple_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1826_protocol_multiple_features.sql",
      "read_script": "generator/spark-reads-df/verify_1826_protocol_multiple_features.py",
      "description": "Multiple writer features active simultaneously: deletion vectors,",
      "status": "pass",
      "duration_ms": 3980,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:00:03.759718+00:00",
      "read_cold_ms": 2249,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
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        "type:string",
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      "id": "df-writes/delta/1828_protocol_evolution_upgrade",
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      "status": "pass",
      "duration_ms": 3165,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:00:09.086744+00:00",
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        "type:integer",
        "dml:insert",
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      "manual_tags": [],
      "concern_note": null
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      "id": "df-writes/delta/1829_protocol_with_partition",
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      "status": "pass",
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        "type:integer",
        "type:string",
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      "status": "pass",
      "duration_ms": 2659,
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        "format:delta"
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      "id": "df-writes/delta/1830_protocol_full_lifecycle",
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        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
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        "delta:partitioning",
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      "manual_tags": [],
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        "type:integer",
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        "dml:insert",
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        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
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        "type:decimal",
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      "read_script": "generator/spark-reads-df/verify_1844_aggregation_max_min.py",
      "description": "Aggregated INSERT with MIN, MAX, and AVG aggregates.",
      "status": "pass",
      "duration_ms": 2682,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:00:57.657605+00:00",
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      "read_warm_ms": 476,
      "skip_feature": null,
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        "type:floating",
        "type:integer",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1845_window_running_sum",
      "num": 1845,
      "name": "window_running_sum",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT with running sum window function.",
      "status": "pass",
      "duration_ms": 2402,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:00.060824+00:00",
      "read_cold_ms": 1620,
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      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1846_window_dense_rank_typed",
      "num": 1846,
      "name": "window_dense_rank_typed",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1846_window_dense_rank_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1846_window_dense_rank_typed.py",
      "description": "DENSE_RANK window over a typed INT column.",
      "status": "pass",
      "duration_ms": 2621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:02.683523+00:00",
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      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1847_aggregation_having_typed",
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 2766,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:05.450438+00:00",
      "read_cold_ms": 1646,
      "read_warm_ms": 535,
      "skip_feature": null,
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        "type:floating",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1848_distinct_typed_combo",
      "num": 1848,
      "name": "distinct_typed_combo",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "SELECT DISTINCT across typed columns (INT + DECIMAL + BOOLEAN).",
      "status": "pass",
      "duration_ms": 3354,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:08.805913+00:00",
      "read_cold_ms": 2035,
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      "skip_feature": null,
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        "type:floating",
        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1849_subquery_aggregated",
      "num": 1849,
      "name": "subquery_aggregated",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 2149,
      "error": null,
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/184_streaming_write",
      "num": 184,
      "name": "streaming_write",
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      "reader": "spark",
      "write_script": "generator/df-sql/184_streaming_write.sql",
      "read_script": "generator/spark-reads-df/verify_184_streaming_write.py",
      "description": "Streaming write simulation with append-only semantics Multiple micro-batches creating multiple versions EVENT_TYPES = [\"click\", \"view\", \"purchase\", \"cart_add\", \"wishlist\"] BASE_TIMESTAMP = 1704067200000000 (2024-01-01T00:00:00Z)",
      "status": "pass",
      "duration_ms": 1622,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:39.172849+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 246,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1850_aggregation_ultimate",
      "num": 1850,
      "name": "aggregation_ultimate",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "ULTIMATE aggregation test combining GROUP BY + HAVING +",
      "status": "pass",
      "duration_ms": 2710,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:13.666936+00:00",
      "read_cold_ms": 1195,
      "read_warm_ms": 500,
      "skip_feature": null,
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        "type:floating",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1851_like_basic",
      "num": 1851,
      "name": "like_basic",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1851_like_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1851_like_basic.py",
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      "status": "pass",
      "duration_ms": 2540,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:16.208023+00:00",
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      "skip_feature": null,
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        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1852_like_underscore",
      "num": 1852,
      "name": "like_underscore",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1852_like_underscore.sql",
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      "status": "pass",
      "duration_ms": 2949,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:19.158485+00:00",
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        "dml:delete",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1853_like_escape",
      "num": 1853,
      "name": "like_escape",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1853_like_escape.sql",
      "read_script": "generator/spark-reads-df/verify_1853_like_escape.py",
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      "status": "pass",
      "duration_ms": 3916,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:23.075916+00:00",
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      "skip_feature": null,
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        "dml:delete",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 1854,
      "name": "like_start",
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/1854_like_start.sql",
      "read_script": "generator/spark-reads-df/verify_1854_like_start.py",
      "description": "LIKE 'prefix%' (anchored prefix match).",
      "status": "pass",
      "duration_ms": 3904,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:26.981279+00:00",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1855_like_end",
      "num": 1855,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1855_like_end.sql",
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      "description": "LIKE '%suffix' anchored suffix match used in UPDATE WHERE.",
      "status": "pass",
      "duration_ms": 3528,
      "error": null,
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      "verified_at": "2026-05-19T18:01:30.510612+00:00",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1856_like_middle",
      "num": 1856,
      "name": "like_middle",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1856_like_middle.sql",
      "read_script": "generator/spark-reads-df/verify_1856_like_middle.py",
      "description": "LIKE '%middle%' substring match.",
      "status": "pass",
      "duration_ms": 4207,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:34.718821+00:00",
      "read_cold_ms": 2262,
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      "skip_feature": null,
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      "write_cold_ms": 119,
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        "dml:delete",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1857_like_in_update",
      "num": 1857,
      "name": "like_in_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1857_like_in_update.sql",
      "read_script": "generator/spark-reads-df/verify_1857_like_in_update.py",
      "description": "UPDATE WHERE name LIKE 'item_1%' sets tier='premium'.",
      "status": "pass",
      "duration_ms": 4940,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:39.659494+00:00",
      "read_cold_ms": 1917,
      "read_warm_ms": 929,
      "skip_feature": null,
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      "write_cold_ms": 91,
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      "tags": [
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1858_like_in_merge",
      "num": 1858,
      "name": "like_in_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1858_like_in_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1858_like_in_merge.py",
      "description": "MERGE with LIKE in WHEN MATCHED conditional branches.",
      "status": "pass",
      "duration_ms": 4600,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:44.260249+00:00",
      "read_cold_ms": 2748,
      "read_warm_ms": 900,
      "skip_feature": null,
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1859_not_like",
      "num": 1859,
      "name": "not_like",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1859_not_like.sql",
      "read_script": "generator/spark-reads-df/verify_1859_not_like.py",
      "description": "DELETE WHERE name NOT LIKE 'keep_%'.",
      "status": "pass",
      "duration_ms": 4762,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:49.023877+00:00",
      "read_cold_ms": 2514,
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      "skip_feature": null,
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      "write_cold_ms": 74,
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      "tags": [
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/185_cdf_consume",
      "num": 185,
      "name": "cdf_consume",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/185_cdf_consume.sql",
      "read_script": "generator/spark-reads-df/verify_185_cdf_consume.py",
      "description": "Change Data Feed (CDF) enabled table INSERT, UPDATE, DELETE, and MERGE operations STATUSES = [\"active\", \"inactive\", \"pending\"]",
      "status": "pass",
      "duration_ms": 1661,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:40.834891+00:00",
      "read_cold_ms": 1018,
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      "skip_feature": null,
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      "write_cold_ms": 186,
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      "tags": [
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        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1860_like_partition",
      "num": 1860,
      "name": "like_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1860_like_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1860_like_partition.py",
      "description": "LIKE predicate combined with partition predicate.",
      "status": "pass",
      "duration_ms": 4104,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T18:01:53.129471+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 144,
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      "tags": [
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        "dml:delete",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1861_like_cdc",
      "num": 1861,
      "name": "like_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1861_like_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1861_like_cdc.py",
      "description": "LIKE-based DELETE on a CDC-enabled table.",
      "status": "pass",
      "duration_ms": 4321,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:01:57.451777+00:00",
      "read_cold_ms": 2616,
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      "skip_feature": null,
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      "write_cold_ms": 128,
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      "tags": [
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        "dml:cdc-write",
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        "delta:deletion-vectors",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1862_starts_with",
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      "name": "starts_with",
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      "description": "STARTS_WITH(name, 'item_1') prefix predicate.",
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        "type:integer",
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        "dml:delete",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1863_ends_with",
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      "description": "Suffix match via LIKE '%_done'.",
      "status": "pass",
      "duration_ms": 4179,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1864_contains_like",
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      "writer": "delta-forge",
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      "description": "WHERE LIKE '%substring%' contains-style match.",
      "status": "pass",
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        "type:integer",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1865_position_substring",
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    },
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      "id": "df-writes/delta/1866_length_predicate",
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      ],
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    },
    {
      "id": "df-writes/delta/1867_string_concat_predicate",
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      "description": "CONCAT in WHERE predicate.",
      "status": "pass",
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      "extras": {},
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1868_upper_predicate",
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      ],
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    },
    {
      "id": "df-writes/delta/1869_lower_predicate",
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      "status": "pass",
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      "error": null,
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      "extras": {},
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      ],
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    },
    {
      "id": "df-writes/delta/186_true_concurrent",
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      "reader": "spark",
      "write_script": "generator/df-sql/186_true_concurrent.sql",
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      "description": "- True concurrent write simulation with multiple versions - UPDATE and DELETE operations with deletion vectors - OCC conflict detection testing",
      "status": "pass",
      "duration_ms": 1742,
      "error": null,
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        "type:integer",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1870_string_compare_partition",
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      "duration_ms": 3891,
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    },
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    },
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      "id": "df-writes/delta/1873_like_zorder",
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    },
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4012,
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      "manual_tags": [],
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    },
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      "description": "String functions across the full DML lifecycle.",
      "status": "pass",
      "duration_ms": 5010,
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      "extras": {},
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      "manual_tags": [],
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    },
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/1876_year_extract.sql",
      "read_script": "generator/spark-reads-df/verify_1876_year_extract.py",
      "description": "YEAR() function applied to TIMESTAMP via UPDATE.",
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      "extras": {},
      "verified_at": "2026-05-19T18:03:01.522023+00:00",
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      "manual_tags": [],
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    },
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      "write_script": "generator/df-sql/1877_month_extract.sql",
      "read_script": "generator/spark-reads-df/verify_1877_month_extract.py",
      "description": "MONTH() function applied to TIMESTAMP via UPDATE.",
      "status": "pass",
      "duration_ms": 3807,
      "error": null,
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      "extras": {},
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    },
    {
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1878_day_extract.sql",
      "read_script": "generator/spark-reads-df/verify_1878_day_extract.py",
      "description": "DAY() function applied to TIMESTAMP via UPDATE.",
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      "duration_ms": 3836,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    },
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      "id": "df-writes/delta/1879_year_month_day",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1879_year_month_day.sql",
      "read_script": "generator/spark-reads-df/verify_1879_year_month_day.py",
      "description": "YEAR/MONTH/DAY in a single UPDATE statement.",
      "status": "pass",
      "duration_ms": 4359,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:13.529620+00:00",
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      "tags": [
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      "manual_tags": [],
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      "id": "df-writes/delta/187_large_files",
      "num": 187,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/187_large_files.sql",
      "read_script": "generator/spark-reads-df/verify_187_large_files.py",
      "description": "Large file handling with 100K+ rows UPDATE and DELETE operations on large datasets Large string payloads (200 chars each)",
      "status": "pass",
      "duration_ms": 12492,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:55.070406+00:00",
      "read_cold_ms": 1003,
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      "skip_feature": null,
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      "tags": [
        "type:floating",
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        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1880_year_in_where",
      "num": 1880,
      "name": "year_in_where",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1880_year_in_where.sql",
      "read_script": "generator/spark-reads-df/verify_1880_year_in_where.py",
      "description": "YEAR() in DELETE WHERE clause.",
      "status": "pass",
      "duration_ms": 4358,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:17.890169+00:00",
      "read_cold_ms": 2391,
      "read_warm_ms": 824,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 23,
      "write_warm_ms": 39,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1881_extract_year",
      "num": 1881,
      "name": "extract_year",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1881_extract_year.sql",
      "read_script": "generator/spark-reads-df/verify_1881_extract_year.py",
      "description": "EXTRACT(YEAR FROM date). event_date = 19723+i days (i=1..100 -> 2024-01-01..2024-04-09). year_val should be 2024 for all.",
      "status": "pass",
      "duration_ms": 5517,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:23.409638+00:00",
      "read_cold_ms": 3153,
      "read_warm_ms": 1125,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 232,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1882_extract_month",
      "num": 1882,
      "name": "extract_month",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1882_extract_month.sql",
      "read_script": "generator/spark-reads-df/verify_1882_extract_month.py",
      "description": "EXTRACT(MONTH FROM date).",
      "status": "pass",
      "duration_ms": 5095,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:28.507760+00:00",
      "read_cold_ms": 3468,
      "read_warm_ms": 782,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 132,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1883_extract_day",
      "num": 1883,
      "name": "extract_day",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1883_extract_day.sql",
      "read_script": "generator/spark-reads-df/verify_1883_extract_day.py",
      "description": "EXTRACT(DAY FROM date). Days 1..31 expected.",
      "status": "pass",
      "duration_ms": 4548,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:33.056940+00:00",
      "read_cold_ms": 2809,
      "read_warm_ms": 856,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 67,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1884_date_diff",
      "num": 1884,
      "name": "date_diff",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1884_date_diff.sql",
      "read_script": "generator/spark-reads-df/verify_1884_date_diff.py",
      "description": "DATEDIFF(end, start) returning days between two dates.",
      "status": "pass",
      "duration_ms": 4218,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:37.275966+00:00",
      "read_cold_ms": 2353,
      "read_warm_ms": 1089,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 79,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1885_date_add_function",
      "num": 1885,
      "name": "date_add_function",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1885_date_add_function.sql",
      "read_script": "generator/spark-reads-df/verify_1885_date_add_function.py",
      "description": "DATE_ADD(base_date, 30) producing a future date.",
      "status": "pass",
      "duration_ms": 3965,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:41.242655+00:00",
      "read_cold_ms": 2490,
      "read_warm_ms": 743,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 45,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1886_date_sub_function",
      "num": 1886,
      "name": "date_sub_function",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1886_date_sub_function.sql",
      "read_script": "generator/spark-reads-df/verify_1886_date_sub_function.py",
      "description": "DATE_SUB(base_date, 7) producing a past date.",
      "status": "pass",
      "duration_ms": 3914,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:45.158760+00:00",
      "read_cold_ms": 2437,
      "read_warm_ms": 675,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 203,
      "write_warm_ms": 51,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1887_to_date",
      "num": 1887,
      "name": "to_date",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1887_to_date.sql",
      "read_script": "generator/spark-reads-df/verify_1887_to_date.py",
      "description": "TO_DATE(string) parsing date string into DATE.",
      "status": "pass",
      "duration_ms": 3918,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:49.077392+00:00",
      "read_cold_ms": 2429,
      "read_warm_ms": 793,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 84,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1888_to_timestamp",
      "num": 1888,
      "name": "to_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1888_to_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1888_to_timestamp.py",
      "description": "TO_TIMESTAMP(string) parsing timestamp string.",
      "status": "pass",
      "duration_ms": 4574,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:53.653472+00:00",
      "read_cold_ms": 2612,
      "read_warm_ms": 965,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1889_date_format",
      "num": 1889,
      "name": "date_format",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1889_date_format.sql",
      "read_script": "generator/spark-reads-df/verify_1889_date_format.py",
      "description": "DATE_FORMAT(date, 'yyyy-MM-dd') for output formatting.",
      "status": "pass",
      "duration_ms": 4043,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:03:57.697644+00:00",
      "read_cold_ms": 2387,
      "read_warm_ms": 1012,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 79,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/188_many_files",
      "num": 188,
      "name": "many_files",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/188_many_files.sql",
      "read_script": "generator/spark-reads-df/verify_188_many_files.py",
      "description": "Many files interoperability testing (200+ small files) 100 initial rows (batch=0) + 100 batches of 20 rows each UPDATE, DELETE, and OPTIMIZE operations",
      "status": "pass",
      "duration_ms": 1448,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:56.518957+00:00",
      "read_cold_ms": 952,
      "read_warm_ms": 231,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 7220,
      "write_warm_ms": 6891,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1890_year_in_update_set",
      "num": 1890,
      "name": "year_in_update_set",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1890_year_in_update_set.sql",
      "read_script": "generator/spark-reads-df/verify_1890_year_in_update_set.py",
      "description": "YEAR() inside CONCAT in UPDATE SET expression.",
      "status": "pass",
      "duration_ms": 4541,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:02.240566+00:00",
      "read_cold_ms": 2625,
      "read_warm_ms": 754,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 189,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1891_month_filter_partition",
      "num": 1891,
      "name": "month_filter_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1891_month_filter_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1891_month_filter_partition.py",
      "description": "MONTH() in DELETE WHERE on partitioned table.",
      "status": "pass",
      "duration_ms": 5348,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:07.591997+00:00",
      "read_cold_ms": 3161,
      "read_warm_ms": 1076,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 66,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1892_year_filter_cdc",
      "num": 1892,
      "name": "year_filter_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1892_year_filter_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1892_year_filter_cdc.py",
      "description": "YEAR() in DELETE WHERE on a CDC-enabled table.",
      "status": "pass",
      "duration_ms": 5018,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:12.613395+00:00",
      "read_cold_ms": 3422,
      "read_warm_ms": 707,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1893_date_arithmetic",
      "num": 1893,
      "name": "date_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1893_date_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_1893_date_arithmetic.py",
      "description": "Date offset stored as integer days_old without function calls.",
      "status": "pass",
      "duration_ms": 3773,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:16.387830+00:00",
      "read_cold_ms": 2307,
      "read_warm_ms": 686,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 88,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1894_date_in_partition",
      "num": 1894,
      "name": "date_in_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1894_date_in_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1894_date_in_partition.py",
      "description": "DATE column with derived integer partition column.",
      "status": "pass",
      "duration_ms": 2870,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:19.258833+00:00",
      "read_cold_ms": 1617,
      "read_warm_ms": 674,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 39,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1895_timestamp_minute_extract",
      "num": 1895,
      "name": "timestamp_minute_extract",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1895_timestamp_minute_extract.sql",
      "read_script": "generator/spark-reads-df/verify_1895_timestamp_minute_extract.py",
      "description": "HOUR() function applied to TIMESTAMP.",
      "status": "pass",
      "duration_ms": 3703,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:22.962808+00:00",
      "read_cold_ms": 2177,
      "read_warm_ms": 720,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 257,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1896_date_function_chain",
      "num": 1896,
      "name": "date_function_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1896_date_function_chain.sql",
      "read_script": "generator/spark-reads-df/verify_1896_date_function_chain.py",
      "description": "Chained date functions YEAR/MONTH composed with CONCAT.",
      "status": "pass",
      "duration_ms": 3992,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:26.956376+00:00",
      "read_cold_ms": 2479,
      "read_warm_ms": 778,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
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      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1897_date_in_merge",
      "num": 1897,
      "name": "date_in_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1897_date_in_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1897_date_in_merge.py",
      "description": "YEAR() in MERGE WHEN MATCHED conditional branch.",
      "status": "pass",
      "duration_ms": 3615,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:30.572695+00:00",
      "read_cold_ms": 2171,
      "read_warm_ms": 714,
      "skip_feature": null,
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      "write_cold_ms": 144,
      "write_warm_ms": 117,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1898_date_function_constraint",
      "num": 1898,
      "name": "date_function_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1898_date_function_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1898_date_function_constraint.py",
      "description": "CHECK constraint comparing event_date to a constant Date32 floor.",
      "status": "pass",
      "duration_ms": 2940,
      "error": null,
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      "verified_at": "2026-05-19T18:04:33.513312+00:00",
      "read_cold_ms": 1806,
      "read_warm_ms": 396,
      "skip_feature": null,
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      "write_cold_ms": 49,
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        "type:date",
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1899_date_extract_evolve",
      "num": 1899,
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 4093,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:37.607572+00:00",
      "read_cold_ms": 2190,
      "read_warm_ms": 833,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
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        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/189_photon_engine",
      "num": 189,
      "name": "photon_engine",
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      "write_script": "generator/df-sql/189_photon_engine.sql",
      "read_script": "generator/spark-reads-df/verify_189_photon_engine.py",
      "description": "Photon engine interoperability testing Large dataset (5000+ rows), UPDATE/DELETE with DVs",
      "status": "pass",
      "duration_ms": 1666,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:58.185489+00:00",
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 178,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
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        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/18_action_txn_idempotent_writes",
      "num": 18,
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      "reader": "spark",
      "write_script": "generator/df-sql/18_action_txn_idempotent_writes.sql",
      "read_script": "generator/spark-reads-df/verify_18_action_txn_idempotent_writes.py",
      "description": "Transaction IDs prevent duplicate payment processing during failures and retries.",
      "status": "pass",
      "duration_ms": 4467,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:43:55.888295+00:00",
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      "write_cold_ms": 155,
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      "tags": [
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        "type:decimal",
        "type:integer",
        "type:string",
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        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1900_date_lifecycle",
      "num": 1900,
      "name": "date_lifecycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1900_date_lifecycle.sql",
      "read_script": "generator/spark-reads-df/verify_1900_date_lifecycle.py",
      "description": "Date functions across full DML lifecycle (INSERT, UPDATE, DELETE, MERGE).",
      "status": "pass",
      "duration_ms": 4109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:41.719287+00:00",
      "read_cold_ms": 2368,
      "read_warm_ms": 796,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 243,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1901_ctas_basic",
      "num": 1901,
      "name": "ctas_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1901_ctas_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1901_ctas_basic.py",
      "description": "CREATE TABLE AS SELECT (CTAS) with simple types.",
      "status": "pass",
      "duration_ms": 4050,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:45.770144+00:00",
      "read_cold_ms": 2330,
      "read_warm_ms": 439,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 23,
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        "type:integer",
        "type:string",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1902_ctas_typed",
      "num": 1902,
      "name": "ctas_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1902_ctas_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1902_ctas_typed.py",
      "description": "CTAS with multiple column types (BIGINT, STRING, INT, DECIMAL).",
      "status": "pass",
      "duration_ms": 4296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:50.067228+00:00",
      "read_cold_ms": 2215,
      "read_warm_ms": 504,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 25,
      "write_warm_ms": 23,
      "tags": [
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        "type:integer",
        "type:string",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1903_ctas_partitioned",
      "num": 1903,
      "name": "ctas_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1903_ctas_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_1903_ctas_partitioned.py",
      "description": "CTAS into partitioned table (partitioned by region).",
      "status": "pass",
      "duration_ms": 3142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:53.210292+00:00",
      "read_cold_ms": 2059,
      "read_warm_ms": 501,
      "skip_feature": null,
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      "write_cold_ms": 35,
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      "tags": [
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        "type:string",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1904_ctas_with_aggregation",
      "num": 1904,
      "name": "ctas_with_aggregation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1904_ctas_with_aggregation.sql",
      "read_script": "generator/spark-reads-df/verify_1904_ctas_with_aggregation.py",
      "description": "CTAS from an aggregated (GROUP BY) query.",
      "status": "pass",
      "duration_ms": 2919,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:04:56.130827+00:00",
      "read_cold_ms": 1796,
      "read_warm_ms": 532,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 28,
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      "tags": [
        "type:integer",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1905_ctas_then_dml",
      "num": 1905,
      "name": "ctas_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1905_ctas_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1905_ctas_then_dml.py",
      "description": "CTAS followed by subsequent INSERT and UPDATE DML.",
      "status": "pass",
      "duration_ms": 5465,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:01.596886+00:00",
      "read_cold_ms": 2753,
      "read_warm_ms": 785,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 204,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1906_truncate_basic",
      "num": 1906,
      "name": "truncate_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1906_truncate_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1906_truncate_basic.py",
      "description": "TRUNCATE TABLE removing all rows, then re-insert.",
      "status": "pass",
      "duration_ms": 3671,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:05.269558+00:00",
      "read_cold_ms": 2178,
      "read_warm_ms": 491,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 136,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1907_truncate_typed",
      "num": 1907,
      "name": "truncate_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1907_truncate_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1907_truncate_typed.py",
      "description": "TRUNCATE TABLE on a table with multiple types.",
      "status": "pass",
      "duration_ms": 3988,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:09.259751+00:00",
      "read_cold_ms": 2245,
      "read_warm_ms": 626,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
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      "tags": [
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        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1908_truncate_then_zorder",
      "num": 1908,
      "name": "truncate_then_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1908_truncate_then_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1908_truncate_then_zorder.py",
      "description": "TRUNCATE, multi-batch re-insert, then OPTIMIZE ZORDER BY.",
      "status": "pass",
      "duration_ms": 3730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:12.990850+00:00",
      "read_cold_ms": 1892,
      "read_warm_ms": 771,
      "skip_feature": null,
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      "write_cold_ms": 261,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1909_truncate_partition",
      "num": 1909,
      "name": "truncate_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1909_truncate_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1909_truncate_partition.py",
      "description": "TRUNCATE on a partitioned table.",
      "status": "pass",
      "duration_ms": 3384,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:16.376592+00:00",
      "read_cold_ms": 1890,
      "read_warm_ms": 566,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/190_corrupt_log",
      "num": 190,
      "name": "corrupt_log",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/190_corrupt_log.sql",
      "read_script": "generator/spark-reads-df/verify_190_corrupt_log.py",
      "description": "Corrupt transaction log recovery testing Multiple versions with checkpoint triggers Deletion vectors enabled",
      "status": "pass",
      "duration_ms": 1426,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:33:59.612417+00:00",
      "read_cold_ms": 817,
      "read_warm_ms": 269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 475,
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      "tags": [
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        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1910_truncate_cdc",
      "num": 1910,
      "name": "truncate_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1910_truncate_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1910_truncate_cdc.py",
      "description": "TRUNCATE on a CDC-enabled table. CDF should capture the",
      "status": "pass",
      "duration_ms": 3485,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:19.862508+00:00",
      "read_cold_ms": 1773,
      "read_warm_ms": 566,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 67,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1911_set_tblproperties_cdc",
      "num": 1911,
      "name": "set_tblproperties_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1911_set_tblproperties_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1911_set_tblproperties_cdc.py",
      "description": "Enable CDC mid-life via ALTER TABLE SET TBLPROPERTIES.",
      "status": "pass",
      "duration_ms": 3313,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:23.177134+00:00",
      "read_cold_ms": 1331,
      "read_warm_ms": 607,
      "skip_feature": null,
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      "write_cold_ms": 157,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1912_set_tblproperties_dv",
      "num": 1912,
      "name": "set_tblproperties_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1912_set_tblproperties_dv.sql",
      "read_script": "generator/spark-reads-df/verify_1912_set_tblproperties_dv.py",
      "description": "Enable deletion vectors mid-life via ALTER TABLE SET TBLPROPERTIES.",
      "status": "pass",
      "duration_ms": 4338,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:27.516785+00:00",
      "read_cold_ms": 2433,
      "read_warm_ms": 756,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 141,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1913_set_tblproperties_appendonly",
      "num": 1913,
      "name": "set_tblproperties_appendonly",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1913_set_tblproperties_appendonly.sql",
      "read_script": "generator/spark-reads-df/verify_1913_set_tblproperties_appendonly.py",
      "description": "Enable delta.appendOnly mid-life. After SET, UPDATE/DELETE",
      "status": "pass",
      "duration_ms": 3668,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:31.186828+00:00",
      "read_cold_ms": 2104,
      "read_warm_ms": 442,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 80,
      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1914_unset_tblproperties",
      "num": 1914,
      "name": "unset_tblproperties",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1914_unset_tblproperties.sql",
      "read_script": "generator/spark-reads-df/verify_1914_unset_tblproperties.py",
      "description": "ALTER TABLE UNSET TBLPROPERTIES. Removing a property mid-life.",
      "status": "pass",
      "duration_ms": 3485,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:34.673452+00:00",
      "read_cold_ms": 1801,
      "read_warm_ms": 528,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 109,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1915_alter_column_int_to_bigint",
      "num": 1915,
      "name": "alter_column_int_to_bigint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1915_alter_column_int_to_bigint.sql",
      "read_script": "generator/spark-reads-df/verify_1915_alter_column_int_to_bigint.py",
      "description": "Type widening via ALTER TABLE ... CHANGE COLUMN val val BIGINT.",
      "status": "pass",
      "duration_ms": 3929,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:38.604971+00:00",
      "read_cold_ms": 2413,
      "read_warm_ms": 645,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1916_alter_column_int_to_long",
      "num": 1916,
      "name": "alter_column_int_to_long",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1916_alter_column_int_to_long.sql",
      "read_script": "generator/spark-reads-df/verify_1916_alter_column_int_to_long.py",
      "description": "Type widening using ALTER COLUMN ... SET DATA TYPE syntax.",
      "status": "pass",
      "duration_ms": 3683,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:42.290461+00:00",
      "read_cold_ms": 2359,
      "read_warm_ms": 513,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1917_drop_constraint_then_violate",
      "num": 1917,
      "name": "drop_constraint_then_violate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1917_drop_constraint_then_violate.sql",
      "read_script": "generator/spark-reads-df/verify_1917_drop_constraint_then_violate.py",
      "description": "ADD CONSTRAINT, INSERT valid, DROP CONSTRAINT, INSERT",
      "status": "pass",
      "duration_ms": 3724,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:46.015767+00:00",
      "read_cold_ms": 2276,
      "read_warm_ms": 344,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 203,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1918_set_tblproperties_partition",
      "num": 1918,
      "name": "set_tblproperties_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1918_set_tblproperties_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1918_set_tblproperties_partition.py",
      "description": "SET TBLPROPERTIES on a partitioned table mid-life.",
      "status": "pass",
      "duration_ms": 2972,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:48.989448+00:00",
      "read_cold_ms": 1874,
      "read_warm_ms": 429,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 332,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1919_set_then_unset",
      "num": 1919,
      "name": "set_then_unset",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1919_set_then_unset.sql",
      "read_script": "generator/spark-reads-df/verify_1919_set_then_unset.py",
      "description": "SET followed by UNSET TBLPROPERTIES in sequence.",
      "status": "pass",
      "duration_ms": 3819,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:52.809323+00:00",
      "read_cold_ms": 1667,
      "read_warm_ms": 551,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 192,
      "write_warm_ms": 241,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/191_partial_checkpoint",
      "num": 191,
      "name": "partial_checkpoint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/191_partial_checkpoint.sql",
      "read_script": "generator/spark-reads-df/verify_191_partial_checkpoint.py",
      "description": "Partial checkpoint write handling and recovery CATEGORIES = [\"electronics\", \"clothing\", \"home\", \"sports\", \"books\", \"toys\"]",
      "status": "pass",
      "duration_ms": 7136,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:06.749666+00:00",
      "read_cold_ms": 909,
      "read_warm_ms": 307,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 398,
      "write_warm_ms": 320,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1920_alter_column_widen_with_dml",
      "num": 1920,
      "name": "alter_column_widen_with_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1920_alter_column_widen_with_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1920_alter_column_widen_with_dml.py",
      "description": "ALTER COLUMN type widening followed by UPDATE/DELETE",
      "status": "pass",
      "duration_ms": 4522,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:05:57.332410+00:00",
      "read_cold_ms": 2266,
      "read_warm_ms": 1199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 235,
      "write_warm_ms": 210,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1921_alter_column_widen_partition",
      "num": 1921,
      "name": "alter_column_widen_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1921_alter_column_widen_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1921_alter_column_widen_partition.py",
      "description": "ALTER COLUMN type widening on a partitioned table.",
      "status": "pass",
      "duration_ms": 3568,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:00.901770+00:00",
      "read_cold_ms": 1929,
      "read_warm_ms": 509,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 76,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1922_drop_constraint_chain",
      "num": 1922,
      "name": "drop_constraint_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1922_drop_constraint_chain.sql",
      "read_script": "generator/spark-reads-df/verify_1922_drop_constraint_chain.py",
      "description": "Multiple constraints, sequential DROP CONSTRAINT calls.",
      "status": "pass",
      "duration_ms": 3329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:04.232247+00:00",
      "read_cold_ms": 1793,
      "read_warm_ms": 537,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 294,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1923_set_tblproperties_multiple",
      "num": 1923,
      "name": "set_tblproperties_multiple",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1923_set_tblproperties_multiple.sql",
      "read_script": "generator/spark-reads-df/verify_1923_set_tblproperties_multiple.py",
      "description": "Setting multiple properties in a single ALTER TABLE.",
      "status": "pass",
      "duration_ms": 3624,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:07.857651+00:00",
      "read_cold_ms": 1911,
      "read_warm_ms": 491,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1924_ctas_with_constraint",
      "num": 1924,
      "name": "ctas_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1924_ctas_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1924_ctas_with_constraint.py",
      "description": "CTAS followed by adding a CHECK constraint and more inserts.",
      "status": "pass",
      "duration_ms": 3630,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:11.488282+00:00",
      "read_cold_ms": 1873,
      "read_warm_ms": 723,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 203,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1925_ddl_lifecycle",
      "num": 1925,
      "name": "ddl_lifecycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1925_ddl_lifecycle.sql",
      "read_script": "generator/spark-reads-df/verify_1925_ddl_lifecycle.py",
      "description": "Full DDL lifecycle in one table: CTAS + ADD CONSTRAINT",
      "status": "pass",
      "duration_ms": 5149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:16.638026+00:00",
      "read_cold_ms": 2350,
      "read_warm_ms": 725,
      "skip_feature": null,
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      "write_cold_ms": 235,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1926_insert_zero_rows",
      "num": 1926,
      "name": "insert_zero_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1926_insert_zero_rows.sql",
      "read_script": "generator/spark-reads-df/verify_1926_insert_zero_rows.py",
      "description": "INSERT statement that produces zero rows via WHERE clause.",
      "status": "pass",
      "duration_ms": 2625,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:19.263955+00:00",
      "read_cold_ms": 1597,
      "read_warm_ms": 545,
      "skip_feature": null,
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      "write_cold_ms": 31,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1927_insert_one_row",
      "num": 1927,
      "name": "insert_one_row",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1927_insert_one_row.sql",
      "read_script": "generator/spark-reads-df/verify_1927_insert_one_row.py",
      "description": "Single-row INSERT, then full DML lifecycle against 1 row.",
      "status": "pass",
      "duration_ms": 4738,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:24.003497+00:00",
      "read_cold_ms": 2195,
      "read_warm_ms": 655,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
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      "tags": [
        "type:integer",
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        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1928_insert_two_rows",
      "num": 1928,
      "name": "insert_two_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1928_insert_two_rows.sql",
      "read_script": "generator/spark-reads-df/verify_1928_insert_two_rows.py",
      "description": "Exactly 2-row INSERT, UPDATE 1, DELETE 1, then INSERT 1.",
      "status": "pass",
      "duration_ms": 4731,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:28.735185+00:00",
      "read_cold_ms": 1957,
      "read_warm_ms": 902,
      "skip_feature": null,
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      "write_cold_ms": 147,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1929_insert_then_zero_match_dml",
      "num": 1929,
      "name": "insert_then_zero_match_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1929_insert_then_zero_match_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1929_insert_then_zero_match_dml.py",
      "description": "INSERT then UPDATE/DELETE/MERGE with zero matches.",
      "status": "pass",
      "duration_ms": 3274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:32.010274+00:00",
      "read_cold_ms": 1822,
      "read_warm_ms": 662,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 83,
      "tags": [
        "type:integer",
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        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/192_clock_skew",
      "num": 192,
      "name": "clock_skew",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/192_clock_skew.sql",
      "read_script": "generator/spark-reads-df/verify_192_clock_skew.py",
      "description": "Clock skew handling in commit logs Create table with deletion vectors enabled",
      "status": "pass",
      "duration_ms": 4743,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:11.492996+00:00",
      "read_cold_ms": 782,
      "read_warm_ms": 164,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 349,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1930_insert_max_int_count",
      "num": 1930,
      "name": "insert_max_int_count",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1930_insert_max_int_count.sql",
      "read_script": "generator/spark-reads-df/verify_1930_insert_max_int_count.py",
      "description": "INSERT exactly 256 rows (power-of-2 boundary).",
      "status": "pass",
      "duration_ms": 3906,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:35.917860+00:00",
      "read_cold_ms": 2488,
      "read_warm_ms": 782,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1931_insert_512_rows",
      "num": 1931,
      "name": "insert_512_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1931_insert_512_rows.sql",
      "read_script": "generator/spark-reads-df/verify_1931_insert_512_rows.py",
      "description": "INSERT exactly 512 rows (power-of-2 boundary).",
      "status": "pass",
      "duration_ms": 3764,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:39.682461+00:00",
      "read_cold_ms": 2158,
      "read_warm_ms": 727,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1932_insert_1024_rows",
      "num": 1932,
      "name": "insert_1024_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1932_insert_1024_rows.sql",
      "read_script": "generator/spark-reads-df/verify_1932_insert_1024_rows.py",
      "description": "INSERT exactly 1024 rows (power-of-2 boundary).",
      "status": "pass",
      "duration_ms": 3055,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:42.737940+00:00",
      "read_cold_ms": 1824,
      "read_warm_ms": 501,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1933_insert_4096_rows",
      "num": 1933,
      "name": "insert_4096_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1933_insert_4096_rows.py",
      "description": "INSERT exactly 4096 rows (page size boundary).",
      "status": "pass",
      "duration_ms": 2664,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:45.402843+00:00",
      "read_cold_ms": 1729,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1934_insert_8191_rows",
      "num": 1934,
      "name": "insert_8191_rows",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1934_insert_8191_rows.py",
      "description": "INSERT exactly 8191 rows (just under page boundary 8192).",
      "status": "pass",
      "duration_ms": 3344,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:48.747861+00:00",
      "read_cold_ms": 2240,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1935_insert_8192_rows",
      "num": 1935,
      "name": "insert_8192_rows",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT exactly 8192 rows (exact page boundary).",
      "status": "pass",
      "duration_ms": 3008,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:51.756812+00:00",
      "read_cold_ms": 1969,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1936_dv_max_size",
      "num": 1936,
      "name": "dv_max_size",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1936_dv_max_size.sql",
      "read_script": "generator/spark-reads-df/verify_1936_dv_max_size.py",
      "description": "Large DELETE with deletion vectors -- tests DV bitmap",
      "status": "pass",
      "duration_ms": 4151,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:06:55.909006+00:00",
      "read_cold_ms": 2251,
      "read_warm_ms": 786,
      "skip_feature": null,
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      "write_cold_ms": 56,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1937_pushdown_proof_int",
      "num": 1937,
      "name": "pushdown_proof_int",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1937_pushdown_proof_int.py",
      "description": "Predicate pushdown proof on an INT column.",
      "status": "pass",
      "duration_ms": 4430,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:00.342224+00:00",
      "read_cold_ms": 2145,
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      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "type:string",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1938_pushdown_proof_decimal",
      "num": 1938,
      "name": "pushdown_proof_decimal",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1938_pushdown_proof_decimal.py",
      "description": "Predicate pushdown proof on a DECIMAL column.",
      "status": "pass",
      "duration_ms": 3831,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:04.174579+00:00",
      "read_cold_ms": 1768,
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      "skip_feature": null,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1939_explain_after_insert",
      "num": 1939,
      "name": "explain_after_insert",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_1939_explain_after_insert.py",
      "description": "Table for df.filter().explain() verification after multi-batch insert.",
      "status": "pass",
      "duration_ms": 4557,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:08.732840+00:00",
      "read_cold_ms": 2094,
      "read_warm_ms": 540,
      "skip_feature": null,
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      "write_cold_ms": 129,
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        "type:string",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/193_file_atomicity",
      "num": 193,
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/193_file_atomicity.sql",
      "read_script": "generator/spark-reads-df/verify_193_file_atomicity.py",
      "description": "File rename atomicity testing amount = (100 + ((i * 83) % 99901)) / 100.0",
      "status": "pass",
      "duration_ms": 4809,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:16.302501+00:00",
      "read_cold_ms": 744,
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      "skip_feature": null,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1940_files_read_metric",
      "num": 1940,
      "name": "files_read_metric",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1940_files_read_metric.sql",
      "read_script": "generator/spark-reads-df/verify_1940_files_read_metric.py",
      "description": "File skipping by Delta statistics. 5 disjoint score batches",
      "status": "pass",
      "duration_ms": 3604,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:12.338136+00:00",
      "read_cold_ms": 1953,
      "read_warm_ms": 469,
      "skip_feature": null,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1941_parquet_direct_read",
      "num": 1941,
      "name": "parquet_direct_read",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1941_parquet_direct_read.sql",
      "read_script": "generator/spark-reads-df/verify_1941_parquet_direct_read.py",
      "description": "Table where verification script reads the underlying Parquet",
      "status": "pass",
      "duration_ms": 3560,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:15.898981+00:00",
      "read_cold_ms": 2114,
      "read_warm_ms": 485,
      "skip_feature": null,
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      "write_cold_ms": 46,
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        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1942_zero_row_after_delete",
      "num": 1942,
      "name": "zero_row_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1942_zero_row_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_1942_zero_row_after_delete.py",
      "description": "DELETE all rows, then verify 0 rows are readable.",
      "status": "pass",
      "duration_ms": 5039,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:20.939383+00:00",
      "read_cold_ms": 3392,
      "read_warm_ms": 878,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
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      "tags": [
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        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1943_one_row_per_batch",
      "num": 1943,
      "name": "one_row_per_batch",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1943_one_row_per_batch.sql",
      "read_script": "generator/spark-reads-df/verify_1943_one_row_per_batch.py",
      "description": "Extreme fragmentation -- 100 sequential INSERT batches",
      "status": "pass",
      "duration_ms": 4303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:25.243595+00:00",
      "read_cold_ms": 2350,
      "read_warm_ms": 679,
      "skip_feature": null,
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      "write_cold_ms": 12659,
      "write_warm_ms": 12217,
      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1944_50_batches_typed",
      "num": 1944,
      "name": "50_batches_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1944_50_batches_typed.sql",
      "read_script": "generator/spark-reads-df/verify_1944_50_batches_typed.py",
      "description": "50 INSERT batches of 10 rows each with multiple types.",
      "status": "pass",
      "duration_ms": 3732,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:28.977412+00:00",
      "read_cold_ms": 2514,
      "read_warm_ms": 568,
      "skip_feature": null,
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      "tags": [
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        "scale:many-batches",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/1945_verify_pushed_filters",
      "num": 1945,
      "name": "verify_pushed_filters",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1945_verify_pushed_filters.sql",
      "read_script": "generator/spark-reads-df/verify_1945_verify_pushed_filters.py",
      "description": "Table for PushedFilters plan-text verification.",
      "status": "pass",
      "duration_ms": 4828,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:33.807603+00:00",
      "read_cold_ms": 2482,
      "read_warm_ms": 635,
      "skip_feature": null,
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      "write_cold_ms": 105,
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        "dml:insert",
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        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1946_verify_files_skipped",
      "num": 1946,
      "name": "verify_files_skipped",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1946_verify_files_skipped.sql",
      "read_script": "generator/spark-reads-df/verify_1946_verify_files_skipped.py",
      "description": "File skipping reduces the number of files read.",
      "status": "pass",
      "duration_ms": 5830,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:39.639325+00:00",
      "read_cold_ms": 2379,
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      "skip_feature": null,
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      "write_cold_ms": 140,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1947_verify_format_v2",
      "num": 1947,
      "name": "verify_format_v2",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1947_verify_format_v2.sql",
      "read_script": "generator/spark-reads-df/verify_1947_verify_format_v2.py",
      "description": "Table that uses Delta v2 features (deletion vectors).",
      "status": "pass",
      "duration_ms": 5617,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:45.258146+00:00",
      "read_cold_ms": 3556,
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      "skip_feature": null,
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      "write_cold_ms": 50,
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      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/1948_long_table_history",
      "num": 1948,
      "name": "long_table_history",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1948_long_table_history.sql",
      "read_script": "generator/spark-reads-df/verify_1948_long_table_history.py",
      "description": "30+ versions for history depth testing. Verification script",
      "status": "pass",
      "duration_ms": 8169,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:07:53.430044+00:00",
      "read_cold_ms": 3013,
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      "skip_feature": null,
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      "write_cold_ms": 2356,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1949_alternating_dml",
      "num": 1949,
      "name": "alternating_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1949_alternating_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1949_alternating_dml.py",
      "description": "Alternating DML pattern (INSERT, UPDATE, INSERT, UPDATE,",
      "status": "pass",
      "duration_ms": 6836,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:00.270193+00:00",
      "read_cold_ms": 3681,
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      "skip_feature": null,
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      "write_cold_ms": 4778,
      "write_warm_ms": 4160,
      "tags": [
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        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/194_partitioned_write",
      "num": 194,
      "name": "partitioned_write",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/194_partitioned_write.sql",
      "read_script": "generator/spark-reads-df/verify_194_partitioned_write.py",
      "description": "Partitioned table with Hive-style partitioning PARTITIONED BY (region) 50 rows distributed across us-east, us-west, eu-west BASE_TIMESTAMP = 1717200000000000 (2024-06-01T00:00:00Z) REGIONS = [\"us-east\", \"us-west\", \"eu-west\"]",
      "status": "pass",
      "duration_ms": 4151,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:20.454025+00:00",
      "read_cold_ms": 753,
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      "skip_feature": null,
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      "write_cold_ms": 36,
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      "tags": [
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        "dml:insert",
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        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1950_ultimate_size_test",
      "num": 1950,
      "name": "ultimate_size_test",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1950_ultimate_size_test.sql",
      "read_script": "generator/spark-reads-df/verify_1950_ultimate_size_test.py",
      "description": "Ultimate stress test combining large INSERT, UPDATE, DELETE,",
      "status": "pass",
      "duration_ms": 4393,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:04.664413+00:00",
      "read_cold_ms": 2749,
      "read_warm_ms": 841,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 341,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1951_ict_enable_basic",
      "num": 1951,
      "name": "ict_enable_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1951_ict_enable_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1951_ict_enable_basic.py",
      "description": "ICT enabled at table creation, basic INSERT.",
      "status": "pass",
      "duration_ms": 2851,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:07.516208+00:00",
      "read_cold_ms": 1785,
      "read_warm_ms": 545,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 24,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1952_ict_monotonic_after_dml",
      "num": 1952,
      "name": "ict_monotonic_after_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1952_ict_monotonic_after_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1952_ict_monotonic_after_dml.py",
      "description": "ICT timestamps strictly increase across INSERT, UPDATE, DELETE.",
      "status": "pass",
      "duration_ms": 4319,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:11.836295+00:00",
      "read_cold_ms": 2647,
      "read_warm_ms": 893,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1953_ict_with_partition",
      "num": 1953,
      "name": "ict_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1953_ict_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1953_ict_with_partition.py",
      "description": "ICT + PARTITIONED BY region (4 partitions).",
      "status": "pass",
      "duration_ms": 3111,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:14.948216+00:00",
      "read_cold_ms": 1995,
      "read_warm_ms": 400,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 58,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1954_ict_with_cdc",
      "num": 1954,
      "name": "ict_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1954_ict_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1954_ict_with_cdc.py",
      "description": "ICT + Change Data Feed enabled.",
      "status": "pass",
      "duration_ms": 7409,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:22.359278+00:00",
      "read_cold_ms": 3135,
      "read_warm_ms": 1365,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 295,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1955_ict_after_optimize",
      "num": 1955,
      "name": "ict_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1955_ict_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_1955_ict_after_optimize.py",
      "description": "ICT preserved across OPTIMIZE compaction.",
      "status": "pass",
      "duration_ms": 3350,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:25.711947+00:00",
      "read_cold_ms": 1978,
      "read_warm_ms": 605,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 476,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1956_ict_with_dv",
      "num": 1956,
      "name": "ict_with_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1956_ict_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_1956_ict_with_dv.py",
      "description": "ICT + Deletion Vectors with predicate DELETE.",
      "status": "pass",
      "duration_ms": 4661,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:30.375172+00:00",
      "read_cold_ms": 2762,
      "read_warm_ms": 940,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1957_ict_time_travel_by_timestamp",
      "num": 1957,
      "name": "ict_time_travel_by_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1957_ict_time_travel_by_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_1957_ict_time_travel_by_timestamp.py",
      "description": "ICT enables time travel by timestamp readability.",
      "status": "pass",
      "duration_ms": 4645,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:35.022119+00:00",
      "read_cold_ms": 2143,
      "read_warm_ms": 507,
      "skip_feature": null,
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      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1958_ict_after_restore",
      "num": 1958,
      "name": "ict_after_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1958_ict_after_restore.sql",
      "read_script": "generator/spark-reads-df/verify_1958_ict_after_restore.py",
      "description": "ICT + RESTORE. Verify ICT preserved after restore.",
      "status": "pass",
      "duration_ms": 3397,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:38.420215+00:00",
      "read_cold_ms": 2128,
      "read_warm_ms": 567,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1959_rowtrack_enable_basic",
      "num": 1959,
      "name": "rowtrack_enable_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1959_rowtrack_enable_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1959_rowtrack_enable_basic.py",
      "description": "Row tracking enabled, basic INSERT.",
      "status": "pass",
      "duration_ms": 2669,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:41.090223+00:00",
      "read_cold_ms": 1739,
      "read_warm_ms": 525,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/195_partition_pruning",
      "num": 195,
      "name": "partition_pruning",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/195_partition_pruning.sql",
      "read_script": "generator/spark-reads-df/verify_195_partition_pruning.py",
      "description": "Multi-partition table with 12 AWS-style regions PARTITIONED BY (region) 120 rows (10 per partition) BASE_TIMESTAMP = 1704067200000000 (2024-01-01T00:00:00Z) REGIONS = 12 AWS-style regions",
      "status": "pass",
      "duration_ms": 6399,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:26.854192+00:00",
      "read_cold_ms": 598,
      "read_warm_ms": 146,
      "skip_feature": null,
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      "write_cold_ms": 156,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1960_rowtrack_after_update",
      "num": 1960,
      "name": "rowtrack_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1960_rowtrack_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_1960_rowtrack_after_update.py",
      "description": "Row tracking + UPDATE preserves row IDs.",
      "status": "pass",
      "duration_ms": 3912,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:45.003490+00:00",
      "read_cold_ms": 2247,
      "read_warm_ms": 876,
      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:insert",
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        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1961_rowtrack_after_merge",
      "num": 1961,
      "name": "rowtrack_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1961_rowtrack_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_1961_rowtrack_after_merge.py",
      "description": "Row tracking + MERGE inserting 20 new rows.",
      "status": "pass",
      "duration_ms": 2889,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:47.893458+00:00",
      "read_cold_ms": 1836,
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      "skip_feature": null,
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      "write_cold_ms": 131,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1962_rowtrack_with_optimize",
      "num": 1962,
      "name": "rowtrack_with_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1962_rowtrack_with_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_1962_rowtrack_with_optimize.py",
      "description": "Row tracking + OPTIMIZE preserves row IDs.",
      "status": "pass",
      "duration_ms": 3075,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:50.969523+00:00",
      "read_cold_ms": 1983,
      "read_warm_ms": 508,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 314,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1963_rowtrack_with_dv",
      "num": 1963,
      "name": "rowtrack_with_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1963_rowtrack_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_1963_rowtrack_with_dv.py",
      "description": "Row tracking + DV with predicate DELETE.",
      "status": "pass",
      "duration_ms": 4309,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:08:55.279478+00:00",
      "read_cold_ms": 2090,
      "read_warm_ms": 986,
      "skip_feature": null,
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      "write_cold_ms": 53,
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      "tags": [
        "type:integer",
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        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1964_rowtrack_partition",
      "num": 1964,
      "name": "rowtrack_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1964_rowtrack_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1964_rowtrack_partition.py",
      "description": "Row tracking + PARTITIONED BY (4 partitions).",
      "status": "pass",
      "duration_ms": 4938,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:00.218198+00:00",
      "read_cold_ms": 2954,
      "read_warm_ms": 668,
      "skip_feature": null,
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      "write_cold_ms": 119,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1965_v2_checkpoint_basic",
      "num": 1965,
      "name": "v2_checkpoint_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1965_v2_checkpoint_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1965_v2_checkpoint_basic.py",
      "description": "V2 checkpoint policy with 10 small INSERTs of 10.",
      "status": "pass",
      "duration_ms": 5175,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:05.394611+00:00",
      "read_cold_ms": 3933,
      "read_warm_ms": 636,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 606,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1966_v2_checkpoint_multi_sidecar",
      "num": 1966,
      "name": "v2_checkpoint_multi_sidecar",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1966_v2_checkpoint_multi_sidecar.sql",
      "read_script": "generator/spark-reads-df/verify_1966_v2_checkpoint_multi_sidecar.py",
      "description": "V2 checkpoint with 20 small INSERTs to encourage multi-sidecar.",
      "status": "pass",
      "duration_ms": 3600,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:08.995534+00:00",
      "read_cold_ms": 2331,
      "read_warm_ms": 551,
      "skip_feature": null,
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      "write_cold_ms": 1678,
      "write_warm_ms": 1399,
      "tags": [
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        "type:string",
        "dml:insert",
        "delta:checkpoint-sidecar",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1967_v2_checkpoint_after_vacuum",
      "num": 1967,
      "name": "v2_checkpoint_after_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1967_v2_checkpoint_after_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_1967_v2_checkpoint_after_vacuum.py",
      "description": "V2 checkpoint + INSERT/UPDATE/DELETE + VACUUM RETAIN 0.",
      "status": "pass",
      "duration_ms": 4876,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:13.872836+00:00",
      "read_cold_ms": 3178,
      "read_warm_ms": 915,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
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      "tags": [
        "type:integer",
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        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1968_v2_checkpoint_after_optimize",
      "num": 1968,
      "name": "v2_checkpoint_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1968_v2_checkpoint_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_1968_v2_checkpoint_after_optimize.py",
      "description": "V2 checkpoint + 8 INSERTs + OPTIMIZE compaction.",
      "status": "pass",
      "duration_ms": 3943,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:17.817213+00:00",
      "read_cold_ms": 2364,
      "read_warm_ms": 904,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 698,
      "write_warm_ms": 481,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1969_v2_checkpoint_uuid_naming",
      "num": 1969,
      "name": "v2_checkpoint_uuid_naming",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1969_v2_checkpoint_uuid_naming.sql",
      "read_script": "generator/spark-reads-df/verify_1969_v2_checkpoint_uuid_naming.py",
      "description": "V2 checkpoint files use UUID naming convention.",
      "status": "pass",
      "duration_ms": 3403,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:21.221997+00:00",
      "read_cold_ms": 2087,
      "read_warm_ms": 825,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 767,
      "write_warm_ms": 867,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/196_statistics_roundtrip",
      "num": 196,
      "name": "statistics_roundtrip",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/196_statistics_roundtrip.sql",
      "read_script": "generator/spark-reads-df/verify_196_statistics_roundtrip.py",
      "description": "CHAOS TEST WORKFLOW (Statistics and Data Skipping Interop): This test verifies that DeltaForge correctly writes statistics that DBX can use for data skipping (file pruning). Critical for query performance.",
      "status": "pass",
      "duration_ms": 7008,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:33.863525+00:00",
      "read_cold_ms": 853,
      "read_warm_ms": 200,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 51,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1970_drop_col_basic_colmap",
      "num": 1970,
      "name": "drop_col_basic_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1970_drop_col_basic_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_1970_drop_col_basic_colmap.py",
      "description": "Basic ALTER TABLE DROP COLUMN with column mapping enabled.",
      "status": "pass",
      "duration_ms": 3225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:24.448497+00:00",
      "read_cold_ms": 2327,
      "read_warm_ms": 467,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 94,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1971_drop_col_then_dml",
      "num": 1971,
      "name": "drop_col_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1971_drop_col_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1971_drop_col_then_dml.py",
      "description": "DROP COLUMN followed by UPDATE and DELETE on remaining columns.",
      "status": "pass",
      "duration_ms": 4559,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:29.008728+00:00",
      "read_cold_ms": 2617,
      "read_warm_ms": 855,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1972_drop_col_then_optimize",
      "num": 1972,
      "name": "drop_col_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1972_drop_col_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_1972_drop_col_then_optimize.py",
      "description": "DROP COLUMN after multiple INSERT batches, then OPTIMIZE.",
      "status": "pass",
      "duration_ms": 4148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:33.157631+00:00",
      "read_cold_ms": 2324,
      "read_warm_ms": 704,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 507,
      "write_warm_ms": 340,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1973_drop_col_then_zorder",
      "num": 1973,
      "name": "drop_col_then_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1973_drop_col_then_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1973_drop_col_then_zorder.py",
      "description": "DROP COLUMN followed by OPTIMIZE ZORDER BY remaining column.",
      "status": "pass",
      "duration_ms": 4198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:37.358386+00:00",
      "read_cold_ms": 3220,
      "read_warm_ms": 566,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1974_drop_col_with_cdc",
      "num": 1974,
      "name": "drop_col_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1974_drop_col_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1974_drop_col_with_cdc.py",
      "description": "DROP COLUMN on table with CDF enabled, then more INSERTs.",
      "status": "pass",
      "duration_ms": 3739,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:41.100016+00:00",
      "read_cold_ms": 2490,
      "read_warm_ms": 555,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 236,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1975_drop_col_partitioned",
      "num": 1975,
      "name": "drop_col_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1975_drop_col_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_1975_drop_col_partitioned.py",
      "description": "DROP COLUMN on a partitioned table (drop a non-partition column).",
      "status": "pass",
      "duration_ms": 2957,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:44.059500+00:00",
      "read_cold_ms": 1946,
      "read_warm_ms": 484,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 124,
      "write_warm_ms": 106,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1976_drop_col_chain_three",
      "num": 1976,
      "name": "drop_col_chain_three",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1976_drop_col_chain_three.sql",
      "read_script": "generator/spark-reads-df/verify_1976_drop_col_chain_three.py",
      "description": "Three sequential DROP COLUMN operations.",
      "status": "pass",
      "duration_ms": 3029,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:47.091354+00:00",
      "read_cold_ms": 1813,
      "read_warm_ms": 739,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1977_drop_col_then_add_same_name",
      "num": 1977,
      "name": "drop_col_then_add_same_name",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1977_drop_col_then_add_same_name.sql",
      "read_script": "generator/spark-reads-df/verify_1977_drop_col_then_add_same_name.py",
      "description": "DROP a column then ADD it back with the same name (different physical id).",
      "status": "pass",
      "duration_ms": 4116,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:51.208866+00:00",
      "read_cold_ms": 2718,
      "read_warm_ms": 483,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 154,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1978_gencol_basic_arith",
      "num": 1978,
      "name": "gencol_basic_arith",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1978_gencol_basic_arith.sql",
      "read_script": "generator/spark-reads-df/verify_1978_gencol_basic_arith.py",
      "description": "Basic arithmetic generated column c = a + b.",
      "status": "pass",
      "duration_ms": 3381,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:54.591326+00:00",
      "read_cold_ms": 2336,
      "read_warm_ms": 464,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1979_gencol_string_concat",
      "num": 1979,
      "name": "gencol_string_concat",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1979_gencol_string_concat.sql",
      "read_script": "generator/spark-reads-df/verify_1979_gencol_string_concat.py",
      "description": "String generated column full = CONCAT(first, ' ', last).",
      "status": "pass",
      "duration_ms": 3228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:09:57.821228+00:00",
      "read_cold_ms": 2297,
      "read_warm_ms": 522,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/197_nested_schema_modify",
      "num": 197,
      "name": "nested_schema_modify",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/197_nested_schema_modify.sql",
      "read_script": "generator/spark-reads-df/verify_197_nested_schema_modify.py",
      "description": "CHAOS TEST WORKFLOW (Nested Schema UPDATE/DELETE/MERGE): This test verifies DeltaForge can correctly modify data in deeply nested struct fields. Critical for real-world schemas with complex structures.",
      "status": "pass",
      "duration_ms": 10273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:44.136980+00:00",
      "read_cold_ms": 664,
      "read_warm_ms": 153,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 27,
      "write_warm_ms": 30,
      "tags": [
        "type:array",
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1980_gencol_date_extract",
      "num": 1980,
      "name": "gencol_date_extract",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1980_gencol_date_extract.sql",
      "read_script": "generator/spark-reads-df/verify_1980_gencol_date_extract.py",
      "description": "Generated column extracting YEAR from a TIMESTAMP.",
      "status": "pass",
      "duration_ms": 3511,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:01.333896+00:00",
      "read_cold_ms": 2406,
      "read_warm_ms": 350,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 229,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1981_gencol_after_update",
      "num": 1981,
      "name": "gencol_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1981_gencol_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_1981_gencol_after_update.py",
      "description": "Generated column auto-recomputed after UPDATE on base column.",
      "status": "pass",
      "duration_ms": 4018,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:05.353182+00:00",
      "read_cold_ms": 2231,
      "read_warm_ms": 971,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 324,
      "write_warm_ms": 231,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1982_gencol_partition_key",
      "num": 1982,
      "name": "gencol_partition_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1982_gencol_partition_key.sql",
      "read_script": "generator/spark-reads-df/verify_1982_gencol_partition_key.py",
      "description": "Generated column used as a partition key (id_bucket = id % 6).",
      "status": "pass",
      "duration_ms": 4311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:09.666181+00:00",
      "read_cold_ms": 2920,
      "read_warm_ms": 751,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1983_gencol_with_constraint",
      "num": 1983,
      "name": "gencol_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1983_gencol_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_1983_gencol_with_constraint.py",
      "description": "Generated column combined with a CHECK constraint on it.",
      "status": "pass",
      "duration_ms": 4836,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:14.504236+00:00",
      "read_cold_ms": 2797,
      "read_warm_ms": 834,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1984_gencol_with_cdc",
      "num": 1984,
      "name": "gencol_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1984_gencol_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_1984_gencol_with_cdc.py",
      "description": "Generated column combined with Change Data Feed.",
      "status": "pass",
      "duration_ms": 6267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:20.772325+00:00",
      "read_cold_ms": 4300,
      "read_warm_ms": 1154,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 106,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1985_gencol_decimal_calc",
      "num": 1985,
      "name": "gencol_decimal_calc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1985_gencol_decimal_calc.sql",
      "read_script": "generator/spark-reads-df/verify_1985_gencol_decimal_calc.py",
      "description": "Generated column with decimal arithmetic (price * qty).",
      "status": "pass",
      "duration_ms": 3191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:23.963908+00:00",
      "read_cold_ms": 2003,
      "read_warm_ms": 686,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 58,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1986_widen_byte_to_short",
      "num": 1986,
      "name": "widen_byte_to_short",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1986_widen_byte_to_short.sql",
      "read_script": "generator/spark-reads-df/verify_1986_widen_byte_to_short.py",
      "description": "Type widening from TINYINT (byte) to SMALLINT (short).",
      "status": "pass",
      "duration_ms": 4302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:28.267717+00:00",
      "read_cold_ms": 2865,
      "read_warm_ms": 547,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 56,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1987_widen_short_to_int",
      "num": 1987,
      "name": "widen_short_to_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1987_widen_short_to_int.sql",
      "read_script": "generator/spark-reads-df/verify_1987_widen_short_to_int.py",
      "description": "Type widening from SMALLINT to INT.",
      "status": "pass",
      "duration_ms": 3516,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:31.784611+00:00",
      "read_cold_ms": 2266,
      "read_warm_ms": 678,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 124,
      "write_warm_ms": 102,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1988_widen_int_to_long_partition",
      "num": 1988,
      "name": "widen_int_to_long_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1988_widen_int_to_long_partition.sql",
      "read_script": "generator/spark-reads-df/verify_1988_widen_int_to_long_partition.py",
      "description": "Type widening INT -> BIGINT on a partitioned (non-key) column.",
      "status": "pass",
      "duration_ms": 3486,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:35.272204+00:00",
      "read_cold_ms": 1813,
      "read_warm_ms": 782,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 313,
      "write_warm_ms": 222,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1989_widen_float_to_double",
      "num": 1989,
      "name": "widen_float_to_double",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1989_widen_float_to_double.sql",
      "read_script": "generator/spark-reads-df/verify_1989_widen_float_to_double.py",
      "description": "Type widening FLOAT -> DOUBLE.",
      "status": "pass",
      "duration_ms": 3593,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:38.866884+00:00",
      "read_cold_ms": 2424,
      "read_warm_ms": 549,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 196,
      "write_warm_ms": 47,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/198_map_array_types",
      "num": 198,
      "name": "map_array_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/198_map_array_types.sql",
      "read_script": "generator/spark-reads-df/verify_198_map_array_types.py",
      "description": "CHAOS TEST WORKFLOW (Complex Type Operations): This test verifies DeltaForge can correctly handle Map<K,V> and Array<T> types during INSERT/UPDATE/DELETE operations. Critical for document-style data.",
      "status": "pass",
      "duration_ms": 3555,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:23.354235+00:00",
      "read_cold_ms": 2356,
      "read_warm_ms": 580,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 91,
      "tags": [
        "type:array",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1990_widen_decimal_scale_up",
      "num": 1990,
      "name": "widen_decimal_scale_up",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1990_widen_decimal_scale_up.sql",
      "read_script": "generator/spark-reads-df/verify_1990_widen_decimal_scale_up.py",
      "description": "Type widening DECIMAL(10,2) -> DECIMAL(20,2) (precision up).",
      "status": "pass",
      "duration_ms": 3689,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:42.557713+00:00",
      "read_cold_ms": 2042,
      "read_warm_ms": 1026,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 25,
      "write_warm_ms": 67,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1991_widen_decimal_precision",
      "num": 1991,
      "name": "widen_decimal_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1991_widen_decimal_precision.sql",
      "read_script": "generator/spark-reads-df/verify_1991_widen_decimal_precision.py",
      "description": "Type widening DECIMAL(8,2) -> DECIMAL(18,2).",
      "status": "pass",
      "duration_ms": 2939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:45.497755+00:00",
      "read_cold_ms": 1625,
      "read_warm_ms": 728,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 71,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1992_widen_then_optimize",
      "num": 1992,
      "name": "widen_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1992_widen_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_1992_widen_then_optimize.py",
      "description": "INT -> BIGINT widening followed by OPTIMIZE compaction",
      "status": "pass",
      "duration_ms": 3348,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:48.847502+00:00",
      "read_cold_ms": 2056,
      "read_warm_ms": 603,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 335,
      "write_warm_ms": 236,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1993_widen_then_zorder",
      "num": 1993,
      "name": "widen_then_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1993_widen_then_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1993_widen_then_zorder.py",
      "description": "INT -> BIGINT widening followed by OPTIMIZE ZORDER BY widened col.",
      "status": "pass",
      "duration_ms": 3404,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:52.252468+00:00",
      "read_cold_ms": 2326,
      "read_warm_ms": 468,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 177,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1994_collation_unicode_basic",
      "num": 1994,
      "name": "collation_unicode_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1994_collation_unicode_basic.sql",
      "read_script": "generator/spark-reads-df/verify_1994_collation_unicode_basic.py",
      "description": "Enable collations table feature; INSERT 50 string rows.",
      "status": "pass",
      "duration_ms": 3857,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:56.110667+00:00",
      "read_cold_ms": 2275,
      "read_warm_ms": 606,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 24,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1995_collation_case_insensitive",
      "num": 1995,
      "name": "collation_case_insensitive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1995_collation_case_insensitive.sql",
      "read_script": "generator/spark-reads-df/verify_1995_collation_case_insensitive.py",
      "description": "Collations feature enabled; 50 rows with mixed-case strings.",
      "status": "pass",
      "duration_ms": 3582,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:10:59.694508+00:00",
      "read_cold_ms": 2209,
      "read_warm_ms": 666,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 125,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1996_collation_filter_pushdown",
      "num": 1996,
      "name": "collation_filter_pushdown",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1996_collation_filter_pushdown.sql",
      "read_script": "generator/spark-reads-df/verify_1996_collation_filter_pushdown.py",
      "description": "Collations feature + DELETE WHERE col = literal (filter pushdown path).",
      "status": "pass",
      "duration_ms": 4830,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:11:04.525376+00:00",
      "read_cold_ms": 3141,
      "read_warm_ms": 899,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 219,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1997_collation_with_zorder",
      "num": 1997,
      "name": "collation_with_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1997_collation_with_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_1997_collation_with_zorder.py",
      "description": "Collations feature + OPTIMIZE ZORDER BY string column.",
      "status": "pass",
      "duration_ms": 3299,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:11:07.825809+00:00",
      "read_cold_ms": 2299,
      "read_warm_ms": 448,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1998_collation_partition_key",
      "num": 1998,
      "name": "collation_partition_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1998_collation_partition_key.sql",
      "read_script": "generator/spark-reads-df/verify_1998_collation_partition_key.py",
      "description": "Collations feature on a partitioned table; partition key is STRING.",
      "status": "pass",
      "duration_ms": 3948,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:11:11.775156+00:00",
      "read_cold_ms": 2764,
      "read_warm_ms": 657,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 46,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/1999_collation_after_dml",
      "num": 1999,
      "name": "collation_after_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/1999_collation_after_dml.sql",
      "read_script": "generator/spark-reads-df/verify_1999_collation_after_dml.py",
      "description": "Collations feature + INSERT/UPDATE/DELETE workflow.",
      "status": "pass",
      "duration_ms": 4514,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:11:16.291247+00:00",
      "read_cold_ms": 2498,
      "read_warm_ms": 987,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 165,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/199_log_compaction",
      "num": 199,
      "name": "log_compaction",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/199_log_compaction.sql",
      "read_script": "generator/spark-reads-df/verify_199_log_compaction.py",
      "description": "CHAOS TEST WORKFLOW (Log Compaction/Checkpointing): This test verifies that DeltaForge-triggered log compaction (checkpointing) creates checkpoints that DBX can correctly read. Critical for large tables.",
      "status": "pass",
      "duration_ms": 3084,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:26.439662+00:00",
      "read_cold_ms": 2254,
      "read_warm_ms": 377,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1046,
      "write_warm_ms": 846,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:log-compaction",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/19_action_protocol_version_upgrade",
      "num": 19,
      "name": "action_protocol_version_upgrade",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/19_action_protocol_version_upgrade.sql",
      "read_script": "generator/spark-reads-df/verify_19_action_protocol_version_upgrade.py",
      "description": "Demonstrates protocol version upgrade action with CDC and column mapping features.",
      "status": "pass",
      "duration_ms": 4945,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:31.385673+00:00",
      "read_cold_ms": 2028,
      "read_warm_ms": 1083,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 191,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2000_domain_clustering_basic",
      "num": 2000,
      "name": "domain_clustering_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2000_domain_clustering_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2000_domain_clustering_basic.py",
      "description": "Domain metadata feature enabled + CLUSTER BY clustering domain.",
      "status": "pass",
      "duration_ms": 3253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T18:11:19.544931+00:00",
      "read_cold_ms": 1996,
      "read_warm_ms": 687,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 29,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:liquid-clustering",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2001_domain_clustering_after_optimize",
      "num": 2001,
      "name": "domain_clustering_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2001_domain_clustering_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2001_domain_clustering_after_optimize.py",
      "description": "CLUSTER BY + multiple INSERT batches + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 2876,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:24.025366+00:00",
      "read_cold_ms": 1542,
      "read_warm_ms": 450,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 357,
      "write_warm_ms": 360,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:liquid-clustering",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2002_domain_rowtrack_combined",
      "num": 2002,
      "name": "domain_rowtrack_combined",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2002_domain_rowtrack_combined.sql",
      "read_script": "generator/spark-reads-df/verify_2002_domain_rowtrack_combined.py",
      "description": "domainMetadata + rowTracking features together with CLUSTER BY.",
      "status": "pass",
      "duration_ms": 1763,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:25.788576+00:00",
      "read_cold_ms": 1133,
      "read_warm_ms": 346,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 21,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:liquid-clustering",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2003_domain_persist_through_restore",
      "num": 2003,
      "name": "domain_persist_through_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2003_domain_persist_through_restore.py",
      "description": "Domain metadata persists through RESTORE TO VERSION.",
      "status": "pass",
      "duration_ms": 1604,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:27.393098+00:00",
      "read_cold_ms": 1004,
      "read_warm_ms": 344,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
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      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:liquid-clustering",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2004_identity_after_optimize",
      "num": 2004,
      "name": "identity_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2004_identity_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2004_identity_after_optimize.py",
      "description": "IDENTITY column with 5 batched INSERTs of 20 rows each",
      "status": "pass",
      "duration_ms": 2398,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:29.791364+00:00",
      "read_cold_ms": 1040,
      "read_warm_ms": 384,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 426,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2005_identity_after_merge_insert",
      "num": 2005,
      "name": "identity_after_merge_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2005_identity_after_merge_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2005_identity_after_merge_insert.py",
      "description": "IDENTITY column with INSERT 50 followed by a MERGE that",
      "status": "pass",
      "duration_ms": 2104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:31.896490+00:00",
      "read_cold_ms": 944,
      "read_warm_ms": 318,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2006_identity_with_partition",
      "num": 2006,
      "name": "identity_with_partition",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2006_identity_with_partition.py",
      "description": "IDENTITY column with PARTITIONED BY region. 80 rows",
      "status": "pass",
      "duration_ms": 1818,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:33.715376+00:00",
      "read_cold_ms": 902,
      "read_warm_ms": 310,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2007_identity_after_restore",
      "num": 2007,
      "name": "identity_after_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2007_identity_after_restore.sql",
      "read_script": "generator/spark-reads-df/verify_2007_identity_after_restore.py",
      "description": "IDENTITY column with INSERT 100 rows (V0->V1), DELETE 30",
      "status": "pass",
      "duration_ms": 2084,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:35.800213+00:00",
      "read_cold_ms": 948,
      "read_warm_ms": 308,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 171,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2008_identity_with_cdc",
      "num": 2008,
      "name": "identity_with_cdc",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2008_identity_with_cdc.py",
      "description": "IDENTITY column with CDC enabled. INSERT 80 (ids 1..80),",
      "status": "pass",
      "duration_ms": 4031,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:39.832215+00:00",
      "read_cold_ms": 1408,
      "read_warm_ms": 503,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2009_identity_after_delete_resume",
      "num": 2009,
      "name": "identity_after_delete_resume",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2009_identity_after_delete_resume.sql",
      "read_script": "generator/spark-reads-df/verify_2009_identity_after_delete_resume.py",
      "description": "IDENTITY HWM continues across DELETE. INSERT 50 (ids 1..50),",
      "status": "pass",
      "duration_ms": 2599,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:42.431670+00:00",
      "read_cold_ms": 1137,
      "read_warm_ms": 412,
      "skip_feature": null,
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      "write_cold_ms": 263,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/200_time_travel_timestamp",
      "num": 200,
      "name": "time_travel_timestamp",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/200_time_travel_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_200_time_travel_timestamp.py",
      "description": "CHAOS TEST WORKFLOW (Time Travel by Timestamp): This test verifies DeltaForge correctly handles time travel queries using timestamps (not just versions). Critical for point-in-time recovery.",
      "status": "pass",
      "duration_ms": 3672,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:35.058840+00:00",
      "read_cold_ms": 1784,
      "read_warm_ms": 774,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
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        "delta:time-travel",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2010_default_literal_after_evolve",
      "num": 2010,
      "name": "default_literal_after_evolve",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2010_default_literal_after_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2010_default_literal_after_evolve.py",
      "description": "ALTER TABLE ADD COLUMN with DEFAULT after rows already",
      "status": "pass",
      "duration_ms": 1730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:44.162155+00:00",
      "read_cold_ms": 863,
      "read_warm_ms": 322,
      "skip_feature": null,
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      "write_cold_ms": 134,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2011_default_expression_now",
      "num": 2011,
      "name": "default_expression_now",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2011_default_expression_now.sql",
      "read_script": "generator/spark-reads-df/verify_2011_default_expression_now.py",
      "description": "DEFAULT expression -- column with a fixed timestamp default",
      "status": "pass",
      "duration_ms": 1766,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:45.928630+00:00",
      "read_cold_ms": 895,
      "read_warm_ms": 309,
      "skip_feature": null,
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      "write_cold_ms": 66,
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      "tags": [
        "type:integer",
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        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2012_default_with_partition",
      "num": 2012,
      "name": "default_with_partition",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2012_default_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2012_default_with_partition.py",
      "description": "Partitioned table with a non-partition column having a",
      "status": "pass",
      "duration_ms": 1617,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:47.546505+00:00",
      "read_cold_ms": 859,
      "read_warm_ms": 275,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 53,
      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2013_default_with_constraint",
      "num": 2013,
      "name": "default_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2013_default_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2013_default_with_constraint.py",
      "description": "Column with both DEFAULT and a CHECK constraint. The default",
      "status": "pass",
      "duration_ms": 1621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:49.168362+00:00",
      "read_cold_ms": 873,
      "read_warm_ms": 265,
      "skip_feature": null,
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      "write_cold_ms": 67,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2014_default_in_merge_insert",
      "num": 2014,
      "name": "default_in_merge_insert",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2014_default_in_merge_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2014_default_in_merge_insert.py",
      "description": "MERGE NOT MATCHED INSERT relies on a column with DEFAULT.",
      "status": "pass",
      "duration_ms": 1632,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:50.801509+00:00",
      "read_cold_ms": 870,
      "read_warm_ms": 285,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
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      "tags": [
        "type:integer",
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        "dml:merge",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2015_default_decimal_value",
      "num": 2015,
      "name": "default_decimal_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2015_default_decimal_value.sql",
      "read_script": "generator/spark-reads-df/verify_2015_default_decimal_value.py",
      "description": "DEFAULT for a DECIMAL column. 50 rows: 30 omit price (use",
      "status": "pass",
      "duration_ms": 1701,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:52.503025+00:00",
      "read_cold_ms": 833,
      "read_warm_ms": 307,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
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      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2016_ctas_with_cdc",
      "num": 2016,
      "name": "ctas_with_cdc",
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      "reader": "spark",
      "write_script": "generator/df-sql/2016_ctas_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2016_ctas_with_cdc.py",
      "description": "CTAS with TBLPROPERTIES enabling CDF. 100 rows from source.",
      "status": "pass",
      "duration_ms": 1428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:53.931245+00:00",
      "read_cold_ms": 917,
      "read_warm_ms": 296,
      "skip_feature": null,
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      "write_cold_ms": 53,
      "write_warm_ms": 80,
      "tags": [
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        "delta:change-data-feed",
        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2017_ctas_with_dv",
      "num": 2017,
      "name": "ctas_with_dv",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2017_ctas_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_2017_ctas_with_dv.py",
      "description": "CTAS with deletion vectors enabled, then DELETE 20 rows.",
      "status": "pass",
      "duration_ms": 2015,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:55.947017+00:00",
      "read_cold_ms": 994,
      "read_warm_ms": 408,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 200,
      "tags": [
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        "dml:delete",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2018_ctas_with_colmap",
      "num": 2018,
      "name": "ctas_with_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2018_ctas_with_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2018_ctas_with_colmap.py",
      "description": "CTAS with column mapping mode = name. 80 rows.",
      "status": "pass",
      "duration_ms": 1250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:57.197380+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
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      "tags": [
        "type:integer",
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        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2019_ctas_from_filtered",
      "num": 2019,
      "name": "ctas_from_filtered",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2019_ctas_from_filtered.sql",
      "read_script": "generator/spark-reads-df/verify_2019_ctas_from_filtered.py",
      "description": "CTAS with WHERE filter -- source generates 100 rows but",
      "status": "pass",
      "duration_ms": 1814,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:23:59.012026+00:00",
      "read_cold_ms": 787,
      "read_warm_ms": 268,
      "skip_feature": null,
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      "write_cold_ms": 104,
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      "tags": [
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        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/201_special_partition_values",
      "num": 201,
      "name": "special_partition_values",
      "type": "df-writes",
      "format": "delta",
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      "manual_tags": [],
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      "manual_tags": [],
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    {
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      "verified_at": "2026-05-14T15:24:17.079867+00:00",
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    {
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    {
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      "write_script": "generator/df-sql/2036_set_tblprop_targetfilesize.sql",
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      "description": "SET delta.targetFileSize then INSERT and OPTIMIZE.",
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      "duration_ms": 1306,
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      "verified_at": "2026-05-14T15:24:22.727340+00:00",
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      "id": "df-writes/delta/2037_set_tblprop_checkpointinterval",
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      "reader": "spark",
      "write_script": "generator/df-sql/2037_set_tblprop_checkpointinterval.sql",
      "read_script": "generator/spark-reads-df/verify_2037_set_tblprop_checkpointinterval.py",
      "description": "SET delta.checkpointInterval=5, force 12 commits via small INSERTs.",
      "status": "pass",
      "duration_ms": 2046,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:24.774382+00:00",
      "read_cold_ms": 1472,
      "read_warm_ms": 280,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 799,
      "write_warm_ms": 1430,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2038_set_tblprop_autoOptimize",
      "num": 2038,
      "name": "set_tblprop_autoOptimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2038_set_tblprop_autoOptimize.sql",
      "read_script": "generator/spark-reads-df/verify_2038_set_tblprop_autoOptimize.py",
      "description": "SET delta.autoOptimize.optimizeWrite=true, then INSERT 100.",
      "status": "pass",
      "duration_ms": 1306,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:26.080817+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2039_unset_then_set_again",
      "num": 2039,
      "name": "unset_then_set_again",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2039_unset_then_set_again.sql",
      "read_script": "generator/spark-reads-df/verify_2039_unset_then_set_again.py",
      "description": "SET delta.enableChangeDataFeed=true, UNSET, then SET again.",
      "status": "pass",
      "duration_ms": 1316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:27.397136+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 320,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/203_null_partition_values",
      "num": 203,
      "name": "null_partition_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/203_null_partition_values.sql",
      "read_script": "generator/spark-reads-df/verify_203_null_partition_values.py",
      "description": "CHAOS TEST WORKFLOW (NULL Values in Partition Columns): This test verifies DeltaForge correctly handles NULL values in partition columns. NULLs are stored in special __HIVE_DEFAULT_PARTITION__ directory.",
      "status": "pass",
      "duration_ms": 3400,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:46.434431+00:00",
      "read_cold_ms": 1686,
      "read_warm_ms": 763,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 81,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2040_set_appendonly_then_violate",
      "num": 2040,
      "name": "set_appendonly_then_violate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2040_set_appendonly_then_violate.sql",
      "read_script": "generator/spark-reads-df/verify_2040_set_appendonly_then_violate.py",
      "description": "SET delta.appendOnly=true; INSERT 50; subsequent INSERT works.",
      "status": "pass",
      "duration_ms": 1267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:28.664650+00:00",
      "read_cold_ms": 725,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2041_uniform_iceberg_basic",
      "num": 2041,
      "name": "uniform_iceberg_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2041_uniform_iceberg_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2041_uniform_iceberg_basic.py",
      "description": "Cluster O - UniForm Iceberg basic Enable UniForm Iceberg, INSERT 50 rows, verify metadata.",
      "status": "pass",
      "duration_ms": 1289,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:29.954172+00:00",
      "read_cold_ms": 745,
      "read_warm_ms": 165,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2042_uniform_iceberg_after_dml",
      "num": 2042,
      "name": "uniform_iceberg_after_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2042_uniform_iceberg_after_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2042_uniform_iceberg_after_dml.py",
      "description": "Cluster O - UniForm Iceberg + DML INSERT 100, UPDATE 30, DELETE 20. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 1958,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:31.912976+00:00",
      "read_cold_ms": 988,
      "read_warm_ms": 398,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 242,
      "write_warm_ms": 166,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2043_uniform_iceberg_partitioned",
      "num": 2043,
      "name": "uniform_iceberg_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2043_uniform_iceberg_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_2043_uniform_iceberg_partitioned.py",
      "description": "Cluster O - UniForm Iceberg + PARTITIONED BY region 80 rows distributed across 4 regions.",
      "status": "pass",
      "duration_ms": 1364,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:33.277208+00:00",
      "read_cold_ms": 667,
      "read_warm_ms": 163,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 362,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2044_uniform_iceberg_after_optimize",
      "num": 2044,
      "name": "uniform_iceberg_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2044_uniform_iceberg_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2044_uniform_iceberg_after_optimize.py",
      "description": "Cluster O - UniForm Iceberg + 5 INSERTs of 20 + OPTIMIZE",
      "status": "pass",
      "duration_ms": 1197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:34.474539+00:00",
      "read_cold_ms": 638,
      "read_warm_ms": 173,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 607,
      "write_warm_ms": 473,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2045_uniform_iceberg_typed_columns",
      "num": 2045,
      "name": "uniform_iceberg_typed_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2045_uniform_iceberg_typed_columns.sql",
      "read_script": "generator/spark-reads-df/verify_2045_uniform_iceberg_typed_columns.py",
      "description": "Cluster O - UniForm Iceberg with diverse column types 60 rows. INT, BIGINT, STRING, DECIMAL(10,2), DATE, TIMESTAMP, BOOLEAN.",
      "status": "pass",
      "duration_ms": 1154,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:35.629077+00:00",
      "read_cold_ms": 614,
      "read_warm_ms": 157,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 86,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2046_uniform_iceberg_evolution",
      "num": 2046,
      "name": "uniform_iceberg_evolution",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2046_uniform_iceberg_evolution.sql",
      "read_script": "generator/spark-reads-df/verify_2046_uniform_iceberg_evolution.py",
      "description": "Cluster O - UniForm Iceberg + schema evolution INSERT 50, ALTER ADD COLUMN extra, INSERT 30. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 1151,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:36.780514+00:00",
      "read_cold_ms": 657,
      "read_warm_ms": 162,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 265,
      "write_warm_ms": 234,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2047_ict_rowtrack_combined",
      "num": 2047,
      "name": "ict_rowtrack_combined",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2047_ict_rowtrack_combined.sql",
      "read_script": "generator/spark-reads-df/verify_2047_ict_rowtrack_combined.py",
      "description": "Cluster P - ICT + rowTracking + INSERT 100 + UPDATE 30",
      "status": "pass",
      "duration_ms": 1898,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:38.679273+00:00",
      "read_cold_ms": 976,
      "read_warm_ms": 375,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 171,
      "write_warm_ms": 268,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2048_gencol_rowtrack_optimize",
      "num": 2048,
      "name": "gencol_rowtrack_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2048_gencol_rowtrack_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2048_gencol_rowtrack_optimize.py",
      "description": "Cluster P - generated col + rowTracking + 5 INSERTs of 20 + OPTIMIZE",
      "status": "pass",
      "duration_ms": 1324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:40.004208+00:00",
      "read_cold_ms": 642,
      "read_warm_ms": 154,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 838,
      "write_warm_ms": 808,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:generated-columns",
        "delta:optimize",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2049_widen_evolve_cdc",
      "num": 2049,
      "name": "widen_evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2049_widen_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2049_widen_evolve_cdc.py",
      "description": "Cluster P - type widening + ALTER ADD COLUMN + CDF INSERT 80, ALTER widen score INT->BIGINT, ALTER add col extra, INSERT 20.",
      "status": "pass",
      "duration_ms": 1666,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:41.670647+00:00",
      "read_cold_ms": 639,
      "read_warm_ms": 137,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 277,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "schema:add-column",
        "schema:type-widening",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/204_unicode_data",
      "num": 204,
      "name": "unicode_data",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/204_unicode_data.sql",
      "read_script": "generator/spark-reads-df/verify_204_unicode_data.py",
      "description": "Validates Unicode data table with 50 rows containing 20 distinct language samples including CJK, Arabic, Hebrew, emoji, math symbols, zero-width spaces, and supplementary plane characters. Rows 21-50 are duplicates with modified text.",
      "status": "pass",
      "duration_ms": 2933,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:49.368987+00:00",
      "read_cold_ms": 2166,
      "read_warm_ms": 389,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 38,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2050_domain_clustering_uniform",
      "num": 2050,
      "name": "domain_clustering_uniform",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2050_domain_clustering_uniform.sql",
      "read_script": "generator/spark-reads-df/verify_2050_domain_clustering_uniform.py",
      "description": "Cluster P - clustering domain + UniForm Iceberg + INSERT 80 Liquid clustering produces a domainMetadata action. Combined with UniForm.",
      "status": "pass",
      "duration_ms": 1238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:42.909049+00:00",
      "read_cold_ms": 701,
      "read_warm_ms": 164,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:liquid-clustering",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2051_create_or_replace_basic",
      "num": 2051,
      "name": "create_or_replace_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2051_create_or_replace_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2051_create_or_replace_basic.py",
      "description": "CREATE OR REPLACE TABLE replaces existing table contents.",
      "status": "pass",
      "duration_ms": 1346,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:44.255734+00:00",
      "read_cold_ms": 807,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 59,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2052_create_or_replace_schema_change",
      "num": 2052,
      "name": "create_or_replace_schema_change",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2052_create_or_replace_schema_change.sql",
      "read_script": "generator/spark-reads-df/verify_2052_create_or_replace_schema_change.py",
      "description": "CREATE OR REPLACE with a different schema.",
      "status": "pass",
      "duration_ms": 1291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:45.547503+00:00",
      "read_cold_ms": 766,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 160,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2053_create_or_replace_partitioned",
      "num": 2053,
      "name": "create_or_replace_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2053_create_or_replace_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_2053_create_or_replace_partitioned.py",
      "description": "CREATE OR REPLACE adding partitioning.",
      "status": "pass",
      "duration_ms": 1670,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:47.218392+00:00",
      "read_cold_ms": 814,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2054_create_or_replace_with_cdc",
      "num": 2054,
      "name": "create_or_replace_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2054_create_or_replace_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2054_create_or_replace_with_cdc.py",
      "description": "CREATE OR REPLACE preserving CDC enable.",
      "status": "pass",
      "duration_ms": 1523,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:48.742195+00:00",
      "read_cold_ms": 736,
      "read_warm_ms": 265,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
      "write_warm_ms": 112,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2055_drop_then_recreate_same_name",
      "num": 2055,
      "name": "drop_then_recreate_same_name",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2055_drop_then_recreate_same_name.sql",
      "read_script": "generator/spark-reads-df/verify_2055_drop_then_recreate_same_name.py",
      "description": "DROP TABLE then CREATE with same name at same location.",
      "status": "pass",
      "duration_ms": 1288,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:50.031219+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 56,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2056_alter_add_multi_column",
      "num": 2056,
      "name": "alter_add_multi_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2056_alter_add_multi_column.py",
      "description": "ALTER TABLE ADD COLUMNS with multiple columns at once.",
      "status": "pass",
      "duration_ms": 1337,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:51.368644+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 280,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 99,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2057_alter_add_column_after_position",
      "num": 2057,
      "name": "alter_add_column_after_position",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2057_alter_add_column_after_position.sql",
      "read_script": "generator/spark-reads-df/verify_2057_alter_add_column_after_position.py",
      "description": "ALTER TABLE ADD COLUMN with AFTER positioning.",
      "status": "pass",
      "duration_ms": 1320,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:52.689495+00:00",
      "read_cold_ms": 774,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 132,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2058_alter_add_column_first",
      "num": 2058,
      "name": "alter_add_column_first",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2058_alter_add_column_first.sql",
      "read_script": "generator/spark-reads-df/verify_2058_alter_add_column_first.py",
      "description": "ALTER TABLE ADD COLUMN with FIRST positioning.",
      "status": "pass",
      "duration_ms": 1277,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:53.966800+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 252,
      "write_warm_ms": 113,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2059_alter_drop_column_partition_safe",
      "num": 2059,
      "name": "alter_drop_column_partition_safe",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2059_alter_drop_column_partition_safe.sql",
      "read_script": "generator/spark-reads-df/verify_2059_alter_drop_column_partition_safe.py",
      "description": "ALTER TABLE DROP COLUMN on a non-partition column.",
      "status": "pass",
      "duration_ms": 1629,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:55.596099+00:00",
      "read_cold_ms": 767,
      "read_warm_ms": 283,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 66,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/205_idempotent_writes",
      "num": 205,
      "name": "idempotent_writes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/205_idempotent_writes.sql",
      "read_script": "generator/spark-reads-df/verify_205_idempotent_writes.py",
      "description": "CHAOS TEST WORKFLOW (Idempotent/Exactly-Once Writes): This test verifies DeltaForge correctly implements the SetTransaction action for idempotent writes. Critical for streaming/exactly-once semantics.",
      "status": "pass",
      "duration_ms": 3507,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:52.877010+00:00",
      "read_cold_ms": 2024,
      "read_warm_ms": 864,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 27,
      "write_warm_ms": 19,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2060_alter_rename_column_with_data",
      "num": 2060,
      "name": "alter_rename_column_with_data",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2060_alter_rename_column_with_data.sql",
      "read_script": "generator/spark-reads-df/verify_2060_alter_rename_column_with_data.py",
      "description": "ALTER TABLE RENAME COLUMN preserves all data.",
      "status": "pass",
      "duration_ms": 1461,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:57.057394+00:00",
      "read_cold_ms": 921,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 261,
      "write_warm_ms": 193,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2061_alter_rename_then_add_same_name",
      "num": 2061,
      "name": "alter_rename_then_add_same_name",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2061_alter_rename_then_add_same_name.sql",
      "read_script": "generator/spark-reads-df/verify_2061_alter_rename_then_add_same_name.py",
      "description": "rename old col away, then add new col reusing the old name.",
      "status": "pass",
      "duration_ms": 1257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:58.315354+00:00",
      "read_cold_ms": 721,
      "read_warm_ms": 289,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 526,
      "write_warm_ms": 235,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2062_alter_change_column_comment",
      "num": 2062,
      "name": "alter_change_column_comment",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2062_alter_change_column_comment.sql",
      "read_script": "generator/spark-reads-df/verify_2062_alter_change_column_comment.py",
      "description": "ALTER COLUMN ... COMMENT 'new comment'.",
      "status": "pass",
      "duration_ms": 1310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:24:59.626256+00:00",
      "read_cold_ms": 752,
      "read_warm_ms": 278,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2063_alter_table_set_comment",
      "num": 2063,
      "name": "alter_table_set_comment",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2063_alter_table_set_comment.sql",
      "read_script": "generator/spark-reads-df/verify_2063_alter_table_set_comment.py",
      "description": "ALTER TABLE SET TBLPROPERTIES adding a comment property.",
      "status": "pass",
      "duration_ms": 1332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:00.958817+00:00",
      "read_cold_ms": 779,
      "read_warm_ms": 277,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 178,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2064_alter_table_unset_property",
      "num": 2064,
      "name": "alter_table_unset_property",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2064_alter_table_unset_property.sql",
      "read_script": "generator/spark-reads-df/verify_2064_alter_table_unset_property.py",
      "description": "ALTER TABLE UNSET TBLPROPERTIES removing a custom property.",
      "status": "pass",
      "duration_ms": 1393,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:02.352592+00:00",
      "read_cold_ms": 830,
      "read_warm_ms": 280,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 159,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2065_create_table_with_column_comments",
      "num": 2065,
      "name": "create_table_with_column_comments",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2065_create_table_with_column_comments.sql",
      "read_script": "generator/spark-reads-df/verify_2065_create_table_with_column_comments.py",
      "description": "CREATE TABLE with COMMENT on each column.",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:03.704537+00:00",
      "read_cold_ms": 792,
      "read_warm_ms": 283,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 36,
      "tags": [
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2066_create_table_partitioned_three_keys",
      "num": 2066,
      "name": "create_table_partitioned_three_keys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2066_create_table_partitioned_three_keys.sql",
      "read_script": "generator/spark-reads-df/verify_2066_create_table_partitioned_three_keys.py",
      "description": "PARTITIONED BY (a, b, c) -- three partition keys.",
      "status": "pass",
      "duration_ms": 2182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:05.887147+00:00",
      "read_cold_ms": 805,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 197,
      "write_warm_ms": 231,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2067_create_partitioned_with_cdc",
      "num": 2067,
      "name": "create_partitioned_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2067_create_partitioned_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2067_create_partitioned_with_cdc.py",
      "description": "partitioned + CDC across multiple versions.",
      "status": "pass",
      "duration_ms": 2424,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:08.311635+00:00",
      "read_cold_ms": 1012,
      "read_warm_ms": 355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 570,
      "write_warm_ms": 329,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2068_create_partitioned_with_dv",
      "num": 2068,
      "name": "create_partitioned_with_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2068_create_partitioned_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_2068_create_partitioned_with_dv.py",
      "description": "partitioned + DV deletes.",
      "status": "pass",
      "duration_ms": 2067,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:10.379006+00:00",
      "read_cold_ms": 918,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2069_create_table_all_numeric_types",
      "num": 2069,
      "name": "create_table_all_numeric_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2069_create_table_all_numeric_types.sql",
      "read_script": "generator/spark-reads-df/verify_2069_create_table_all_numeric_types.py",
      "description": "tinyint, smallint, int, bigint, float, double, decimal columns.",
      "status": "pass",
      "duration_ms": 1286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:11.665333+00:00",
      "read_cold_ms": 744,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 30,
      "write_warm_ms": 38,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/206_auto_optimize_trigger",
      "num": 206,
      "name": "auto_optimize_trigger",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/206_auto_optimize_trigger.sql",
      "read_script": "generator/spark-reads-df/verify_206_auto_optimize_trigger.py",
      "description": "CHAOS TEST WORKFLOW (Auto-Optimize Trigger): This test verifies that DeltaForge's auto-optimize feature creates compacted files that Databricks can read correctly.",
      "status": "pass",
      "duration_ms": 3700,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:44:56.578309+00:00",
      "read_cold_ms": 1704,
      "read_warm_ms": 874,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 470,
      "write_warm_ms": 687,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2070_create_table_all_string_types",
      "num": 2070,
      "name": "create_table_all_string_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2070_create_table_all_string_types.sql",
      "read_script": "generator/spark-reads-df/verify_2070_create_table_all_string_types.py",
      "description": "string, varchar, char, binary types.",
      "status": "pass",
      "duration_ms": 1233,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:12.898576+00:00",
      "read_cold_ms": 725,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 30,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
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    {
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      "read_script": "generator/spark-reads-df/verify_2089_merge_then_update.py",
      "description": "MERGE followed by an UPDATE statement.",
      "status": "pass",
      "duration_ms": 1685,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:42.893694+00:00",
      "read_cold_ms": 905,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/208_auto_optimize_mixed",
      "num": 208,
      "name": "auto_optimize_mixed",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3414,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:45:03.261755+00:00",
      "read_cold_ms": 1753,
      "read_warm_ms": 685,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 316,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2090_merge_followed_by_truncate",
      "num": 2090,
      "name": "merge_followed_by_truncate",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2090_merge_followed_by_truncate.sql",
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      "description": "MERGE then TRUNCATE -> empty table.",
      "status": "pass",
      "duration_ms": 1232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:44.126103+00:00",
      "read_cold_ms": 724,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 244,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:truncate",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2091_merge_into_dv_table",
      "num": 2091,
      "name": "merge_into_dv_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2091_merge_into_dv_table.sql",
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      "description": "MERGE into a table that already has Deletion Vector tracked",
      "status": "pass",
      "duration_ms": 1657,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:45.783798+00:00",
      "read_cold_ms": 869,
      "read_warm_ms": 368,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 348,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2092_merge_two_consecutive",
      "num": 2092,
      "name": "merge_two_consecutive",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2092_merge_two_consecutive.py",
      "description": "Two MERGE statements back to back.",
      "status": "pass",
      "duration_ms": 1681,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:47.465755+00:00",
      "read_cold_ms": 920,
      "read_warm_ms": 361,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 213,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2093_insert_update_delete_chain",
      "num": 2093,
      "name": "insert_update_delete_chain",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2093_insert_update_delete_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2093_insert_update_delete_chain.py",
      "description": "Multi-statement DML chain INSERT 50 -> UPDATE 20 -> DELETE 10 -> INSERT 30.",
      "status": "pass",
      "duration_ms": 1676,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:49.142673+00:00",
      "read_cold_ms": 870,
      "read_warm_ms": 394,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 117,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2094_delete_then_merge_reinsert",
      "num": 2094,
      "name": "delete_then_merge_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2094_delete_then_merge_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_2094_delete_then_merge_reinsert.py",
      "description": "DELETE all rows then MERGE re-inserts the same ids.",
      "status": "pass",
      "duration_ms": 1684,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:50.827443+00:00",
      "read_cold_ms": 915,
      "read_warm_ms": 382,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 138,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2095_update_then_merge_overwrite",
      "num": 2095,
      "name": "update_then_merge_overwrite",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2095_update_then_merge_overwrite.sql",
      "read_script": "generator/spark-reads-df/verify_2095_update_then_merge_overwrite.py",
      "description": "UPDATE then MERGE that re-updates the same rows.",
      "status": "pass",
      "duration_ms": 1705,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:52.533201+00:00",
      "read_cold_ms": 941,
      "read_warm_ms": 361,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2096_merge_typed_decimal",
      "num": 2096,
      "name": "merge_typed_decimal",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2096_merge_typed_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_2096_merge_typed_decimal.py",
      "description": "MERGE on a DECIMAL key column.",
      "status": "pass",
      "duration_ms": 1721,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:54.255386+00:00",
      "read_cold_ms": 964,
      "read_warm_ms": 358,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
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      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2097_merge_typed_string",
      "num": 2097,
      "name": "merge_typed_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2097_merge_typed_string.sql",
      "read_script": "generator/spark-reads-df/verify_2097_merge_typed_string.py",
      "description": "MERGE on a STRING key column.",
      "status": "pass",
      "duration_ms": 1723,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:55.979566+00:00",
      "read_cold_ms": 963,
      "read_warm_ms": 361,
      "skip_feature": null,
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      "write_cold_ms": 111,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2098_merge_typed_date",
      "num": 2098,
      "name": "merge_typed_date",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2098_merge_typed_date.sql",
      "read_script": "generator/spark-reads-df/verify_2098_merge_typed_date.py",
      "description": "MERGE on a DATE key column.",
      "status": "pass",
      "duration_ms": 1724,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:57.704548+00:00",
      "read_cold_ms": 961,
      "read_warm_ms": 353,
      "skip_feature": null,
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      "write_cold_ms": 54,
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      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2099_merge_with_identity",
      "num": 2099,
      "name": "merge_with_identity",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2099_merge_with_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2099_merge_with_identity.py",
      "description": "MERGE into a table with an IDENTITY column. Identity values",
      "status": "pass",
      "duration_ms": 1677,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:25:59.381937+00:00",
      "read_cold_ms": 890,
      "read_warm_ms": 379,
      "skip_feature": null,
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      "write_cold_ms": 214,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/209_reorg_purge",
      "num": 209,
      "name": "reorg_purge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/209_reorg_purge.sql",
      "read_script": "generator/spark-reads-df/verify_209_reorg_purge.py",
      "description": "REORG PURGE operation for physically removing soft-deleted column data.",
      "status": "pass",
      "duration_ms": 2667,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:45:05.932353+00:00",
      "read_cold_ms": 1703,
      "read_warm_ms": 423,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/20_action_commit_info_provenance",
      "num": 20,
      "name": "action_commit_info_provenance",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/20_action_commit_info_provenance.sql",
      "read_script": "generator/spark-reads-df/verify_20_action_commit_info_provenance.py",
      "description": "Demonstrates commitInfo with provenance information for regulatory compliance.",
      "status": "pass",
      "duration_ms": 5832,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:45:11.768078+00:00",
      "read_cold_ms": 3456,
      "read_warm_ms": 878,
      "skip_feature": null,
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      "write_cold_ms": 332,
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      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2100_merge_with_default",
      "num": 2100,
      "name": "merge_with_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2100_merge_with_default.sql",
      "read_script": "generator/spark-reads-df/verify_2100_merge_with_default.py",
      "description": "MERGE into a table where one column has a DEFAULT value, used",
      "status": "pass",
      "duration_ms": 1713,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:01.095841+00:00",
      "read_cold_ms": 931,
      "read_warm_ms": 381,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2101_identity_basic_insert",
      "num": 2101,
      "name": "identity_basic_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2101_identity_basic_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2101_identity_basic_insert.py",
      "description": "GENERATED ALWAYS AS IDENTITY auto-generates monotonic ids.",
      "status": "pass",
      "duration_ms": 1341,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:02.437841+00:00",
      "read_cold_ms": 838,
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      "skip_feature": null,
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      "write_cold_ms": 53,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2102_identity_by_default",
      "num": 2102,
      "name": "identity_by_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2102_identity_by_default.sql",
      "read_script": "generator/spark-reads-df/verify_2102_identity_by_default.py",
      "description": "GENERATED BY DEFAULT AS IDENTITY allows mixing user-supplied",
      "status": "pass",
      "duration_ms": 1184,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:03.622019+00:00",
      "read_cold_ms": 690,
      "read_warm_ms": 252,
      "skip_feature": null,
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      "write_cold_ms": 189,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2103_identity_with_partition",
      "num": 2103,
      "name": "identity_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2103_identity_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2103_identity_with_partition.py",
      "description": "IDENTITY column on a partitioned table; partition column is",
      "status": "pass",
      "duration_ms": 1213,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:04.835910+00:00",
      "read_cold_ms": 694,
      "read_warm_ms": 268,
      "skip_feature": null,
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      "write_cold_ms": 37,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2104_identity_after_delete",
      "num": 2104,
      "name": "identity_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2104_identity_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2104_identity_after_delete.py",
      "description": "IDENTITY values continue past previous high water mark even",
      "status": "pass",
      "duration_ms": 1609,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:06.445684+00:00",
      "read_cold_ms": 882,
      "read_warm_ms": 340,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2105_identity_after_update",
      "num": 2105,
      "name": "identity_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2105_identity_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2105_identity_after_update.py",
      "description": "IDENTITY column values are preserved across UPDATE of OTHER",
      "status": "pass",
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      "error": null,
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        "format:delta"
      ],
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    },
    {
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      "verified_at": "2026-05-14T15:26:09.947612+00:00",
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        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2107_identity_two_blocks",
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      "name": "identity_two_blocks",
      "type": "df-writes",
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      "read_script": "generator/spark-reads-df/verify_2107_identity_two_blocks.py",
      "description": "Two separate INSERT statements produce two identity blocks",
      "status": "pass",
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      "verified_at": "2026-05-14T15:26:11.218097+00:00",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2108_identity_start_with",
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      "verified_at": "2026-05-14T15:26:12.426758+00:00",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2109_identity_increment_by_5",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2109_identity_increment_by_5.py",
      "description": "GENERATED ... AS IDENTITY (START WITH 1 INCREMENT BY 5).",
      "status": "pass",
      "duration_ms": 1232,
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      "verified_at": "2026-05-14T15:26:13.659942+00:00",
      "read_cold_ms": 705,
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        "dml:insert",
        "delta:default-values",
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        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/210_reorg_checkpoint",
      "num": 210,
      "name": "reorg_checkpoint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/210_reorg_checkpoint.sql",
      "read_script": "generator/spark-reads-df/verify_210_reorg_checkpoint.py",
      "description": "REORG CHECKPOINT operation for forcing checkpoint creation.",
      "status": "pass",
      "duration_ms": 3016,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:45:14.791093+00:00",
      "read_cold_ms": 1859,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2110_identity_optimize",
      "num": 2110,
      "name": "identity_optimize",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2110_identity_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2110_identity_optimize.py",
      "description": "identity column values survive OPTIMIZE compaction.",
      "status": "pass",
      "duration_ms": 1228,
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      "verified_at": "2026-05-14T15:26:14.888184+00:00",
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        "type:integer",
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        "dml:insert",
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        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 2111,
      "name": "default_literal_int",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2111_default_literal_int.py",
      "description": "DEFAULT 42 on int column. INSERT omitting that column",
      "status": "pass",
      "duration_ms": 1200,
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      "verified_at": "2026-05-14T15:26:16.088590+00:00",
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      "tags": [
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        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2112_default_literal_string",
      "num": 2112,
      "name": "default_literal_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2112_default_literal_string.py",
      "description": "DEFAULT 'unknown' on STRING column.",
      "status": "pass",
      "duration_ms": 1171,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:26:17.260790+00:00",
      "read_cold_ms": 679,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2113_default_current_date",
      "num": 2113,
      "name": "default_current_date",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2113_default_current_date.py",
      "description": "DEFAULT CURRENT_DATE on a DATE column. Each new row",
      "status": "pass",
      "duration_ms": 1242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:18.503597+00:00",
      "read_cold_ms": 731,
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      "tags": [
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        "dml:insert",
        "delta:default-values",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2114_default_after_evolution",
      "num": 2114,
      "name": "default_after_evolution",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2114_default_after_evolution.sql",
      "read_script": "generator/spark-reads-df/verify_2114_default_after_evolution.py",
      "description": "ALTER TABLE ADD COLUMN x INT DEFAULT 0 mid-table-life.",
      "status": "pass",
      "duration_ms": 1288,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:19.792726+00:00",
      "read_cold_ms": 760,
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      "skip_feature": null,
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      "write_cold_ms": 90,
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      "tags": [
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2115_default_with_partition",
      "num": 2115,
      "name": "default_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2115_default_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2115_default_with_partition.py",
      "description": "DEFAULT value on a non-partition column of a partitioned table.",
      "status": "pass",
      "duration_ms": 1258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:21.051138+00:00",
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      "tags": [
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        "dml:insert",
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        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2116_default_with_constraint",
      "num": 2116,
      "name": "default_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2116_default_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2116_default_with_constraint.py",
      "description": "DEFAULT value combined with a CHECK constraint on the same",
      "status": "pass",
      "duration_ms": 1255,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:26:22.306472+00:00",
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      "write_cold_ms": 100,
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      "tags": [
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        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2117_default_null_explicit",
      "num": 2117,
      "name": "default_null_explicit",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2117_default_null_explicit.sql",
      "read_script": "generator/spark-reads-df/verify_2117_default_null_explicit.py",
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      "status": "pass",
      "duration_ms": 1278,
      "error": null,
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      "verified_at": "2026-05-14T15:26:23.585181+00:00",
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      "tags": [
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        "dml:insert",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2118_default_multiple_columns",
      "num": 2118,
      "name": "default_multiple_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2118_default_multiple_columns.sql",
      "read_script": "generator/spark-reads-df/verify_2118_default_multiple_columns.py",
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      "status": "pass",
      "duration_ms": 1216,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:26:24.801350+00:00",
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      "tags": [
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2119_default_with_cdc",
      "num": 2119,
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      "read_script": "generator/spark-reads-df/verify_2119_default_with_cdc.py",
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      "status": "pass",
      "duration_ms": 1816,
      "error": null,
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      "verified_at": "2026-05-14T15:26:26.617633+00:00",
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      "tags": [
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        "dml:cdc-write",
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        "delta:change-data-feed",
        "delta:default-values",
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/211_reorg_upgrade_uniform",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_211_reorg_upgrade_uniform.py",
      "description": "REORG UPGRADE UNIFORM operation for adding Iceberg compatibility.",
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      "duration_ms": 2832,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:45:17.624293+00:00",
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      "tags": [
        "type:floating",
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        "delta:optimize",
        "iceberg:uniform",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2120_gencol_concat",
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      "name": "gencol_concat",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2120_gencol_concat.sql",
      "read_script": "generator/spark-reads-df/verify_2120_gencol_concat.py",
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      "duration_ms": 1248,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:26:27.865837+00:00",
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:deletion-vectors",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2121_gencol_arithmetic",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2121_gencol_arithmetic.py",
      "description": "GENERATED ALWAYS AS (a + b) on INT columns -> BIGINT result.",
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      "verified_at": "2026-05-14T15:26:29.099588+00:00",
      "read_cold_ms": 711,
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        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/2122_gencol_year_from_date",
      "num": 2122,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2122_gencol_year_from_date.sql",
      "read_script": "generator/spark-reads-df/verify_2122_gencol_year_from_date.py",
      "description": "GENERATED ALWAYS AS (YEAR(event_date)) -- date extraction.",
      "status": "pass",
      "duration_ms": 1221,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:26:30.321450+00:00",
      "read_cold_ms": 703,
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      "skip_feature": null,
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      "tags": [
        "type:date",
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        "dml:insert",
        "delta:deletion-vectors",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2123_gencol_with_partition",
      "num": 2123,
      "name": "gencol_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2123_gencol_with_partition.py",
      "description": "GENERATED column used as the PARTITION key. yr_part is",
      "status": "pass",
      "duration_ms": 1335,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:31.657128+00:00",
      "read_cold_ms": 792,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 72,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2124_gencol_after_update",
      "num": 2124,
      "name": "gencol_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2124_gencol_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2124_gencol_after_update.py",
      "description": "UPDATE on base column triggers recomputation of generated col.",
      "status": "pass",
      "duration_ms": 1680,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:33.337616+00:00",
      "read_cold_ms": 912,
      "read_warm_ms": 383,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2125_gencol_with_constraint",
      "num": 2125,
      "name": "gencol_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2125_gencol_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2125_gencol_with_constraint.py",
      "description": "GENERATED column + CHECK constraint on the generated value.",
      "status": "pass",
      "duration_ms": 1227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:34.565746+00:00",
      "read_cold_ms": 693,
      "read_warm_ms": 278,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2126_zorder_single_int_col",
      "num": 2126,
      "name": "zorder_single_int_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2126_zorder_single_int_col.sql",
      "read_script": "generator/spark-reads-df/verify_2126_zorder_single_int_col.py",
      "description": "OPTIMIZE ZORDER BY single INT column. 100 rows.",
      "status": "pass",
      "duration_ms": 1554,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:36.120799+00:00",
      "read_cold_ms": 757,
      "read_warm_ms": 279,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2127_zorder_string_col",
      "num": 2127,
      "name": "zorder_string_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2127_zorder_string_col.sql",
      "read_script": "generator/spark-reads-df/verify_2127_zorder_string_col.py",
      "description": "OPTIMIZE ZORDER BY a STRING column. 80 rows.",
      "status": "pass",
      "duration_ms": 1483,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:37.604416+00:00",
      "read_cold_ms": 718,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2128_zorder_two_cols",
      "num": 2128,
      "name": "zorder_two_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2128_zorder_two_cols.sql",
      "read_script": "generator/spark-reads-df/verify_2128_zorder_two_cols.py",
      "description": "OPTIMIZE ZORDER BY (a, b). 100 rows.",
      "status": "pass",
      "duration_ms": 1227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:38.832276+00:00",
      "read_cold_ms": 703,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 25,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2129_zorder_after_delete",
      "num": 2129,
      "name": "zorder_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2129_zorder_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2129_zorder_after_delete.py",
      "description": "INSERT 100 rows, DELETE half, then OPTIMIZE ZORDER BY (score).",
      "status": "pass",
      "duration_ms": 1629,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:40.461654+00:00",
      "read_cold_ms": 868,
      "read_warm_ms": 367,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/212_convert_parquet",
      "num": 212,
      "name": "convert_parquet",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/212_convert_parquet.sql",
      "read_script": "generator/spark-reads-df/verify_212_convert_parquet.py",
      "description": "CHAOS TEST WORKFLOW (CONVERT Parquet to Delta): The Rust generator creates raw Parquet files first, then converts to Delta. For the SQL version, we create a Delta table directly with the same data, since the final state should match.",
      "status": "pass",
      "duration_ms": 1136,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:45:18.761572+00:00",
      "read_cold_ms": 641,
      "read_warm_ms": 312,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 24,
      "write_warm_ms": 25,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2130_zorder_after_update",
      "num": 2130,
      "name": "zorder_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2130_zorder_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2130_zorder_after_update.py",
      "description": "INSERT 100, UPDATE some, then OPTIMIZE ZORDER BY (score).",
      "status": "pass",
      "duration_ms": 1235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:41.697161+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
      "write_warm_ms": 204,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2131_zorder_partitioned_table",
      "num": 2131,
      "name": "zorder_partitioned_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2131_zorder_partitioned_table.sql",
      "read_script": "generator/spark-reads-df/verify_2131_zorder_partitioned_table.py",
      "description": "ZORDER on a partitioned table. ZORDER applies within each partition.",
      "status": "pass",
      "duration_ms": 1537,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:43.234468+00:00",
      "read_cold_ms": 772,
      "read_warm_ms": 241,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 58,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2132_zorder_with_cdc",
      "num": 2132,
      "name": "zorder_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2132_zorder_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2132_zorder_with_cdc.py",
      "description": "ZORDER on a CDC-enabled table. ZORDER must not emit CDF rows.",
      "status": "pass",
      "duration_ms": 1435,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:44.669925+00:00",
      "read_cold_ms": 689,
      "read_warm_ms": 227,
      "skip_feature": null,
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      "write_cold_ms": 47,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2133_optimize_compact_many_files",
      "num": 2133,
      "name": "optimize_compact_many_files",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2133_optimize_compact_many_files.sql",
      "read_script": "generator/spark-reads-df/verify_2133_optimize_compact_many_files.py",
      "description": "30 single-row INSERTs followed by OPTIMIZE -- file compaction proof.",
      "status": "pass",
      "duration_ms": 1800,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:46.470175+00:00",
      "read_cold_ms": 1054,
      "read_warm_ms": 252,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1919,
      "write_warm_ms": 4019,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2134_optimize_then_optimize",
      "num": 2134,
      "name": "optimize_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2134_optimize_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2134_optimize_then_optimize.py",
      "description": "OPTIMIZE twice in a row -- second should be a no-op or idempotent.",
      "status": "pass",
      "duration_ms": 1268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:47.738859+00:00",
      "read_cold_ms": 736,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 446,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2135_optimize_after_truncate",
      "num": 2135,
      "name": "optimize_after_truncate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2135_optimize_after_truncate.sql",
      "read_script": "generator/spark-reads-df/verify_2135_optimize_after_truncate.py",
      "description": "TRUNCATE then INSERT then OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:49.014758+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 157,
      "write_warm_ms": 124,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2136_optimize_evolved_schema",
      "num": 2136,
      "name": "optimize_evolved_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2136_optimize_evolved_schema.sql",
      "read_script": "generator/spark-reads-df/verify_2136_optimize_evolved_schema.py",
      "description": "ALTER ADD COLUMN then OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:50.243560+00:00",
      "read_cold_ms": 715,
      "read_warm_ms": 240,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 164,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2137_optimize_with_constraint",
      "num": 2137,
      "name": "optimize_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2137_optimize_with_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2137_optimize_with_constraint.py",
      "description": "OPTIMIZE on a table with a CHECK constraint -- constraint preserved post-OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:51.425815+00:00",
      "read_cold_ms": 678,
      "read_warm_ms": 247,
      "skip_feature": null,
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      "write_cold_ms": 81,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2138_vacuum_basic",
      "num": 2138,
      "name": "vacuum_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2138_vacuum_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2138_vacuum_basic.py",
      "description": "Basic VACUUM with 0-hour retention.",
      "status": "pass",
      "duration_ms": 1228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:52.654327+00:00",
      "read_cold_ms": 725,
      "read_warm_ms": 233,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2139_vacuum_after_optimize",
      "num": 2139,
      "name": "vacuum_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2139_vacuum_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2139_vacuum_after_optimize.py",
      "description": "OPTIMIZE then VACUUM. The VACUUM should remove the pre-OPTIMIZE files.",
      "status": "pass",
      "duration_ms": 1225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:53.880355+00:00",
      "read_cold_ms": 710,
      "read_warm_ms": 254,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/213_convert_parquet_partitioned",
      "num": 213,
      "name": "convert_parquet_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/213_convert_parquet_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_213_convert_parquet_partitioned.py",
      "description": "CHAOS TEST WORKFLOW (CONVERT Partitioned Parquet to Delta): The Rust generator creates partitioned Parquet files (year=/month=/) first, then converts to Delta using CONVERT TO DELTA with partition inference.",
      "status": "pass",
      "duration_ms": 2046,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:45:20.808732+00:00",
      "read_cold_ms": 1559,
      "read_warm_ms": 258,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 84,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2140_vacuum_after_delete",
      "num": 2140,
      "name": "vacuum_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "DELETE then VACUUM.",
      "status": "pass",
      "duration_ms": 1628,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:55.509229+00:00",
      "read_cold_ms": 882,
      "read_warm_ms": 361,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:delete",
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        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2141_vacuum_with_cdc",
      "num": 2141,
      "name": "vacuum_with_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 1661,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:57.171333+00:00",
      "read_cold_ms": 878,
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      "skip_feature": null,
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        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2142_time_travel_v0",
      "num": 2142,
      "name": "time_travel_v0",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2142_time_travel_v0.sql",
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      "description": "Two INSERTs creating versions 1 and 2; query VERSION AS OF 1 returns the first batch.",
      "status": "pass",
      "duration_ms": 1719,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:26:58.891393+00:00",
      "read_cold_ms": 698,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2143_time_travel_v1",
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      "name": "time_travel_v1",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2143_time_travel_v1.sql",
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      "status": "pass",
      "duration_ms": 2342,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:27:01.233701+00:00",
      "read_cold_ms": 751,
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      "skip_feature": null,
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        "type:integer",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2144_time_travel_after_delete",
      "num": 2144,
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT then DELETE. Time travel to pre-delete shows full row count.",
      "status": "pass",
      "duration_ms": 2082,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:27:03.316299+00:00",
      "read_cold_ms": 879,
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      "skip_feature": null,
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        "type:integer",
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        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2145_time_travel_after_update",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 2354,
      "error": null,
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      "extras": {},
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        "dml:insert",
        "dml:update",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2146_time_travel_with_evolution",
      "num": 2146,
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      "type": "df-writes",
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      "reader": "spark",
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      "description": "ADD COLUMN then time travel to pre-evolution -- old version has fewer columns.",
      "status": "pass",
      "duration_ms": 1699,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:27:07.370425+00:00",
      "read_cold_ms": 734,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2147_time_travel_with_cdc",
      "num": 2147,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2147_time_travel_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2147_time_travel_with_cdc.py",
      "description": "CDC-enabled table; multiple versions; time travel returns historical states.",
      "status": "pass",
      "duration_ms": 3352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:27:10.723524+00:00",
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      "skip_feature": null,
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        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
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        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2148_restore_to_version",
      "num": 2148,
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "INSERT, INSERT, INSERT, RESTORE TO VERSION AS OF 1.",
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      "duration_ms": 1227,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 707,
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      "skip_feature": null,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2149_restore_after_delete",
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      "description": "INSERT, DELETE, RESTORE undoes the delete.",
      "status": "pass",
      "duration_ms": 1264,
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      "extras": {},
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/214_convert_iceberg",
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/214_convert_iceberg.sql",
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      "status": "pass",
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      "extras": {},
      "verified_at": "2026-05-20T00:45:23.733539+00:00",
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      "manual_tags": [],
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    {
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      "description": "RESTORE then INSERT new data on top.",
      "status": "pass",
      "duration_ms": 1235,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:14.451404+00:00",
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      "manual_tags": [],
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      "duration_ms": 1676,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:16.128563+00:00",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2152_subq_not_in_select",
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      "reader": "spark",
      "write_script": "generator/df-sql/2152_subq_not_in_select.sql",
      "read_script": "generator/spark-reads-df/verify_2152_subq_not_in_select.py",
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      "status": "pass",
      "duration_ms": 1605,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:17.734652+00:00",
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      "manual_tags": [],
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      "read_script": "generator/spark-reads-df/verify_2153_subq_scalar_max.py",
      "description": "DELETE rows where id < max threshold pattern via WHERE id <= literal.",
      "status": "pass",
      "duration_ms": 1634,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:19.369433+00:00",
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      "manual_tags": [],
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    {
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      "writer": "delta-forge",
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      "description": null,
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      "duration_ms": 1606,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:20.976455+00:00",
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/2155_subq_exists_clause",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2155_subq_exists_clause.py",
      "description": null,
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      "duration_ms": 1221,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:22.197739+00:00",
      "read_cold_ms": 706,
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      "skip_feature": null,
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        "dml:delete",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2156_subq_not_exists_clause",
      "num": 2156,
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      "reader": "spark",
      "write_script": "generator/df-sql/2156_subq_not_exists_clause.sql",
      "read_script": "generator/spark-reads-df/verify_2156_subq_not_exists_clause.py",
      "description": null,
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      "duration_ms": 1636,
      "error": null,
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      "verified_at": "2026-05-14T15:27:23.834204+00:00",
      "read_cold_ms": 883,
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      "skip_feature": null,
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      "tags": [
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        "dml:delete",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2157_subq_in_two_columns",
      "num": 2157,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2157_subq_in_two_columns.sql",
      "read_script": "generator/spark-reads-df/verify_2157_subq_in_two_columns.py",
      "description": null,
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      "duration_ms": 1692,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:27:25.527124+00:00",
      "read_cold_ms": 937,
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2158_subq_scalar_avg",
      "num": 2158,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2158_subq_scalar_avg.sql",
      "read_script": "generator/spark-reads-df/verify_2158_subq_scalar_avg.py",
      "description": "UPDATE pattern setting all rows to computed constant.",
      "status": "pass",
      "duration_ms": 1625,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:27:27.153296+00:00",
      "read_cold_ms": 869,
      "read_warm_ms": 381,
      "skip_feature": null,
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      "tags": [
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        "dml:update",
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    },
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    },
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      "reader": "spark",
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      "read_warm_ms": 422,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 198,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2249_bool_pred_le_only",
      "num": 2249,
      "name": "bool_pred_le_only",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2249_bool_pred_le_only.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1681,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:35.310459+00:00",
      "read_cold_ms": 898,
      "read_warm_ms": 383,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
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        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/224_extreme_partitions",
      "num": 224,
      "name": "extreme_partitions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/224_extreme_partitions.sql",
      "read_script": "generator/spark-reads-df/verify_224_extreme_partitions.py",
      "description": "CHAOS TEST WORKFLOW (Extreme Partitions): This test verifies handling of tables with multiple partitions. Tests partition listing, pruning, and metadata handling.",
      "status": "pass",
      "duration_ms": 3186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:01.945966+00:00",
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      "read_warm_ms": 354,
      "skip_feature": null,
      "skip_cause": null,
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        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2250_bool_pred_in_then_delete",
      "num": 2250,
      "name": "bool_pred_in_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2250_bool_pred_in_then_delete.sql",
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      "description": null,
      "status": "pass",
      "duration_ms": 1696,
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      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:37.007050+00:00",
      "read_cold_ms": 918,
      "read_warm_ms": 360,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2251_merge_identity_then_insert",
      "num": 2251,
      "name": "merge_identity_then_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2251_merge_identity_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2251_merge_identity_then_insert.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1650,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:38.657625+00:00",
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 316,
      "write_warm_ms": 795,
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        "type:string",
        "dml:insert",
        "dml:merge",
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        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2252_merge_then_merge_identity",
      "num": 2252,
      "name": "merge_then_merge_identity",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1281,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:39.939822+00:00",
      "read_cold_ms": 739,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 457,
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2253_merge_default_int",
      "num": 2253,
      "name": "merge_default_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2253_merge_default_int.sql",
      "read_script": "generator/spark-reads-df/verify_2253_merge_default_int.py",
      "description": "MERGE NOT MATCHED INSERT with INT DEFAULT",
      "status": "pass",
      "duration_ms": 1739,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:41.679113+00:00",
      "read_cold_ms": 917,
      "read_warm_ms": 380,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 246,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2254_merge_default_date",
      "num": 2254,
      "name": "merge_default_date",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2254_merge_default_date.sql",
      "read_script": "generator/spark-reads-df/verify_2254_merge_default_date.py",
      "description": "MERGE NOT MATCHED INSERT with DATE DEFAULT",
      "status": "pass",
      "duration_ms": 1283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:42.962377+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 77,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2255_merge_generated_concat",
      "num": 2255,
      "name": "merge_generated_concat",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2255_merge_generated_concat.sql",
      "read_script": "generator/spark-reads-df/verify_2255_merge_generated_concat.py",
      "description": "MERGE into table with GENERATED column (CONCAT)",
      "status": "pass",
      "duration_ms": 1281,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:44.244022+00:00",
      "read_cold_ms": 723,
      "read_warm_ms": 248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 302,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2256_merge_generated_arithmetic",
      "num": 2256,
      "name": "merge_generated_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2256_merge_generated_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_2256_merge_generated_arithmetic.py",
      "description": "MERGE into table with GENERATED column (price * qty)",
      "status": "pass",
      "duration_ms": 1751,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:45.995330+00:00",
      "read_cold_ms": 957,
      "read_warm_ms": 372,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 438,
      "tags": [
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2257_merge_default_bool",
      "num": 2257,
      "name": "merge_default_bool",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2257_merge_default_bool.sql",
      "read_script": "generator/spark-reads-df/verify_2257_merge_default_bool.py",
      "description": "MERGE NOT MATCHED INSERT with BOOLEAN DEFAULT",
      "status": "pass",
      "duration_ms": 1300,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:47.295825+00:00",
      "read_cold_ms": 733,
      "read_warm_ms": 269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 414,
      "write_warm_ms": 489,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2258_merge_default_decimal",
      "num": 2258,
      "name": "merge_default_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2258_merge_default_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_2258_merge_default_decimal.py",
      "description": "MERGE NOT MATCHED INSERT with DECIMAL DEFAULT",
      "status": "pass",
      "duration_ms": 1237,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:48.533435+00:00",
      "read_cold_ms": 697,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
      "write_warm_ms": 299,
      "tags": [
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2259_merge_identity_with_delete_chain",
      "num": 2259,
      "name": "merge_identity_with_delete_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2259_merge_identity_with_delete_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2259_merge_identity_with_delete_chain.py",
      "description": "INSERT, MERGE-insert, DELETE, MERGE-insert again -- HWM must keep growing",
      "status": "pass",
      "duration_ms": 1706,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:50.239785+00:00",
      "read_cold_ms": 924,
      "read_warm_ms": 385,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 252,
      "write_warm_ms": 320,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/225_extreme_columns",
      "num": 225,
      "name": "extreme_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/225_extreme_columns.sql",
      "read_script": "generator/spark-reads-df/verify_225_extreme_columns.py",
      "description": "CHAOS TEST WORKFLOW (Extreme Columns): This test verifies handling of tables with wide schemas. Tests schema serialization, column projection.",
      "status": "pass",
      "duration_ms": 2464,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:04.411101+00:00",
      "read_cold_ms": 1552,
      "read_warm_ms": 357,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 55,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2260_merge_default_and_generated",
      "num": 2260,
      "name": "merge_default_and_generated",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2260_merge_default_and_generated.sql",
      "read_script": "generator/spark-reads-df/verify_2260_merge_default_and_generated.py",
      "description": "MERGE NOT MATCHED INSERT into table with both DEFAULT and GENERATED columns",
      "status": "pass",
      "duration_ms": 1325,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:51.565472+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 297,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 299,
      "write_warm_ms": 77,
      "tags": [
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        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2261_cor_add_identity_column",
      "num": 2261,
      "name": "cor_add_identity_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2261_cor_add_identity_column.sql",
      "read_script": "generator/spark-reads-df/verify_2261_cor_add_identity_column.py",
      "description": "CREATE OR REPLACE adding a new IDENTITY column to schema",
      "status": "pass",
      "duration_ms": 1317,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:52.883434+00:00",
      "read_cold_ms": 738,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 75,
      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2262_cor_add_default_column",
      "num": 2262,
      "name": "cor_add_default_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2262_cor_add_default_column.sql",
      "read_script": "generator/spark-reads-df/verify_2262_cor_add_default_column.py",
      "description": "CREATE OR REPLACE adding a column with DEFAULT",
      "status": "pass",
      "duration_ms": 1274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:54.157699+00:00",
      "read_cold_ms": 702,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 193,
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      "tags": [
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2263_cor_add_generated_column",
      "num": 2263,
      "name": "cor_add_generated_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2263_cor_add_generated_column.sql",
      "read_script": "generator/spark-reads-df/verify_2263_cor_add_generated_column.py",
      "description": "CREATE OR REPLACE adding a GENERATED column",
      "status": "pass",
      "duration_ms": 1269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:55.427061+00:00",
      "read_cold_ms": 718,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 146,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2264_cor_change_partition_columns",
      "num": 2264,
      "name": "cor_change_partition_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2264_cor_change_partition_columns.sql",
      "read_script": "generator/spark-reads-df/verify_2264_cor_change_partition_columns.py",
      "description": "CREATE OR REPLACE with different partition columns",
      "status": "pass",
      "duration_ms": 1333,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:56.760596+00:00",
      "read_cold_ms": 790,
      "read_warm_ms": 269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 272,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2265_cor_remove_partition_columns",
      "num": 2265,
      "name": "cor_remove_partition_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2265_cor_remove_partition_columns.sql",
      "read_script": "generator/spark-reads-df/verify_2265_cor_remove_partition_columns.py",
      "description": "CREATE OR REPLACE removing partition columns",
      "status": "pass",
      "duration_ms": 1282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:58.043503+00:00",
      "read_cold_ms": 743,
      "read_warm_ms": 266,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2266_cor_enable_cdc",
      "num": 2266,
      "name": "cor_enable_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2266_cor_enable_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2266_cor_enable_cdc.py",
      "description": "CREATE OR REPLACE enabling CDC on a table that did not have it",
      "status": "pass",
      "duration_ms": 1380,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:29:59.424349+00:00",
      "read_cold_ms": 790,
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      "skip_feature": null,
      "skip_cause": null,
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        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2267_cor_with_identity_then_insert",
      "num": 2267,
      "name": "cor_with_identity_then_insert",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2267_cor_with_identity_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2267_cor_with_identity_then_insert.py",
      "description": "CREATE OR REPLACE with new IDENTITY then 2 INSERTs to verify HWM persists",
      "status": "pass",
      "duration_ms": 1380,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:00.805160+00:00",
      "read_cold_ms": 780,
      "read_warm_ms": 321,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 538,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2268_cor_with_default_then_insert",
      "num": 2268,
      "name": "cor_with_default_then_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2268_cor_with_default_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2268_cor_with_default_then_insert.py",
      "description": "CREATE OR REPLACE with new DEFAULT then INSERT omitting column",
      "status": "pass",
      "duration_ms": 1272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:02.078284+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2269_cor_remove_property",
      "num": 2269,
      "name": "cor_remove_property",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2269_cor_remove_property.sql",
      "read_script": "generator/spark-reads-df/verify_2269_cor_remove_property.py",
      "description": "CREATE OR REPLACE without TBLPROPERTIES removes them",
      "status": "pass",
      "duration_ms": 1391,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:03.470144+00:00",
      "read_cold_ms": 803,
      "read_warm_ms": 311,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 157,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/226_extreme_versions",
      "num": 226,
      "name": "extreme_versions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/226_extreme_versions.sql",
      "read_script": "generator/spark-reads-df/verify_226_extreme_versions.py",
      "description": "CHAOS TEST WORKFLOW (Extreme Versions): This test verifies handling tables with multiple versions. Tests log replay, checkpoint loading.",
      "status": "pass",
      "duration_ms": 3028,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:07.441332+00:00",
      "read_cold_ms": 1788,
      "read_warm_ms": 560,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1031,
      "write_warm_ms": 813,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2270_cor_then_merge",
      "num": 2270,
      "name": "cor_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2270_cor_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2270_cor_then_merge.py",
      "description": "CREATE OR REPLACE then MERGE on the new schema",
      "status": "pass",
      "duration_ms": 1734,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:05.204444+00:00",
      "read_cold_ms": 903,
      "read_warm_ms": 416,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 342,
      "write_warm_ms": 94,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2271_default_decimal_literal",
      "num": 2271,
      "name": "default_decimal_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2271_default_decimal_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2271_default_decimal_literal.py",
      "description": "DEFAULT with DECIMAL(10,2) literal",
      "status": "pass",
      "duration_ms": 1301,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:06.506334+00:00",
      "read_cold_ms": 735,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
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        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2272_default_date_literal",
      "num": 2272,
      "name": "default_date_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2272_default_date_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2272_default_date_literal.py",
      "description": "DEFAULT with DATE literal",
      "status": "pass",
      "duration_ms": 1310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:07.817089+00:00",
      "read_cold_ms": 724,
      "read_warm_ms": 288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2273_default_timestamp_literal",
      "num": 2273,
      "name": "default_timestamp_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2273_default_timestamp_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2273_default_timestamp_literal.py",
      "description": "DEFAULT with TIMESTAMP literal",
      "status": "pass",
      "duration_ms": 1285,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:09.103000+00:00",
      "read_cold_ms": 715,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2274_default_boolean_literal",
      "num": 2274,
      "name": "default_boolean_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2274_default_boolean_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2274_default_boolean_literal.py",
      "description": "DEFAULT BOOLEAN true",
      "status": "pass",
      "duration_ms": 1363,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:10.466986+00:00",
      "read_cold_ms": 790,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2275_default_bigint_literal",
      "num": 2275,
      "name": "default_bigint_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2275_default_bigint_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2275_default_bigint_literal.py",
      "description": "DEFAULT BIGINT large value",
      "status": "pass",
      "duration_ms": 1320,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:11.787856+00:00",
      "read_cold_ms": 719,
      "read_warm_ms": 316,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2276_default_double_literal",
      "num": 2276,
      "name": "default_double_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2276_default_double_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2276_default_double_literal.py",
      "description": "DEFAULT DOUBLE value",
      "status": "pass",
      "duration_ms": 1287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:13.075578+00:00",
      "read_cold_ms": 733,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
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      "tags": [
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        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2277_default_with_check_constraint",
      "num": 2277,
      "name": "default_with_check_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2277_default_with_check_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2277_default_with_check_constraint.py",
      "description": "DEFAULT value satisfies CHECK constraint",
      "status": "pass",
      "duration_ms": 1323,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:14.399215+00:00",
      "read_cold_ms": 730,
      "read_warm_ms": 283,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2278_default_then_evolve_add_default",
      "num": 2278,
      "name": "default_then_evolve_add_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2278_default_then_evolve_add_default.sql",
      "read_script": "generator/spark-reads-df/verify_2278_default_then_evolve_add_default.py",
      "description": "Existing DEFAULT col preserved when ALTER ADD COLUMN adds another DEFAULT col",
      "status": "pass",
      "duration_ms": 1329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:15.728535+00:00",
      "read_cold_ms": 752,
      "read_warm_ms": 273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 438,
      "write_warm_ms": 197,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2279_gencol_case_expression",
      "num": 2279,
      "name": "gencol_case_expression",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2279_gencol_case_expression.sql",
      "read_script": "generator/spark-reads-df/verify_2279_gencol_case_expression.py",
      "description": "GENERATED column using CASE expression",
      "status": "pass",
      "duration_ms": 1336,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:17.065631+00:00",
      "read_cold_ms": 745,
      "read_warm_ms": 307,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 39,
      "tags": [
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        "delta:deletion-vectors",
        "delta:generated-columns",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/227_identity_basic",
      "num": 227,
      "name": "identity_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/227_identity_basic.sql",
      "read_script": "generator/spark-reads-df/verify_227_identity_basic.py",
      "description": "Basic Identity Column - GENERATED ALWAYS mode",
      "status": "pass",
      "duration_ms": 2371,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:09.814275+00:00",
      "read_cold_ms": 1413,
      "read_warm_ms": 548,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 193,
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      "tags": [
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        "type:timestamp",
        "dml:insert",
        "delta:identity-columns",
        "scale:large-dataset",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2280_gencol_three_arg_arithmetic",
      "num": 2280,
      "name": "gencol_three_arg_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2280_gencol_three_arg_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_2280_gencol_three_arg_arithmetic.py",
      "description": "GENERATED column combining 3 input columns",
      "status": "pass",
      "duration_ms": 1357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:18.422925+00:00",
      "read_cold_ms": 748,
      "read_warm_ms": 295,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2281_gencol_after_two_updates",
      "num": 2281,
      "name": "gencol_after_two_updates",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2281_gencol_after_two_updates.sql",
      "read_script": "generator/spark-reads-df/verify_2281_gencol_after_two_updates.py",
      "description": "Two consecutive UPDATEs on a base column, GENERATED must reflect latest value",
      "status": "pass",
      "duration_ms": 1911,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:20.335087+00:00",
      "read_cold_ms": 1020,
      "read_warm_ms": 431,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
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      "tags": [
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        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2282_gencol_after_delete",
      "num": 2282,
      "name": "gencol_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2282_gencol_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2282_gencol_after_delete.py",
      "description": "GENERATED column values preserved correctly after DELETE",
      "status": "pass",
      "duration_ms": 1940,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:22.276006+00:00",
      "read_cold_ms": 1042,
      "read_warm_ms": 434,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 78,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2283_gencol_partition_then_optimize",
      "num": 2283,
      "name": "gencol_partition_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2283_gencol_partition_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2283_gencol_partition_then_optimize.py",
      "description": "GENERATED column on partitioned table, OPTIMIZE preserves values",
      "status": "pass",
      "duration_ms": 1446,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:23.722236+00:00",
      "read_cold_ms": 789,
      "read_warm_ms": 325,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2284_gencol_update_non_dep_column",
      "num": 2284,
      "name": "gencol_update_non_dep_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2284_gencol_update_non_dep_column.sql",
      "read_script": "generator/spark-reads-df/verify_2284_gencol_update_non_dep_column.py",
      "description": "UPDATE on non-dependency column must NOT touch GENERATED column",
      "status": "pass",
      "duration_ms": 1999,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:25.722099+00:00",
      "read_cold_ms": 1042,
      "read_warm_ms": 430,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 141,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2285_tsntz_partition",
      "num": 2285,
      "name": "tsntz_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2285_tsntz_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2285_tsntz_partition.py",
      "description": "TIMESTAMP_NTZ as a partition column",
      "status": "pass",
      "duration_ms": 1431,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:27.154250+00:00",
      "read_cold_ms": 797,
      "read_warm_ms": 309,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 170,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2286_tsntz_with_cdc",
      "num": 2286,
      "name": "tsntz_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2286_tsntz_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2286_tsntz_with_cdc.py",
      "description": null,
      "status": "pass",
      "duration_ms": 2656,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:29.811285+00:00",
      "read_cold_ms": 1094,
      "read_warm_ms": 489,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 410,
      "write_warm_ms": 288,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2287_tsntz_time_travel",
      "num": 2287,
      "name": "tsntz_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2287_tsntz_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_2287_tsntz_time_travel.py",
      "description": "TIMESTAMP_NTZ + version time travel",
      "status": "pass",
      "duration_ms": 2051,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:31.863012+00:00",
      "read_cold_ms": 848,
      "read_warm_ms": 334,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 430,
      "write_warm_ms": 166,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2288_tsntz_update",
      "num": 2288,
      "name": "tsntz_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2288_tsntz_update.sql",
      "read_script": "generator/spark-reads-df/verify_2288_tsntz_update.py",
      "description": "UPDATE on TIMESTAMP_NTZ column",
      "status": "pass",
      "duration_ms": 2089,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:33.952618+00:00",
      "read_cold_ms": 1061,
      "read_warm_ms": 485,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 249,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2289_tsntz_merge",
      "num": 2289,
      "name": "tsntz_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2289_tsntz_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2289_tsntz_merge.py",
      "description": "MERGE NOT MATCHED INSERT into TIMESTAMP_NTZ table",
      "status": "pass",
      "duration_ms": 1622,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:35.575667+00:00",
      "read_cold_ms": 895,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/228_identity_by_default",
      "num": 228,
      "name": "identity_by_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/228_identity_by_default.sql",
      "read_script": "generator/spark-reads-df/verify_228_identity_by_default.py",
      "description": "GENERATED BY DEFAULT AS IDENTITY mode",
      "status": "pass",
      "duration_ms": 2670,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:12.485698+00:00",
      "read_cold_ms": 2159,
      "read_warm_ms": 209,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 114,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2290_tsntz_min_max_stats",
      "num": 2290,
      "name": "tsntz_min_max_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2290_tsntz_min_max_stats.sql",
      "read_script": "generator/spark-reads-df/verify_2290_tsntz_min_max_stats.py",
      "description": "TIMESTAMP_NTZ with min/max statistics in delta log",
      "status": "pass",
      "duration_ms": 1577,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:37.153389+00:00",
      "read_cold_ms": 842,
      "read_warm_ms": 343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2291_vacuum_dry_run",
      "num": 2291,
      "name": "vacuum_dry_run",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2291_vacuum_dry_run.sql",
      "read_script": "generator/spark-reads-df/verify_2291_vacuum_dry_run.py",
      "description": "VACUUM DRY RUN must NOT delete files or write commit",
      "status": "pass",
      "duration_ms": 2323,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:39.476727+00:00",
      "read_cold_ms": 1173,
      "read_warm_ms": 544,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2292_vacuum_idempotent",
      "num": 2292,
      "name": "vacuum_idempotent",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2292_vacuum_idempotent.sql",
      "read_script": "generator/spark-reads-df/verify_2292_vacuum_idempotent.py",
      "description": "Running VACUUM twice in a row -- second should be no-op (no extra files removed)",
      "status": "pass",
      "duration_ms": 2430,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:41.907926+00:00",
      "read_cold_ms": 1182,
      "read_warm_ms": 625,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2293_vacuum_after_optimize_then_delete",
      "num": 2293,
      "name": "vacuum_after_optimize_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2293_vacuum_after_optimize_then_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2293_vacuum_after_optimize_then_delete.py",
      "description": "OPTIMIZE then DELETE then VACUUM",
      "status": "pass",
      "duration_ms": 2667,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:44.575695+00:00",
      "read_cold_ms": 1318,
      "read_warm_ms": 657,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 190,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2294_vacuum_no_files_to_delete",
      "num": 2294,
      "name": "vacuum_no_files_to_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2294_vacuum_no_files_to_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2294_vacuum_no_files_to_delete.py",
      "description": "VACUUM on a table with no candidate files (no deletes) still emits a commit",
      "status": "pass",
      "duration_ms": 1988,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:46.564611+00:00",
      "read_cold_ms": 989,
      "read_warm_ms": 454,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2295_vacuum_after_multiple_delete_cycles",
      "num": 2295,
      "name": "vacuum_after_multiple_delete_cycles",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2295_vacuum_after_multiple_delete_cycles.sql",
      "read_script": "generator/spark-reads-df/verify_2295_vacuum_after_multiple_delete_cycles.py",
      "description": "Three DELETE cycles followed by VACUUM",
      "status": "pass",
      "duration_ms": 3046,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:49.611432+00:00",
      "read_cold_ms": 1431,
      "read_warm_ms": 772,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
      "write_warm_ms": 243,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2296_map_literal_int_values",
      "num": 2296,
      "name": "map_literal_int_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2296_map_literal_int_values.sql",
      "read_script": "generator/spark-reads-df/verify_2296_map_literal_int_values.py",
      "description": "MAP literal with int values in INSERT",
      "status": "pass",
      "duration_ms": 2373,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:51.985508+00:00",
      "read_cold_ms": 1274,
      "read_warm_ms": 566,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2297_map_literal_in_insert_select",
      "num": 2297,
      "name": "map_literal_in_insert_select",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2297_map_literal_in_insert_select.sql",
      "read_script": "generator/spark-reads-df/verify_2297_map_literal_in_insert_select.py",
      "description": "MAP literal across multiple INSERT SELECT batches",
      "status": "pass",
      "duration_ms": 1818,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:53.804640+00:00",
      "read_cold_ms": 1287,
      "read_warm_ms": 244,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 331,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2298_map_literal_three_keys",
      "num": 2298,
      "name": "map_literal_three_keys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2298_map_literal_three_keys.sql",
      "read_script": "generator/spark-reads-df/verify_2298_map_literal_three_keys.py",
      "description": "MAP literal with three key-value pairs",
      "status": "pass",
      "duration_ms": 1332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:55.137541+00:00",
      "read_cold_ms": 815,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2299_drop_table_with_files_explicit",
      "num": 2299,
      "name": "drop_table_with_files_explicit",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2299_drop_table_with_files_explicit.sql",
      "read_script": "generator/spark-reads-df/verify_2299_drop_table_with_files_explicit.py",
      "description": "DROP TABLE WITH FILES then re-CREATE at same LOCATION",
      "status": "pass",
      "duration_ms": 1232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:56.370618+00:00",
      "read_cold_ms": 680,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 449,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/229_identity_hwm",
      "num": 229,
      "name": "identity_hwm",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/229_identity_hwm.sql",
      "read_script": "generator/spark-reads-df/verify_229_identity_hwm.py",
      "description": "Validates high watermark tracking table with 50 sequential events. 5 batches of 10 rows each, ids 1-50, event_type \"event_{i}\", event_time = BASE + i*1_000_000 microseconds.",
      "status": "pass",
      "duration_ms": 3118,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:15.604984+00:00",
      "read_cold_ms": 1612,
      "read_warm_ms": 169,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2598,
      "write_warm_ms": 3111,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:identity-columns",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/22_action_sidecar_file_reference",
      "num": 22,
      "name": "action_sidecar_file_reference",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/22_action_sidecar_file_reference.sql",
      "read_script": "generator/spark-reads-df/verify_22_action_sidecar_file_reference.py",
      "description": "Demonstrates sidecar file information action for V2 checkpoints. Sidecar files split checkpoint data into manageable pieces for scalability.",
      "status": "pass",
      "duration_ms": 3885,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:19.491164+00:00",
      "read_cold_ms": 2271,
      "read_warm_ms": 684,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 897,
      "write_warm_ms": 626,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:checkpoint-sidecar",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2300_drop_table_if_exists_then_create",
      "num": 2300,
      "name": "drop_table_if_exists_then_create",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2300_drop_table_if_exists_then_create.sql",
      "read_script": "generator/spark-reads-df/verify_2300_drop_table_if_exists_then_create.py",
      "description": "DROP TABLE IF EXISTS on existing table, then CREATE fresh",
      "status": "pass",
      "duration_ms": 1221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:57.592071+00:00",
      "read_cold_ms": 719,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 460,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2301_vacuum_retention_tombstone_check",
      "num": 2301,
      "name": "vacuum_retention_tombstone_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2301_vacuum_retention_tombstone_check.sql",
      "read_script": "generator/spark-reads-df/verify_2301_vacuum_retention_tombstone_check.py",
      "description": "VACUUM RETAIN 0 HOURS removes tombstoned files; verify VACUUM commit emitted",
      "status": "pass",
      "duration_ms": 1661,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:30:59.253406+00:00",
      "read_cold_ms": 911,
      "read_warm_ms": 365,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 185,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2302_vacuum_after_restore",
      "num": 2302,
      "name": "vacuum_after_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2302_vacuum_after_restore.sql",
      "read_script": "generator/spark-reads-df/verify_2302_vacuum_after_restore.py",
      "description": "RESTORE to earlier version then VACUUM removes files added after restore point",
      "status": "pass",
      "duration_ms": 1215,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:00.469508+00:00",
      "read_cold_ms": 718,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 196,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2303_vacuum_zorder_then_purge",
      "num": 2303,
      "name": "vacuum_zorder_then_purge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2303_vacuum_zorder_then_purge.sql",
      "read_script": "generator/spark-reads-df/verify_2303_vacuum_zorder_then_purge.py",
      "description": "OPTIMIZE ZORDER then VACUUM purges old pre-zorder files",
      "status": "pass",
      "duration_ms": 1284,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:01.754523+00:00",
      "read_cold_ms": 752,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 523,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2304_vacuum_partitioned_preserves_dirs",
      "num": 2304,
      "name": "vacuum_partitioned_preserves_dirs",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2304_vacuum_partitioned_preserves_dirs.sql",
      "read_script": "generator/spark-reads-df/verify_2304_vacuum_partitioned_preserves_dirs.py",
      "description": "VACUUM on partitioned table preserves partition directories with live data",
      "status": "pass",
      "duration_ms": 1621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:03.375745+00:00",
      "read_cold_ms": 864,
      "read_warm_ms": 347,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 617,
      "write_warm_ms": 381,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2305_optimize_single_file_noop",
      "num": 2305,
      "name": "optimize_single_file_noop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2305_optimize_single_file_noop.sql",
      "read_script": "generator/spark-reads-df/verify_2305_optimize_single_file_noop.py",
      "description": "OPTIMIZE on a table with already a single file is effectively a no-op",
      "status": "pass",
      "duration_ms": 1254,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:04.630786+00:00",
      "read_cold_ms": 703,
      "read_warm_ms": 282,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2306_optimize_empty_table",
      "num": 2306,
      "name": "optimize_empty_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2306_optimize_empty_table.sql",
      "read_script": "generator/spark-reads-df/verify_2306_optimize_empty_table.py",
      "description": "OPTIMIZE on an empty table is a no-op but should not error",
      "status": "pass",
      "duration_ms": 1225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:05.856212+00:00",
      "read_cold_ms": 719,
      "read_warm_ms": 245,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2307_optimize_dv_only_table",
      "num": 2307,
      "name": "optimize_dv_only_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2307_optimize_dv_only_table.sql",
      "read_script": "generator/spark-reads-df/verify_2307_optimize_dv_only_table.py",
      "description": "OPTIMIZE on a table whose only modifications are DV-tagged deletes",
      "status": "pass",
      "duration_ms": 1619,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:07.475605+00:00",
      "read_cold_ms": 845,
      "read_warm_ms": 363,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2308_reorg_purge_after_merge",
      "num": 2308,
      "name": "reorg_purge_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2308_reorg_purge_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2308_reorg_purge_after_merge.py",
      "description": "REORG TABLE ... APPLY (PURGE) after MERGE rewrites files to physically remove deleted rows",
      "status": "pass",
      "duration_ms": 1688,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:09.164229+00:00",
      "read_cold_ms": 915,
      "read_warm_ms": 372,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
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      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2309_reorg_purge_partitioned",
      "num": 2309,
      "name": "reorg_purge_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2309_reorg_purge_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_2309_reorg_purge_partitioned.py",
      "description": "REORG TABLE APPLY (PURGE) on a partitioned table",
      "status": "pass",
      "duration_ms": 1750,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:10.915085+00:00",
      "read_cold_ms": 977,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 202,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/230_identity_blocks",
      "num": 230,
      "name": "identity_blocks",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/230_identity_blocks.sql",
      "read_script": "generator/spark-reads-df/verify_230_identity_blocks.py",
      "description": "Validates block allocation table with 100 rows. id: 1-100, worker_id: cycles 1-4, data: \"initial_data_{i}\" where i=0..99.",
      "status": "pass",
      "duration_ms": 2727,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:22.219338+00:00",
      "read_cold_ms": 1694,
      "read_warm_ms": 532,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 6337,
      "write_warm_ms": 5904,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:constraints",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2310_zorder_single_col_with_nulls",
      "num": 2310,
      "name": "zorder_single_col_with_nulls",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2310_zorder_single_col_with_nulls.sql",
      "read_script": "generator/spark-reads-df/verify_2310_zorder_single_col_with_nulls.py",
      "description": "ZORDER on a single column that contains nulls",
      "status": "pass",
      "duration_ms": 1227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:12.142819+00:00",
      "read_cold_ms": 709,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
      "write_warm_ms": 213,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2311_zorder_three_cols_mixed",
      "num": 2311,
      "name": "zorder_three_cols_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2311_zorder_three_cols_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_2311_zorder_three_cols_mixed.py",
      "description": "ZORDER BY three columns of mixed types",
      "status": "pass",
      "duration_ms": 1235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:13.378305+00:00",
      "read_cold_ms": 692,
      "read_warm_ms": 270,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 362,
      "write_warm_ms": 125,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2312_zorder_after_schema_evolve",
      "num": 2312,
      "name": "zorder_after_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2312_zorder_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2312_zorder_after_schema_evolve.py",
      "description": "Add a new column then ZORDER BY the new column",
      "status": "pass",
      "duration_ms": 1248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:14.626535+00:00",
      "read_cold_ms": 738,
      "read_warm_ms": 250,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 326,
      "write_warm_ms": 272,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2313_optimize_where_partition_predicate",
      "num": 2313,
      "name": "optimize_where_partition_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2313_optimize_where_partition_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_2313_optimize_where_partition_predicate.py",
      "description": "OPTIMIZE WHERE partition predicate compacts only the targeted partition",
      "status": "pass",
      "duration_ms": 1359,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:15.986642+00:00",
      "read_cold_ms": 806,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 229,
      "write_warm_ms": 286,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2314_zorder_string_col_locality",
      "num": 2314,
      "name": "zorder_string_col_locality",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2314_zorder_string_col_locality.sql",
      "read_script": "generator/spark-reads-df/verify_2314_zorder_string_col_locality.py",
      "description": "ZORDER BY a string column to test string locality clustering",
      "status": "pass",
      "duration_ms": 1257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:17.244626+00:00",
      "read_cold_ms": 717,
      "read_warm_ms": 252,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 167,
      "write_warm_ms": 79,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2315_zorder_date_col_locality",
      "num": 2315,
      "name": "zorder_date_col_locality",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2315_zorder_date_col_locality.sql",
      "read_script": "generator/spark-reads-df/verify_2315_zorder_date_col_locality.py",
      "description": "ZORDER BY a DATE column",
      "status": "pass",
      "duration_ms": 1222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:18.467615+00:00",
      "read_cold_ms": 706,
      "read_warm_ms": 252,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 178,
      "write_warm_ms": 273,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2316_optimize_sequence_idempotent",
      "num": 2316,
      "name": "optimize_sequence_idempotent",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2316_optimize_sequence_idempotent.sql",
      "read_script": "generator/spark-reads-df/verify_2316_optimize_sequence_idempotent.py",
      "description": "Multiple consecutive OPTIMIZE commands are idempotent",
      "status": "pass",
      "duration_ms": 1248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:19.715931+00:00",
      "read_cold_ms": 696,
      "read_warm_ms": 254,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 486,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2317_optimize_then_vacuum_retain_zero",
      "num": 2317,
      "name": "optimize_then_vacuum_retain_zero",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2317_optimize_then_vacuum_retain_zero.sql",
      "read_script": "generator/spark-reads-df/verify_2317_optimize_then_vacuum_retain_zero.py",
      "description": "OPTIMIZE then VACUUM RETAIN 0 HOURS removes old small files immediately",
      "status": "pass",
      "duration_ms": 1241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:20.957525+00:00",
      "read_cold_ms": 688,
      "read_warm_ms": 277,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 449,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2318_vacuum_after_cdc_enabled",
      "num": 2318,
      "name": "vacuum_after_cdc_enabled",
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      "write_script": "generator/df-sql/2318_vacuum_after_cdc_enabled.sql",
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      "description": "Enable CDC, do DML, then VACUUM",
      "status": "pass",
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      "error": null,
      "notes": null,
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        "dml:delete",
        "dml:insert",
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      "name": "checkpoint_after_ten_commits",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1565,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:24.155513+00:00",
      "read_cold_ms": 1022,
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/231_multi_identity",
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      "writer": "delta-forge",
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      "duration_ms": 2140,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-20T00:46:24.360139+00:00",
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        "type:string",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2320_checkpoint_after_evolve_schema",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Schema evolve then more commits should yield checkpoint capturing new schema",
      "status": "pass",
      "duration_ms": 1631,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:25.786928+00:00",
      "read_cold_ms": 1085,
      "read_warm_ms": 255,
      "skip_feature": null,
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        "dml:insert",
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        "schema:add-column",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2321_optimize_with_column_mapping",
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      "reader": "spark",
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      "description": "OPTIMIZE on a table with column mapping mode = name",
      "status": "pass",
      "duration_ms": 1249,
      "error": null,
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      "read_cold_ms": 694,
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      "skip_feature": null,
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      "write_cold_ms": 365,
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        "dml:insert",
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        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2322_reorg_apply_purge_keeps_count",
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      "status": "pass",
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      "extras": {},
      "verified_at": "2026-05-14T15:31:28.269939+00:00",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2323_vacuum_with_retention_property",
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      "description": "Set delta.deletedFileRetentionDuration property and run VACUUM",
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        "type:integer",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2324_optimize_not_null_constraint",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2324_optimize_not_null_constraint.sql",
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      "description": "OPTIMIZE on a table with NOT NULL constraint preserves the constraint and data",
      "status": "pass",
      "duration_ms": 1250,
      "error": null,
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      "skip_feature": null,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2325_zorder_then_immediate_update",
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      "reader": "spark",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2326_identity_start_1000_inc_5",
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      "reader": "spark",
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      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:31:34.149445+00:00",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2327_identity_generated_always",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2327_identity_generated_always.py",
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      "error": null,
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      "extras": {},
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2328_identity_survives_optimize",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "IDENTITY column survives OPTIMIZE compaction",
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      "error": null,
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      "extras": {},
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2329_identity_resume_after_delete_all",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2329_identity_resume_after_delete_all.sql",
      "read_script": "generator/spark-reads-df/verify_2329_identity_resume_after_delete_all.py",
      "description": "IDENTITY resumes from high-water mark after DELETE all rows",
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      "duration_ms": 1269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:37.923163+00:00",
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        "dml:delete",
        "dml:insert",
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        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/232_identity_delete",
      "num": 232,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/232_identity_delete.sql",
      "read_script": "generator/spark-reads-df/verify_232_identity_delete.py",
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      "status": "pass",
      "duration_ms": 3565,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:46:27.926401+00:00",
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      "skip_feature": null,
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        "delta:optimize",
        "direction:df-writes",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2330_identity_multi_batch_sum",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2330_identity_multi_batch_sum.py",
      "description": "IDENTITY across multiple INSERT batches; verify total sum is correct",
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      "duration_ms": 1274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:39.197804+00:00",
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        "dml:insert",
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        "delta:identity-columns",
        "scale:many-batches",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2331_default_null_nullable",
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      "name": "default_null_nullable",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2331_default_null_nullable.sql",
      "read_script": "generator/spark-reads-df/verify_2331_default_null_nullable.py",
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      "status": "pass",
      "duration_ms": 1242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:40.440647+00:00",
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2332_default_date_literal",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2332_default_date_literal.sql",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:41.687842+00:00",
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2333_default_alter_set_new",
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      "name": "default_alter_set_new",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:42.931520+00:00",
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2334_default_int_then_update",
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      "name": "default_int_then_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "DEFAULT INT applied, then UPDATE half the rows to non-default",
      "status": "pass",
      "duration_ms": 1668,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:44.599783+00:00",
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      "tags": [
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2335_default_empty_string",
      "num": 2335,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2335_default_empty_string.sql",
      "read_script": "generator/spark-reads-df/verify_2335_default_empty_string.py",
      "description": "DEFAULT '' empty string literal",
      "status": "pass",
      "duration_ms": 1292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:45.892203+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2336_gencol_concat_strings",
      "num": 2336,
      "name": "gencol_concat_strings",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2336_gencol_concat_strings.sql",
      "read_script": "generator/spark-reads-df/verify_2336_gencol_concat_strings.py",
      "description": "GENERATED column from CONCAT of two strings",
      "status": "pass",
      "duration_ms": 1290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:47.183009+00:00",
      "read_cold_ms": 731,
      "read_warm_ms": 258,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 67,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2337_gencol_year_of_date",
      "num": 2337,
      "name": "gencol_year_of_date",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2337_gencol_year_of_date.sql",
      "read_script": "generator/spark-reads-df/verify_2337_gencol_year_of_date.py",
      "description": "GENERATED column = YEAR(date_col)",
      "status": "pass",
      "duration_ms": 1267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:48.451057+00:00",
      "read_cold_ms": 691,
      "read_warm_ms": 279,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 47,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2338_gencol_month_of_ts",
      "num": 2338,
      "name": "gencol_month_of_ts",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2338_gencol_month_of_ts.sql",
      "read_script": "generator/spark-reads-df/verify_2338_gencol_month_of_ts.py",
      "description": "GENERATED column = MONTH(timestamp)",
      "status": "pass",
      "duration_ms": 1265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:49.716443+00:00",
      "read_cold_ms": 692,
      "read_warm_ms": 278,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 42,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2339_gencol_three_col_arithmetic",
      "num": 2339,
      "name": "gencol_three_col_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2339_gencol_three_col_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_2339_gencol_three_col_arithmetic.py",
      "description": "GENERATED column = (a + b) * c",
      "status": "pass",
      "duration_ms": 1292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:51.008976+00:00",
      "read_cold_ms": 747,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 46,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/233_identity_merge",
      "num": 233,
      "name": "identity_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/233_identity_merge.sql",
      "read_script": "generator/spark-reads-df/verify_233_identity_merge.py",
      "description": "Validates identity with MERGE base table. 3 customer rows with explicit IDs. (1,\"C001\",\"c1@email.com\",ts), (2,\"C002\",\"c2@email.com\",ts), (3,\"C003\",\"c3@email.com\",ts)",
      "status": "pass",
      "duration_ms": 1530,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:29.457365+00:00",
      "read_cold_ms": 1202,
      "read_warm_ms": 154,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 22,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:identity-columns",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2340_gencol_merge_insert",
      "num": 2340,
      "name": "gencol_merge_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2340_gencol_merge_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2340_gencol_merge_insert.py",
      "description": "GENERATED column populated correctly via MERGE NOT MATCHED INSERT",
      "status": "pass",
      "duration_ms": 1311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:52.320456+00:00",
      "read_cold_ms": 743,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2341_gencol_type_cast",
      "num": 2341,
      "name": "gencol_type_cast",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2341_gencol_type_cast.sql",
      "read_script": "generator/spark-reads-df/verify_2341_gencol_type_cast.py",
      "description": "GENERATED column with explicit CAST (INT -> STRING)",
      "status": "pass",
      "duration_ms": 1272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:53.593564+00:00",
      "read_cold_ms": 708,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 56,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2342_tsntz_min_year_1970",
      "num": 2342,
      "name": "tsntz_min_year_1970",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2342_tsntz_min_year_1970.sql",
      "read_script": "generator/spark-reads-df/verify_2342_tsntz_min_year_1970.py",
      "description": "TIMESTAMP_NTZ with epoch year 1970",
      "status": "pass",
      "duration_ms": 1283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:54.877215+00:00",
      "read_cold_ms": 725,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 18,
      "write_warm_ms": 37,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2343_tsntz_microsecond_precision",
      "num": 2343,
      "name": "tsntz_microsecond_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2343_tsntz_microsecond_precision.sql",
      "read_script": "generator/spark-reads-df/verify_2343_tsntz_microsecond_precision.py",
      "description": "TIMESTAMP_NTZ values with microsecond precision",
      "status": "pass",
      "duration_ms": 1309,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:56.186425+00:00",
      "read_cold_ms": 747,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 20,
      "write_warm_ms": 31,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2344_tsntz_merge_update_clause",
      "num": 2344,
      "name": "tsntz_merge_update_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2344_tsntz_merge_update_clause.sql",
      "read_script": "generator/spark-reads-df/verify_2344_tsntz_merge_update_clause.py",
      "description": "TIMESTAMP_NTZ updated via MERGE WHEN MATCHED THEN UPDATE SET",
      "status": "pass",
      "duration_ms": 1832,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:58.019166+00:00",
      "read_cold_ms": 944,
      "read_warm_ms": 437,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2345_tsntz_zorder",
      "num": 2345,
      "name": "tsntz_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2345_tsntz_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_2345_tsntz_zorder.py",
      "description": "TIMESTAMP_NTZ column used as Z-ORDER key",
      "status": "pass",
      "duration_ms": 1330,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:31:59.350009+00:00",
      "read_cold_ms": 783,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2346_default_identity_generated_combo",
      "num": 2346,
      "name": "default_identity_generated_combo",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2346_default_identity_generated_combo.sql",
      "read_script": "generator/spark-reads-df/verify_2346_default_identity_generated_combo.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1384,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:00.734379+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 281,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 39,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2347_gencol_coalesce",
      "num": 2347,
      "name": "gencol_coalesce",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2347_gencol_coalesce.sql",
      "read_script": "generator/spark-reads-df/verify_2347_gencol_coalesce.py",
      "description": "GENERATED column using COALESCE on nullable input",
      "status": "pass",
      "duration_ms": 1345,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:02.080213+00:00",
      "read_cold_ms": 780,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 39,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2348_identity_update_other_cols",
      "num": 2348,
      "name": "identity_update_other_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2348_identity_update_other_cols.sql",
      "read_script": "generator/spark-reads-df/verify_2348_identity_update_other_cols.py",
      "description": "IDENTITY column remains stable when other columns are UPDATEd",
      "status": "pass",
      "duration_ms": 1891,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:03.971913+00:00",
      "read_cold_ms": 1055,
      "read_warm_ms": 390,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 208,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2349_alter_add_column_with_default",
      "num": 2349,
      "name": "alter_add_column_with_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2349_alter_add_column_with_default.sql",
      "read_script": "generator/spark-reads-df/verify_2349_alter_add_column_with_default.py",
      "description": "ALTER TABLE ADD COLUMN with DEFAULT applied to subsequent inserts",
      "status": "pass",
      "duration_ms": 1378,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:05.350576+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 285,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 159,
      "write_warm_ms": 181,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/234_identity_resume",
      "num": 234,
      "name": "identity_resume",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/234_identity_resume.sql",
      "read_script": "generator/spark-reads-df/verify_234_identity_resume.py",
      "description": "Identity Resume After DBX Insert",
      "status": "pass",
      "duration_ms": 1458,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:30.916705+00:00",
      "read_cold_ms": 972,
      "read_warm_ms": 135,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 24,
      "write_warm_ms": 19,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2350_gencol_from_partition_col",
      "num": 2350,
      "name": "gencol_from_partition_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2350_gencol_from_partition_col.sql",
      "read_script": "generator/spark-reads-df/verify_2350_gencol_from_partition_col.py",
      "description": "GENERATED column referencing source of a partition column",
      "status": "pass",
      "duration_ms": 1346,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:06.697498+00:00",
      "read_cold_ms": 765,
      "read_warm_ms": 288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 73,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2351_merge_matched_with_filter",
      "num": 2351,
      "name": "merge_matched_with_filter",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2351_merge_matched_with_filter.sql",
      "read_script": "generator/spark-reads-df/verify_2351_merge_matched_with_filter.py",
      "description": "MERGE WHEN MATCHED AND <cond> THEN UPDATE -- only rows passing the filter update",
      "status": "pass",
      "duration_ms": 1845,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:08.543142+00:00",
      "read_cold_ms": 983,
      "read_warm_ms": 401,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2352_merge_nmbs_delete",
      "num": 2352,
      "name": "merge_nmbs_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2352_merge_nmbs_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2352_merge_nmbs_delete.py",
      "description": "MERGE WHEN NOT MATCHED BY SOURCE THEN DELETE -- prunes target rows missing in source",
      "status": "pass",
      "duration_ms": 1905,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:10.449284+00:00",
      "read_cold_ms": 998,
      "read_warm_ms": 421,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2353_merge_nmbs_update",
      "num": 2353,
      "name": "merge_nmbs_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2353_merge_nmbs_update.sql",
      "read_script": "generator/spark-reads-df/verify_2353_merge_nmbs_update.py",
      "description": "MERGE WHEN NOT MATCHED BY SOURCE THEN UPDATE -- mark missing rows as inactive",
      "status": "pass",
      "duration_ms": 1846,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:12.295894+00:00",
      "read_cold_ms": 966,
      "read_warm_ms": 405,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 212,
      "write_warm_ms": 195,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2354_merge_multi_matched_clauses",
      "num": 2354,
      "name": "merge_multi_matched_clauses",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2354_merge_multi_matched_clauses.sql",
      "read_script": "generator/spark-reads-df/verify_2354_merge_multi_matched_clauses.py",
      "description": "MERGE with multiple WHEN MATCHED clauses (delete then update)",
      "status": "pass",
      "duration_ms": 1955,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:14.251182+00:00",
      "read_cold_ms": 1021,
      "read_warm_ms": 442,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 203,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2355_merge_source_aggregation",
      "num": 2355,
      "name": "merge_source_aggregation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2355_merge_source_aggregation.sql",
      "read_script": "generator/spark-reads-df/verify_2355_merge_source_aggregation.py",
      "description": "MERGE source is a subquery with GROUP BY aggregation",
      "status": "pass",
      "duration_ms": 1874,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:16.125819+00:00",
      "read_cold_ms": 947,
      "read_warm_ms": 439,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 157,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2356_merge_source_values_clause",
      "num": 2356,
      "name": "merge_source_values_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2356_merge_source_values_clause.sql",
      "read_script": "generator/spark-reads-df/verify_2356_merge_source_values_clause.py",
      "description": "MERGE source is an inline VALUES list",
      "status": "pass",
      "duration_ms": 1926,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:18.052201+00:00",
      "read_cold_ms": 989,
      "read_warm_ms": 463,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 226,
      "write_warm_ms": 120,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2357_merge_insert_star_match",
      "num": 2357,
      "name": "merge_insert_star_match",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2357_merge_insert_star_match.sql",
      "read_script": "generator/spark-reads-df/verify_2357_merge_insert_star_match.py",
      "description": "MERGE with INSERT * and UPDATE SET * shorthand on identical schemas",
      "status": "pass",
      "duration_ms": 2008,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:20.061369+00:00",
      "read_cold_ms": 1045,
      "read_warm_ms": 461,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 375,
      "write_warm_ms": 130,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2358_merge_partitioned_target",
      "num": 2358,
      "name": "merge_partitioned_target",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2358_merge_partitioned_target.sql",
      "read_script": "generator/spark-reads-df/verify_2358_merge_partitioned_target.py",
      "description": "MERGE into a partitioned target table",
      "status": "pass",
      "duration_ms": 2000,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:22.062499+00:00",
      "read_cold_ms": 1031,
      "read_warm_ms": 472,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 271,
      "write_warm_ms": 226,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2359_merge_with_cdf_enabled",
      "num": 2359,
      "name": "merge_with_cdf_enabled",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2359_merge_with_cdf_enabled.sql",
      "read_script": "generator/spark-reads-df/verify_2359_merge_with_cdf_enabled.py",
      "description": "MERGE on a table with Change Data Feed enabled (CDC events recorded)",
      "status": "pass",
      "duration_ms": 2278,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:24.341238+00:00",
      "read_cold_ms": 1098,
      "read_warm_ms": 470,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 149,
      "write_warm_ms": 229,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/235_default_literal",
      "num": 235,
      "name": "default_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/235_default_literal.sql",
      "read_script": "generator/spark-reads-df/verify_235_default_literal.py",
      "description": "Basic literal default values for columns",
      "status": "pass",
      "duration_ms": 2393,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:33.310626+00:00",
      "read_cold_ms": 1638,
      "read_warm_ms": 451,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 43,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2360_insert_overwrite_where_predicate",
      "num": 2360,
      "name": "insert_overwrite_where_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2360_insert_overwrite_where_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_2360_insert_overwrite_where_predicate.py",
      "description": "INSERT OVERWRITE with replaceWhere-style predicate (replace only one region)",
      "status": "pass",
      "duration_ms": 1581,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:25.923012+00:00",
      "read_cold_ms": 862,
      "read_warm_ms": 342,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 269,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2361_insert_overwrite_partitioned_full",
      "num": 2361,
      "name": "insert_overwrite_partitioned_full",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2361_insert_overwrite_partitioned_full.sql",
      "read_script": "generator/spark-reads-df/verify_2361_insert_overwrite_partitioned_full.py",
      "description": "INSERT OVERWRITE entire partitioned table",
      "status": "pass",
      "duration_ms": 1595,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:27.519180+00:00",
      "read_cold_ms": 863,
      "read_warm_ms": 355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2362_overwrite_then_insert_into",
      "num": 2362,
      "name": "overwrite_then_insert_into",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2362_overwrite_then_insert_into.sql",
      "read_script": "generator/spark-reads-df/verify_2362_overwrite_then_insert_into.py",
      "description": "INSERT OVERWRITE followed by INSERT INTO",
      "status": "pass",
      "duration_ms": 1635,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:29.154715+00:00",
      "read_cold_ms": 890,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 232,
      "write_warm_ms": 260,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2363_overwrite_single_partition",
      "num": 2363,
      "name": "overwrite_single_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2363_overwrite_single_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2363_overwrite_single_partition.py",
      "description": "Replace a single partition by overwriting with rows from one partition only",
      "status": "pass",
      "duration_ms": 1705,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:30.860584+00:00",
      "read_cold_ms": 932,
      "read_warm_ms": 370,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 218,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2364_update_delete_insert_chain",
      "num": 2364,
      "name": "update_delete_insert_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2364_update_delete_insert_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2364_update_delete_insert_chain.py",
      "description": "UPDATE then DELETE then INSERT chain hitting overlapping ids",
      "status": "pass",
      "duration_ms": 2461,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:33.322221+00:00",
      "read_cold_ms": 1166,
      "read_warm_ms": 642,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 193,
      "write_warm_ms": 120,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2365_delete_in_subquery",
      "num": 2365,
      "name": "delete_in_subquery",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2365_delete_in_subquery.sql",
      "read_script": "generator/spark-reads-df/verify_2365_delete_in_subquery.py",
      "description": "DELETE WHERE id IN (subquery from another delta table)",
      "status": "pass",
      "duration_ms": 2619,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:35.942231+00:00",
      "read_cold_ms": 1276,
      "read_warm_ms": 637,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2366_update_from_join_via_merge",
      "num": 2366,
      "name": "update_from_join_via_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2366_update_from_join_via_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2366_update_from_join_via_merge.py",
      "description": "Emulate UPDATE FROM <other table> via MERGE source subquery",
      "status": "pass",
      "duration_ms": 2722,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:38.665502+00:00",
      "read_cold_ms": 1316,
      "read_warm_ms": 697,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 112,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2367_merge_self_via_cte",
      "num": 2367,
      "name": "merge_self_via_cte",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2367_merge_self_via_cte.sql",
      "read_script": "generator/spark-reads-df/verify_2367_merge_self_via_cte.py",
      "description": null,
      "status": "pass",
      "duration_ms": 3023,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:41.689584+00:00",
      "read_cold_ms": 1411,
      "read_warm_ms": 762,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 90,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2368_merge_case_in_set",
      "num": 2368,
      "name": "merge_case_in_set",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2368_merge_case_in_set.sql",
      "read_script": "generator/spark-reads-df/verify_2368_merge_case_in_set.py",
      "description": "MERGE UPDATE SET uses CASE expression",
      "status": "pass",
      "duration_ms": 2861,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:44.551111+00:00",
      "read_cold_ms": 1671,
      "read_warm_ms": 806,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 146,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2369_merge_null_safe_match",
      "num": 2369,
      "name": "merge_null_safe_match",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2369_merge_null_safe_match.sql",
      "read_script": "generator/spark-reads-df/verify_2369_merge_null_safe_match.py",
      "description": "MERGE with NULL handling in match condition (rows with NULL key not matched)",
      "status": "pass",
      "duration_ms": 1915,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:46.466823+00:00",
      "read_cold_ms": 995,
      "read_warm_ms": 443,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/236_default_expression",
      "num": 236,
      "name": "default_expression",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/236_default_expression.sql",
      "read_script": "generator/spark-reads-df/verify_236_default_expression.py",
      "description": "Deterministic expression defaults for columns",
      "status": "pass",
      "duration_ms": 1842,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:35.153024+00:00",
      "read_cold_ms": 1352,
      "read_warm_ms": 196,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 51,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2370_merge_delete_then_reinsert",
      "num": 2370,
      "name": "merge_delete_then_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2370_merge_delete_then_reinsert.sql",
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      "description": "MERGE deletes rows then a follow-up MERGE re-inserts the same ids",
      "status": "pass",
      "duration_ms": 1639,
      "error": null,
      "notes": null,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2371_update_multi_set_chain",
      "num": 2371,
      "name": "update_multi_set_chain",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "UPDATE with multiple SET assignments computed from old values",
      "status": "pass",
      "duration_ms": 1665,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:32:49.772015+00:00",
      "read_cold_ms": 952,
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        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2372_full_refresh_pattern",
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      "name": "full_refresh_pattern",
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      "writer": "delta-forge",
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      "error": null,
      "notes": null,
      "extras": {},
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        "type:integer",
        "type:string",
        "dml:delete",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2373_merge_constant_source",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:52.881400+00:00",
      "read_cold_ms": 921,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2374_overwrite_schema_preserved",
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      "status": "pass",
      "duration_ms": 1322,
      "error": null,
      "notes": null,
      "extras": {},
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2375_merge_then_optimize",
      "num": 2375,
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      "reader": "spark",
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      "description": "MERGE followed by OPTIMIZE -- read still returns same logical state",
      "status": "pass",
      "duration_ms": 1247,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 701,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 232,
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        "type:integer",
        "type:string",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2376_cdf_read_specific_version",
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      "name": "cdf_read_specific_version",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2376_cdf_read_specific_version.sql",
      "read_script": "generator/spark-reads-df/verify_2376_cdf_read_specific_version.py",
      "description": "V0 INSERT 10 / V1 UPDATE 4 / V2 DELETE 3.",
      "status": "pass",
      "duration_ms": 1934,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:57.386646+00:00",
      "read_cold_ms": 945,
      "read_warm_ms": 385,
      "skip_feature": null,
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      "write_cold_ms": 122,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2377_cdf_ending_version_bounded",
      "num": 2377,
      "name": "cdf_ending_version_bounded",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2377_cdf_ending_version_bounded.sql",
      "read_script": "generator/spark-reads-df/verify_2377_cdf_ending_version_bounded.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1852,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:32:59.239576+00:00",
      "read_cold_ms": 963,
      "read_warm_ms": 355,
      "skip_feature": null,
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      "write_cold_ms": 124,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
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        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2378_alter_add_then_drop_column",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2378_alter_add_then_drop_column.sql",
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      "description": "Final schema must be {id, name} like start. Requires column mapping for DROP.",
      "status": "pass",
      "duration_ms": 1609,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 885,
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      "skip_feature": null,
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      "write_cold_ms": 92,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "dml:update",
        "delta:column-mapping",
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        "schema:add-column",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2379_alter_rename_column_twice",
      "num": 2379,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2379_alter_rename_column_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2379_alter_rename_column_twice.py",
      "description": "Final logical column name should be 'omega'.",
      "status": "pass",
      "duration_ms": 1260,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:02.110396+00:00",
      "read_cold_ms": 752,
      "read_warm_ms": 224,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/237_default_null_handling",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/237_default_null_handling.sql",
      "read_script": "generator/spark-reads-df/verify_237_default_null_handling.py",
      "description": "Default vs explicit NULL handling",
      "status": "pass",
      "duration_ms": 1496,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:36.649459+00:00",
      "read_cold_ms": 1084,
      "read_warm_ms": 165,
      "skip_feature": null,
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        "dml:insert",
        "delta:constraints",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2380_evolve_add_struct_column",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2380_evolve_add_struct_column.sql",
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      "description": "Initial schema is flat; new struct column starts as NULL for prior rows.",
      "status": "pass",
      "duration_ms": 1257,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:33:03.368281+00:00",
      "read_cold_ms": 739,
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      "skip_feature": null,
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      "write_cold_ms": 132,
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      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2381_colmap_name_add_column",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2381_colmap_name_add_column.sql",
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      "description": "Column mapping NAME mode + ALTER TABLE ADD COLUMN. New column gets a fresh physical name in delta log metadata.",
      "status": "pass",
      "duration_ms": 1711,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:05.079684+00:00",
      "read_cold_ms": 945,
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      "tags": [
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        "dml:insert",
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        "delta:column-mapping",
        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2382_colmap_id_basic_readback",
      "num": 2382,
      "name": "colmap_id_basic_readback",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2382_colmap_id_basic_readback.sql",
      "read_script": "generator/spark-reads-df/verify_2382_colmap_id_basic_readback.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1218,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:33:06.298425+00:00",
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      "tags": [
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2383_colmap_drop_column",
      "num": 2383,
      "name": "colmap_drop_column",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2383_colmap_drop_column.py",
      "description": "Column mapping NAME mode + ALTER TABLE DROP COLUMN. Verifies the dropped column is no longer visible to readers.",
      "status": "pass",
      "duration_ms": 1241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:07.539617+00:00",
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      "tags": [
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2384_time_travel_version_as_of",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2384_time_travel_version_as_of.sql",
      "read_script": "generator/spark-reads-df/verify_2384_time_travel_version_as_of.py",
      "description": "Verify side reads each version with versionAsOf.",
      "status": "pass",
      "duration_ms": 2160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:09.700450+00:00",
      "read_cold_ms": 711,
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      "skip_feature": null,
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      "tags": [
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2385_time_travel_timestamp_as_of",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1434,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:11.135054+00:00",
      "read_cold_ms": 861,
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      "skip_feature": null,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2386_cdc_partition_delete",
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      "name": "cdc_partition_delete",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2386_cdc_partition_delete.py",
      "description": "CDC enabled + partition column + DELETE operation produces CDF delete events.",
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      "duration_ms": 1407,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:12.543275+00:00",
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      "skip_feature": null,
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      "tags": [
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        "dml:cdc-write",
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        "dml:insert",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2387_check_constraint_after_optimize",
      "num": 2387,
      "name": "check_constraint_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2387_check_constraint_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2387_check_constraint_after_optimize.py",
      "description": "CHECK constraint metadata must survive an OPTIMIZE call.",
      "status": "pass",
      "duration_ms": 1256,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:13.800156+00:00",
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      "skip_feature": null,
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      "tags": [
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2388_multi_check_same_column",
      "num": 2388,
      "name": "multi_check_same_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2388_multi_check_same_column.sql",
      "read_script": "generator/spark-reads-df/verify_2388_multi_check_same_column.py",
      "description": "Two CHECK constraints on the same column (lower bound + upper bound).",
      "status": "pass",
      "duration_ms": 1359,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:15.159813+00:00",
      "read_cold_ms": 810,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 43,
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        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2389_check_constraint_two_columns",
      "num": 2389,
      "name": "check_constraint_two_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2389_check_constraint_two_columns.sql",
      "read_script": "generator/spark-reads-df/verify_2389_check_constraint_two_columns.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1414,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:16.574770+00:00",
      "read_cold_ms": 782,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 65,
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        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/238_default_evolution",
      "num": 238,
      "name": "default_evolution",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/238_default_evolution.sql",
      "read_script": "generator/spark-reads-df/verify_238_default_evolution.py",
      "description": "Schema evolution where new columns with defaults are added",
      "status": "pass",
      "duration_ms": 1861,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:38.511445+00:00",
      "read_cold_ms": 1480,
      "read_warm_ms": 119,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2390_not_null_via_check",
      "num": 2390,
      "name": "not_null_via_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2390_not_null_via_check.sql",
      "read_script": "generator/spark-reads-df/verify_2390_not_null_via_check.py",
      "description": "Enforce non-null via a CHECK (name IS NOT NULL) constraint added post-hoc.",
      "status": "pass",
      "duration_ms": 1338,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:17.913024+00:00",
      "read_cold_ms": 797,
      "read_warm_ms": 238,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2391_cdc_toggle_cycle",
      "num": 2391,
      "name": "cdc_toggle_cycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2391_cdc_toggle_cycle.sql",
      "read_script": "generator/spark-reads-df/verify_2391_cdc_toggle_cycle.py",
      "description": "Final metadata should reflect enableChangeDataFeed = true.",
      "status": "pass",
      "duration_ms": 1292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:19.205426+00:00",
      "read_cold_ms": 744,
      "read_warm_ms": 304,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 159,
      "write_warm_ms": 220,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2392_cdc_merge_update_images",
      "num": 2392,
      "name": "cdc_merge_update_images",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2392_cdc_merge_update_images.sql",
      "read_script": "generator/spark-reads-df/verify_2392_cdc_merge_update_images.py",
      "description": "CDC + MERGE matched UPDATE. Verify update_preimage/update_postimage events in CDF.",
      "status": "pass",
      "duration_ms": 1847,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:21.052839+00:00",
      "read_cold_ms": 922,
      "read_warm_ms": 365,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 112,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2393_evolve_then_time_travel",
      "num": 2393,
      "name": "evolve_then_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2393_evolve_then_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_2393_evolve_then_time_travel.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1867,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:22.920184+00:00",
      "read_cold_ms": 957,
      "read_warm_ms": 371,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2394_colmap_evolve_add_column",
      "num": 2394,
      "name": "colmap_evolve_add_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2394_colmap_evolve_add_column.sql",
      "read_script": "generator/spark-reads-df/verify_2394_colmap_evolve_add_column.py",
      "description": "Column mapping NAME mode + schema evolution (ADD COLUMN) combo.",
      "status": "pass",
      "duration_ms": 1308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:24.228665+00:00",
      "read_cold_ms": 751,
      "read_warm_ms": 293,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2395_cdf_filter_change_type",
      "num": 2395,
      "name": "cdf_filter_change_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2395_cdf_filter_change_type.sql",
      "read_script": "generator/spark-reads-df/verify_2395_cdf_filter_change_type.py",
      "description": "CDC with mixed INSERT/UPDATE/DELETE so Spark side can filter CDF by _change_type.",
      "status": "pass",
      "duration_ms": 2503,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:26.732918+00:00",
      "read_cold_ms": 892,
      "read_warm_ms": 376,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 149,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2396_check_and_default_same_column",
      "num": 2396,
      "name": "check_and_default_same_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2396_check_and_default_same_column.sql",
      "read_script": "generator/spark-reads-df/verify_2396_check_and_default_same_column.py",
      "description": "DEFAULT literal on a column plus a CHECK constraint on the same column.",
      "status": "pass",
      "duration_ms": 1430,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:28.163551+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 311,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 59,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2397_set_then_unset_tblproperty",
      "num": 2397,
      "name": "set_then_unset_tblproperty",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2397_set_then_unset_tblproperty.sql",
      "read_script": "generator/spark-reads-df/verify_2397_set_then_unset_tblproperty.py",
      "description": "SET then UNSET a custom TBLPROPERTY. Latest metadata must not contain the property.",
      "status": "pass",
      "duration_ms": 1251,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:29.415364+00:00",
      "read_cold_ms": 793,
      "read_warm_ms": 225,
      "skip_feature": null,
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      "write_cold_ms": 54,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2398_time_travel_after_vacuum",
      "num": 2398,
      "name": "time_travel_after_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2398_time_travel_after_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_2398_time_travel_after_vacuum.py",
      "description": "VACUUM DRY RUN (non-destructive) then prove recent versions still time-travel readable.",
      "status": "pass",
      "duration_ms": 1808,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:31.223864+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 286,
      "skip_feature": null,
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      "write_cold_ms": 178,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2399_cdf_batch_multi_versions",
      "num": 2399,
      "name": "cdf_batch_multi_versions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2399_cdf_batch_multi_versions.sql",
      "read_script": "generator/spark-reads-df/verify_2399_cdf_batch_multi_versions.py",
      "description": "CDF batch read with startingVersion=1, endingVersion=3 spanning 3 commits.",
      "status": "pass",
      "duration_ms": 1841,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:33.065911+00:00",
      "read_cold_ms": 882,
      "read_warm_ms": 391,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 255,
      "write_warm_ms": 236,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/239_default_mixed",
      "num": 239,
      "name": "default_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/239_default_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_239_default_mixed.py",
      "description": "Table with many different deterministic default value types.",
      "status": "pass",
      "duration_ms": 2079,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:40.591782+00:00",
      "read_cold_ms": 1056,
      "read_warm_ms": 414,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 21,
      "write_warm_ms": 20,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/23_action_checkpoint_metadata_v2",
      "num": 23,
      "name": "action_checkpoint_metadata_v2",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/23_action_checkpoint_metadata_v2.sql",
      "read_script": "generator/spark-reads-df/verify_23_action_checkpoint_metadata_v2.py",
      "description": "Demonstrates checkpoint metadata action (V2) which signals readers to use V2 checkpoint parsing logic.",
      "status": "pass",
      "duration_ms": 4493,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:45.087883+00:00",
      "read_cold_ms": 2383,
      "read_warm_ms": 793,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1280,
      "write_warm_ms": 621,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2400_colmap_partition_column",
      "num": 2400,
      "name": "colmap_partition_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2400_colmap_partition_column.sql",
      "read_script": "generator/spark-reads-df/verify_2400_colmap_partition_column.py",
      "description": "Column mapping NAME mode + PARTITIONED BY combo. Verify partition pruning works.",
      "status": "pass",
      "duration_ms": 1687,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:34.753457+00:00",
      "read_cold_ms": 711,
      "read_warm_ms": 291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2401_create_table_inline_check_constraint",
      "num": 2401,
      "name": "create_table_inline_check_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2401_create_table_inline_check_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2401_create_table_inline_check_constraint.py",
      "description": "Inline CHECK constraint on CREATE TABLE persists to delta.constraints.*",
      "status": "pass",
      "duration_ms": 1220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:35.973661+00:00",
      "read_cold_ms": 730,
      "read_warm_ms": 228,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 88,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2402_blind_merge_identity",
      "num": 2402,
      "name": "blind_merge_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2402_blind_merge_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2402_blind_merge_identity.py",
      "description": "MERGE WHEN NOT MATCHED only (no MATCHED clauses) into IDENTITY table",
      "status": "pass",
      "duration_ms": 1362,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:37.336655+00:00",
      "read_cold_ms": 824,
      "read_warm_ms": 292,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 175,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2403_blind_merge_default",
      "num": 2403,
      "name": "blind_merge_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2403_blind_merge_default.sql",
      "read_script": "generator/spark-reads-df/verify_2403_blind_merge_default.py",
      "description": "MERGE WHEN NOT MATCHED only into DEFAULT table",
      "status": "pass",
      "duration_ms": 1275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:33:38.612312+00:00",
      "read_cold_ms": 748,
      "read_warm_ms": 231,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 84,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2404_blind_merge_generated",
      "num": 2404,
      "name": "blind_merge_generated",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2404_blind_merge_generated.sql",
      "read_script": "generator/spark-reads-df/verify_2404_blind_merge_generated.py",
      "description": "MERGE WHEN NOT MATCHED only into GENERATED table",
      "status": "pass",
      "duration_ms": 1280,
      "error": null,
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      "status": "pass",
      "duration_ms": 2768,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:34:46.544727+00:00",
      "read_cold_ms": 748,
      "read_warm_ms": 308,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1602,
      "write_warm_ms": 1613,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2441_scale_optimize_partitioned_100k",
      "num": 2441,
      "name": "scale_optimize_partitioned_100k",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2441_scale_optimize_partitioned_100k.sql",
      "read_script": "generator/spark-reads-df/verify_2441_scale_optimize_partitioned_100k.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1755,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:34:48.300794+00:00",
      "read_cold_ms": 857,
      "read_warm_ms": 290,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 604,
      "write_warm_ms": 675,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2442_scale_zorder_multi_col_100k",
      "num": 2442,
      "name": "scale_zorder_multi_col_100k",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2442_scale_zorder_multi_col_100k.sql",
      "read_script": "generator/spark-reads-df/verify_2442_scale_zorder_multi_col_100k.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1957,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:34:50.258988+00:00",
      "read_cold_ms": 839,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 278,
      "write_warm_ms": 288,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2443_scale_merge_100k_three_clause",
      "num": 2443,
      "name": "scale_merge_100k_three_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2443_scale_merge_100k_three_clause.sql",
      "read_script": "generator/spark-reads-df/verify_2443_scale_merge_100k_three_clause.py",
      "description": "MATCHED AND id>25000 -> DELETE. NOT MATCHED (ids 100001-125000) -> INSERT. so we need a different source. Let's use 25001-75000: ids 1-100000 exist. Source 25001-75000. MATCHED AND src.id <= 50000 -> UPDATE. MATCHED AND src.id > 50000 -> DELETE.",
      "status": "pass",
      "duration_ms": 2213,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:34:52.473154+00:00",
      "read_cold_ms": 986,
      "read_warm_ms": 382,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 941,
      "write_warm_ms": 1020,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2444_scale_cdc_merge_100k",
      "num": 2444,
      "name": "scale_cdc_merge_100k",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2444_scale_cdc_merge_100k.sql",
      "read_script": "generator/spark-reads-df/verify_2444_scale_cdc_merge_100k.py",
      "description": null,
      "status": "pass",
      "duration_ms": 43402,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:35.875679+00:00",
      "read_cold_ms": 1484,
      "read_warm_ms": 413,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121159,
      "write_warm_ms": 65636,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2445_scale_vacuum_optimize_chain",
      "num": 2445,
      "name": "scale_vacuum_optimize_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2445_scale_vacuum_optimize_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2445_scale_vacuum_optimize_chain.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1775,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:37.651117+00:00",
      "read_cold_ms": 849,
      "read_warm_ms": 403,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 117,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2446_conflict_update_same_rows_twice",
      "num": 2446,
      "name": "conflict_update_same_rows_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2446_conflict_update_same_rows_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2446_conflict_update_same_rows_twice.py",
      "description": "INSERT 1k rows, UPDATE val=100 WHERE id<=500, UPDATE val=200 WHERE id<=700. ids 1-500 get val=200 (second update overwrites first), ids 501-700 get val=200, ids 701-1000 keep original val (i*10).",
      "status": "pass",
      "duration_ms": 1706,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:39.358015+00:00",
      "read_cold_ms": 948,
      "read_warm_ms": 355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 197,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2447_conflict_delete_then_update_overlap",
      "num": 2447,
      "name": "conflict_delete_then_update_overlap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2447_conflict_delete_then_update_overlap.sql",
      "read_script": "generator/spark-reads-df/verify_2447_conflict_delete_then_update_overlap.py",
      "description": "INSERT 1k rows, DELETE WHERE id%3=0, UPDATE val=999 WHERE id%3=1.",
      "status": "pass",
      "duration_ms": 1646,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:41.004662+00:00",
      "read_cold_ms": 895,
      "read_warm_ms": 330,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2448_conflict_merge_overlapping",
      "num": 2448,
      "name": "conflict_merge_overlapping",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2448_conflict_merge_overlapping.sql",
      "read_script": "generator/spark-reads-df/verify_2448_conflict_merge_overlapping.py",
      "description": "INSERT 500 (ids 1-500) + MERGE source (ids 250-750). WHEN MATCHED UPDATE val=0, WHEN NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 1692,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:42.697494+00:00",
      "read_cold_ms": 901,
      "read_warm_ms": 372,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2449_conflict_insert_delete_reinsert",
      "num": 2449,
      "name": "conflict_insert_delete_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2449_conflict_insert_delete_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_2449_conflict_insert_delete_reinsert.py",
      "description": "INSERT 500 (ids 1-500) + DELETE all + INSERT 500 new rows (ids 501-1000).",
      "status": "pass",
      "duration_ms": 1629,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:44.327576+00:00",
      "read_cold_ms": 902,
      "read_warm_ms": 330,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
      "write_warm_ms": 145,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/244_cdc_insert",
      "num": 244,
      "name": "cdc_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/244_cdc_insert.sql",
      "read_script": "generator/spark-reads-df/verify_244_cdc_insert.py",
      "description": "- Change Data Feed (CDF) enabled table for insert tracking - Table property: delta.enableChangeDataFeed = true - Simple order tracking schema with timestamps",
      "status": "pass",
      "duration_ms": 2237,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:57.476682+00:00",
      "read_cold_ms": 1424,
      "read_warm_ms": 340,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 32,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2450_conflict_optimize_between_dml",
      "num": 2450,
      "name": "conflict_optimize_between_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2450_conflict_optimize_between_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2450_conflict_optimize_between_dml.py",
      "description": "INSERT 500 + OPTIMIZE + INSERT 500 (ids 501-1000) + DELETE WHERE id<=250.",
      "status": "pass",
      "duration_ms": 1648,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:45.976015+00:00",
      "read_cold_ms": 888,
      "read_warm_ms": 355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 90,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2451_conflict_vacuum_between_dml",
      "num": 2451,
      "name": "conflict_vacuum_between_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2451_conflict_vacuum_between_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2451_conflict_vacuum_between_dml.py",
      "description": "INSERT 500 + DELETE WHERE id<=250 + VACUUM RETAIN 0 HOURS + INSERT (ids 501-750).",
      "status": "pass",
      "duration_ms": 1595,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:47.571470+00:00",
      "read_cold_ms": 856,
      "read_warm_ms": 361,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 136,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2452_conflict_restore_then_dml",
      "num": 2452,
      "name": "conflict_restore_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2452_conflict_restore_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2452_conflict_restore_then_dml.py",
      "description": "INSERT 500 (v1, val=i*10) + UPDATE val=0 (v2) + RESTORE VERSION 1 + INSERT 200 (ids 501-700).",
      "status": "pass",
      "duration_ms": 1305,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:48.876927+00:00",
      "read_cold_ms": 788,
      "read_warm_ms": 231,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 151,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2453_conflict_merge_self_join",
      "num": 2453,
      "name": "conflict_merge_self_join",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2453_conflict_merge_self_join.sql",
      "read_script": "generator/spark-reads-df/verify_2453_conflict_merge_self_join.py",
      "description": "INSERT 500 rows + MERGE self (source = same table's data) ON id=id WHEN MATCHED UPDATE SET val=val*2. Verify all vals doubled.",
      "status": "pass",
      "duration_ms": 1780,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:50.657981+00:00",
      "read_cold_ms": 1025,
      "read_warm_ms": 352,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2454_conflict_overwrite_then_merge",
      "num": 2454,
      "name": "conflict_overwrite_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2454_conflict_overwrite_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2454_conflict_overwrite_then_merge.py",
      "description": "INSERT 500 + INSERT OVERWRITE with 300 (ids 1-300) + MERGE 200 source (ids 301-500) NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 1269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:51.927819+00:00",
      "read_cold_ms": 714,
      "read_warm_ms": 281,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 225,
      "write_warm_ms": 161,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2455_conflict_schema_evolve_mid_dml",
      "num": 2455,
      "name": "conflict_schema_evolve_mid_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2455_conflict_schema_evolve_mid_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2455_conflict_schema_evolve_mid_dml.py",
      "description": "INSERT 500 (id, val) + ALTER TABLE ADD COLUMN tag STRING + UPDATE SET tag='updated' WHERE id<=250 + INSERT 200 more with tag.",
      "status": "pass",
      "duration_ms": 1672,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:53.600874+00:00",
      "read_cold_ms": 924,
      "read_warm_ms": 336,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 169,
      "write_warm_ms": 128,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2456_conflict_rename_col_then_update",
      "num": 2456,
      "name": "conflict_rename_col_then_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2456_conflict_rename_col_then_update.sql",
      "read_script": "generator/spark-reads-df/verify_2456_conflict_rename_col_then_update.py",
      "description": "INSERT 500 (id, old_name) + ALTER RENAME COLUMN old_name TO new_name + UPDATE SET new_name='changed' WHERE id<=100. Verify column named new_name, 100 rows='changed'.",
      "status": "pass",
      "duration_ms": 1717,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:55.319031+00:00",
      "read_cold_ms": 976,
      "read_warm_ms": 367,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2457_conflict_drop_col_then_insert",
      "num": 2457,
      "name": "conflict_drop_col_then_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2457_conflict_drop_col_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2457_conflict_drop_col_then_insert.py",
      "description": "INSERT 500 (id, val, extra) + ALTER DROP COLUMN extra + INSERT 200 (id, val).",
      "status": "pass",
      "duration_ms": 1274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:56.593839+00:00",
      "read_cold_ms": 770,
      "read_warm_ms": 252,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2458_conflict_add_constraint_existing_data",
      "num": 2458,
      "name": "conflict_add_constraint_existing_data",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2458_conflict_add_constraint_existing_data.sql",
      "read_script": "generator/spark-reads-df/verify_2458_conflict_add_constraint_existing_data.py",
      "description": "INSERT 500 rows (val = i*10, always > 0) + ALTER ADD CONSTRAINT chk CHECK (val > 0). Verify constraint in delta log and all vals > 0.",
      "status": "pass",
      "duration_ms": 1275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:57.869873+00:00",
      "read_cold_ms": 755,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2459_conflict_cdc_rapid_dml",
      "num": 2459,
      "name": "conflict_cdc_rapid_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2459_conflict_cdc_rapid_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2459_conflict_cdc_rapid_dml.py",
      "description": "CDC enabled + INSERT 200 + UPDATE val=val+1 WHERE id<=100 + DELETE WHERE id>180.",
      "status": "pass",
      "duration_ms": 1831,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:35:59.701570+00:00",
      "read_cold_ms": 898,
      "read_warm_ms": 337,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 127,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/245_cdc_delete",
      "num": 245,
      "name": "cdc_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/245_cdc_delete.sql",
      "read_script": "generator/spark-reads-df/verify_245_cdc_delete.py",
      "description": "- Change Data Feed (CDF) enabled table for delete tracking - Table property: delta.enableChangeDataFeed = true - Product catalog schema with boolean is_active flag",
      "status": "pass",
      "duration_ms": 2428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:46:59.906007+00:00",
      "read_cold_ms": 1762,
      "read_warm_ms": 288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 29,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2460_conflict_partition_overwrite",
      "num": 2460,
      "name": "conflict_partition_overwrite",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2460_conflict_partition_overwrite.sql",
      "read_script": "generator/spark-reads-df/verify_2460_conflict_partition_overwrite.py",
      "description": "PARTITIONED BY(region) + INSERT 500 (4 regions) + INSERT OVERWRITE for region='na' with new data. Verify other regions unchanged.",
      "status": "pass",
      "duration_ms": 1309,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:01.011390+00:00",
      "read_cold_ms": 785,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 243,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2461_idempotent_insert_twice",
      "num": 2461,
      "name": "idempotent_insert_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2461_idempotent_insert_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2461_idempotent_insert_twice.py",
      "description": "INSERT 500 (ids 1-500) + INSERT 500 (ids 501-1000). Pure append, no dedup.",
      "status": "pass",
      "duration_ms": 1266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:02.277737+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 258,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 109,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2462_idempotent_merge_twice",
      "num": 2462,
      "name": "idempotent_merge_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2462_idempotent_merge_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2462_idempotent_merge_twice.py",
      "description": "INSERT 500 + MERGE source 300 (ids 1-300 update val=val+100) + MERGE same source again. Second merge should be idempotent since vals already changed.",
      "status": "pass",
      "duration_ms": 1707,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:03.985685+00:00",
      "read_cold_ms": 909,
      "read_warm_ms": 357,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 141,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2463_idempotent_optimize_twice",
      "num": 2463,
      "name": "idempotent_optimize_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2463_idempotent_optimize_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2463_idempotent_optimize_twice.py",
      "description": "INSERT 100 batches of 10 rows each (1000 total) + OPTIMIZE + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1471,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:05.457382+00:00",
      "read_cold_ms": 926,
      "read_warm_ms": 234,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 595,
      "write_warm_ms": 570,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2464_idempotent_vacuum_twice",
      "num": 2464,
      "name": "idempotent_vacuum_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2464_idempotent_vacuum_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2464_idempotent_vacuum_twice.py",
      "description": "INSERT 500 + DELETE 250 + VACUUM RETAIN 0 HOURS + VACUUM RETAIN 0 HOURS.",
      "status": "pass",
      "duration_ms": 1653,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:07.111557+00:00",
      "read_cold_ms": 931,
      "read_warm_ms": 366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 90,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2465_idempotent_zorder_twice",
      "num": 2465,
      "name": "idempotent_zorder_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2465_idempotent_zorder_twice.sql",
      "read_script": "generator/spark-reads-df/verify_2465_idempotent_zorder_twice.py",
      "description": "INSERT 1k rows + ZORDER BY(k) + ZORDER BY(k). Verify 1000 rows intact.",
      "status": "pass",
      "duration_ms": 1305,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:08.417082+00:00",
      "read_cold_ms": 786,
      "read_warm_ms": 246,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 61,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2466_idempotent_restore_current",
      "num": 2466,
      "name": "idempotent_restore_current",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2466_idempotent_restore_current.sql",
      "read_script": "generator/spark-reads-df/verify_2466_idempotent_restore_current.py",
      "description": "INSERT 500 + UPDATE val=0. Version is 2. RESTORE VERSION 2 (restore to current).",
      "status": "pass",
      "duration_ms": 1669,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:10.086732+00:00",
      "read_cold_ms": 907,
      "read_warm_ms": 352,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2467_idempotent_delete_no_match",
      "num": 2467,
      "name": "idempotent_delete_no_match",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2467_idempotent_delete_no_match.sql",
      "read_script": "generator/spark-reads-df/verify_2467_idempotent_delete_no_match.py",
      "description": "INSERT 500 + DELETE WHERE id > 99999 (no rows match). Verify 500 rows unchanged.",
      "status": "pass",
      "duration_ms": 1301,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:11.388685+00:00",
      "read_cold_ms": 771,
      "read_warm_ms": 269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2468_idempotent_update_no_change",
      "num": 2468,
      "name": "idempotent_update_no_change",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2468_idempotent_update_no_change.sql",
      "read_script": "generator/spark-reads-df/verify_2468_idempotent_update_no_change.py",
      "description": "INSERT 500 + UPDATE SET val=val WHERE id > 0 (no actual value change). Verify 500 rows with original vals.",
      "status": "pass",
      "duration_ms": 1677,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:13.066685+00:00",
      "read_cold_ms": 933,
      "read_warm_ms": 331,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 67,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2469_idempotent_truncate_empty",
      "num": 2469,
      "name": "idempotent_truncate_empty",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2469_idempotent_truncate_empty.sql",
      "read_script": "generator/spark-reads-df/verify_2469_idempotent_truncate_empty.py",
      "description": "CREATE table + TRUNCATE (already empty). Verify 0 rows.",
      "status": "pass",
      "duration_ms": 1263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:14.330761+00:00",
      "read_cold_ms": 790,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 6,
      "tags": [
        "type:integer",
        "dml:truncate",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/246_cdc_update",
      "num": 246,
      "name": "cdc_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/246_cdc_update.sql",
      "read_script": "generator/spark-reads-df/verify_246_cdc_update.py",
      "description": "CDC with update tracking enabled",
      "status": "pass",
      "duration_ms": 2347,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:02.254251+00:00",
      "read_cold_ms": 1883,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 69,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2470_duplicate_merge_dedup",
      "num": 2470,
      "name": "duplicate_merge_dedup",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2470_duplicate_merge_dedup.sql",
      "read_script": "generator/spark-reads-df/verify_2470_duplicate_merge_dedup.py",
      "description": "INSERT 1000 rows with duplicate ids (i%500 as id, so 2 per id).",
      "status": "pass",
      "duration_ms": 1697,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:16.028662+00:00",
      "read_cold_ms": 903,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2471_conflict_delete_all_reinsert_different",
      "num": 2471,
      "name": "conflict_delete_all_reinsert_different",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2471_conflict_delete_all_reinsert_different.sql",
      "read_script": "generator/spark-reads-df/verify_2471_conflict_delete_all_reinsert_different.py",
      "description": "INSERT 500 (val=i*10) + DELETE all + INSERT 500 (val=i*20).",
      "status": "pass",
      "duration_ms": 1679,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:17.708697+00:00",
      "read_cold_ms": 927,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2472_conflict_merge_delete_clause",
      "num": 2472,
      "name": "conflict_merge_delete_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2472_conflict_merge_delete_clause.sql",
      "read_script": "generator/spark-reads-df/verify_2472_conflict_merge_delete_clause.py",
      "description": "INSERT 500 + MERGE source 500 (all match) WHEN MATCHED AND src.val%2=0 THEN DELETE WHEN MATCHED THEN UPDATE SET val=999.",
      "status": "pass",
      "duration_ms": 1679,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:19.388107+00:00",
      "read_cold_ms": 905,
      "read_warm_ms": 403,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 94,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2473_conflict_interleaved_insert_update",
      "num": 2473,
      "name": "conflict_interleaved_insert_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2473_conflict_interleaved_insert_update.sql",
      "read_script": "generator/spark-reads-df/verify_2473_conflict_interleaved_insert_update.py",
      "description": "INSERT 200 + UPDATE SET val=0 + INSERT 200 (ids 201-400) + UPDATE SET val=1 WHERE id>200.",
      "status": "pass",
      "duration_ms": 1680,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:21.069251+00:00",
      "read_cold_ms": 923,
      "read_warm_ms": 368,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 166,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2474_conflict_triple_merge",
      "num": 2474,
      "name": "conflict_triple_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2474_conflict_triple_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2474_conflict_triple_merge.py",
      "description": "INSERT 300 + MERGE 200 (ids 1-200 update val=10) + MERGE 200 (ids 100-300 update val=20) + MERGE 200 (ids 200-400 insert new for 301-400). ids 1-99: val=10, ids 100-200: val=20, ids 201-300: val=20, ids 301-400: val from source.",
      "status": "pass",
      "duration_ms": 1657,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:22.726793+00:00",
      "read_cold_ms": 916,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 184,
      "write_warm_ms": 209,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2475_conflict_optimize_delete_optimize",
      "num": 2475,
      "name": "conflict_optimize_delete_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2475_conflict_optimize_delete_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2475_conflict_optimize_delete_optimize.py",
      "description": "INSERT 1k in 10 batches + OPTIMIZE + DELETE WHERE id%5=0 + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1779,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:24.506413+00:00",
      "read_cold_ms": 1021,
      "read_warm_ms": 377,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 565,
      "write_warm_ms": 640,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2476_conflict_zorder_then_delete",
      "num": 2476,
      "name": "conflict_zorder_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2476_conflict_zorder_then_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2476_conflict_zorder_then_delete.py",
      "description": "INSERT 1k + ZORDER BY(k) + DELETE WHERE k=0. Verify rows without k=0.",
      "status": "pass",
      "duration_ms": 1816,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:26.323039+00:00",
      "read_cold_ms": 1038,
      "read_warm_ms": 366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 73,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2477_conflict_cdc_schema_evolve",
      "num": 2477,
      "name": "conflict_cdc_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2477_conflict_cdc_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2477_conflict_cdc_schema_evolve.py",
      "description": "CDC enabled + INSERT 300 + ALTER ADD COLUMN tag STRING + INSERT 200 with tag.",
      "status": "pass",
      "duration_ms": 1440,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:27.764010+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2478_conflict_identity_after_delete",
      "num": 2478,
      "name": "conflict_identity_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2478_conflict_identity_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2478_conflict_identity_after_delete.py",
      "description": "IDENTITY column + INSERT 500 (omit id) + DELETE WHERE id<=250 + INSERT 250 (omit id).",
      "status": "pass",
      "duration_ms": 1714,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:29.478943+00:00",
      "read_cold_ms": 950,
      "read_warm_ms": 366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 135,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2479_conflict_default_value_change",
      "num": 2479,
      "name": "conflict_default_value_change",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2479_conflict_default_value_change.sql",
      "read_script": "generator/spark-reads-df/verify_2479_conflict_default_value_change.py",
      "description": "INSERT 300 with explicit vals + INSERT 200 omitting val (gets default 0).",
      "status": "pass",
      "duration_ms": 1263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:30.742840+00:00",
      "read_cold_ms": 726,
      "read_warm_ms": 245,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/247_cdc_version_range",
      "num": 247,
      "name": "cdc_version_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/247_cdc_version_range.sql",
      "read_script": "generator/spark-reads-df/verify_247_cdc_version_range.py",
      "description": "Multiple operations across versions with CDC enabled",
      "status": "pass",
      "duration_ms": 4584,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:06.839574+00:00",
      "read_cold_ms": 2836,
      "read_warm_ms": 873,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 144,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2480_conflict_generated_col_source_update",
      "num": 2480,
      "name": "conflict_generated_col_source_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2480_conflict_generated_col_source_update.sql",
      "read_script": "generator/spark-reads-df/verify_2480_conflict_generated_col_source_update.py",
      "description": "(id BIGINT, base INT, computed BIGINT GENERATED ALWAYS AS (base * 2)) INSERT 500 + UPDATE SET base=base+100 WHERE id<=250. Verify computed=base*2 for all rows.",
      "status": "pass",
      "duration_ms": 1699,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:32.442704+00:00",
      "read_cold_ms": 905,
      "read_warm_ms": 371,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 125,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2481_unicode_cjk_basic",
      "num": 2481,
      "name": "unicode_cjk_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2481_unicode_cjk_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2481_unicode_cjk_basic.py",
      "description": "500 rows with CJK city names cycling through 5 values. Verifies UTF-8 CJK strings roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1346,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:33.789815+00:00",
      "read_cold_ms": 778,
      "read_warm_ms": 240,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2482_unicode_emoji_strings",
      "num": 2482,
      "name": "unicode_emoji_strings",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2482_unicode_emoji_strings.sql",
      "read_script": "generator/spark-reads-df/verify_2482_unicode_emoji_strings.py",
      "description": "500 rows with accented European loanwords as status values. Verifies accented character roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1284,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:35.074797+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 30,
      "write_warm_ms": 42,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2483_unicode_accented_european",
      "num": 2483,
      "name": "unicode_accented_european",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2483_unicode_accented_european.sql",
      "read_script": "generator/spark-reads-df/verify_2483_unicode_accented_european.py",
      "description": "500 rows with European accented names cycling through 6 values. Verifies accented character roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1294,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:36.369715+00:00",
      "read_cold_ms": 737,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2484_unicode_mixed_scripts",
      "num": 2484,
      "name": "unicode_mixed_scripts",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2484_unicode_mixed_scripts.sql",
      "read_script": "generator/spark-reads-df/verify_2484_unicode_mixed_scripts.py",
      "description": "500 rows mixing Latin + numeric + punctuation patterns. Verifies mixed-script string assembly roundtrips.",
      "status": "pass",
      "duration_ms": 1392,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:37.762141+00:00",
      "read_cold_ms": 725,
      "read_warm_ms": 366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 62,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2485_unicode_cyrillic_basic",
      "num": 2485,
      "name": "unicode_cyrillic_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2485_unicode_cyrillic_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2485_unicode_cyrillic_basic.py",
      "description": "500 rows with Cyrillic-transliterated city names cycling through 5 values. Verifies string roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:39.070413+00:00",
      "read_cold_ms": 768,
      "read_warm_ms": 247,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 37,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2486_unicode_long_multibyte",
      "num": 2486,
      "name": "unicode_long_multibyte",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2486_unicode_long_multibyte.sql",
      "read_script": "generator/spark-reads-df/verify_2486_unicode_long_multibyte.py",
      "description": "500 rows with long strings built from REPEAT of multi-char patterns. Each string is 300 characters long.",
      "status": "pass",
      "duration_ms": 1273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:40.344195+00:00",
      "read_cold_ms": 738,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 34,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2487_unicode_partition_keys",
      "num": 2487,
      "name": "unicode_partition_keys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2487_unicode_partition_keys.sql",
      "read_script": "generator/spark-reads-df/verify_2487_unicode_partition_keys.py",
      "description": "500 rows partitioned by region with 5 partition values. Verifies partition directories and data integrity.",
      "status": "pass",
      "duration_ms": 1234,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:41.578591+00:00",
      "read_cold_ms": 704,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 175,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2488_unicode_in_map_keys",
      "num": 2488,
      "name": "unicode_in_map_keys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2488_unicode_in_map_keys.sql",
      "read_script": "generator/spark-reads-df/verify_2488_unicode_in_map_keys.py",
      "description": "500 rows with MAP<STRING, INT> column containing string keys. Verifies map keys and values roundtrip.",
      "status": "pass",
      "duration_ms": 1311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:42.890041+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2489_unicode_in_struct_fields",
      "num": 2489,
      "name": "unicode_in_struct_fields",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2489_unicode_in_struct_fields.sql",
      "read_script": "generator/spark-reads-df/verify_2489_unicode_in_struct_fields.py",
      "description": "500 rows with STRUCT<first_name:STRING, last_name:STRING> column. Verifies struct string fields roundtrip.",
      "status": "pass",
      "duration_ms": 11902,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:32:56.376802+00:00",
      "read_cold_ms": 10388,
      "read_warm_ms": 585,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 103,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/248_cdc_timestamp_range",
      "num": 248,
      "name": "cdc_timestamp_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/248_cdc_timestamp_range.sql",
      "read_script": "generator/spark-reads-df/verify_248_cdc_timestamp_range.py",
      "description": "CDC queries between timestamps with distinct commit times",
      "status": "pass",
      "duration_ms": 2147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:08.987451+00:00",
      "read_cold_ms": 1654,
      "read_warm_ms": 241,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 57,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2490_unicode_after_update",
      "num": 2490,
      "name": "unicode_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2490_unicode_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2490_unicode_after_update.py",
      "description": "INSERT 500 rows with name='original_N', then UPDATE first 250 to 'updated_N'. Verifies 250 updated + 250 original strings.",
      "status": "pass",
      "duration_ms": 1718,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:45.877560+00:00",
      "read_cold_ms": 938,
      "read_warm_ms": 355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 238,
      "write_warm_ms": 195,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2491_unicode_after_optimize",
      "num": 2491,
      "name": "unicode_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2491_unicode_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2491_unicode_after_optimize.py",
      "description": "INSERT 500 rows with varied strings in 5 batches, then OPTIMIZE. Verifies all strings preserved after compaction.",
      "status": "pass",
      "duration_ms": 1909,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:47.787558+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 280,
      "write_warm_ms": 220,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2492_unicode_cdc_roundtrip",
      "num": 2492,
      "name": "unicode_cdc_roundtrip",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2492_unicode_cdc_roundtrip.sql",
      "read_script": "generator/spark-reads-df/verify_2492_unicode_cdc_roundtrip.py",
      "description": "CDC enabled. INSERT 300 rows with strings, then UPDATE 100 strings. Verifies CDF has update images with correct strings.",
      "status": "pass",
      "duration_ms": 2080,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:49.868408+00:00",
      "read_cold_ms": 1044,
      "read_warm_ms": 386,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 88,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2493_unicode_merge_source",
      "num": 2493,
      "name": "unicode_merge_source",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2493_unicode_merge_source.sql",
      "read_script": "generator/spark-reads-df/verify_2493_unicode_merge_source.py",
      "description": "INSERT 300 target rows, MERGE 200 source rows (ids 201-400). WHEN MATCHED (ids 201-300): UPDATE name. WHEN NOT MATCHED (ids 301-400): INSERT.",
      "status": "pass",
      "duration_ms": 1705,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:51.573973+00:00",
      "read_cold_ms": 892,
      "read_warm_ms": 392,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2494_unicode_delete_by_string",
      "num": 2494,
      "name": "unicode_delete_by_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2494_unicode_delete_by_string.sql",
      "read_script": "generator/spark-reads-df/verify_2494_unicode_delete_by_string.py",
      "description": "INSERT 500 rows with 5 categories, DELETE WHERE category='Books'. Verifies no 'Books' rows remain (400 rows final).",
      "status": "pass",
      "duration_ms": 1626,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:53.200985+00:00",
      "read_cold_ms": 897,
      "read_warm_ms": 343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2495_unicode_colmap_strings",
      "num": 2495,
      "name": "unicode_colmap_strings",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2495_unicode_colmap_strings.sql",
      "read_script": "generator/spark-reads-df/verify_2495_unicode_colmap_strings.py",
      "description": "Column mapping mode=name with string columns. INSERT 500 rows. Verifies logical names readable.",
      "status": "pass",
      "duration_ms": 1424,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:54.625997+00:00",
      "read_cold_ms": 799,
      "read_warm_ms": 241,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2496_special_char_backslash",
      "num": 2496,
      "name": "special_char_backslash",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2496_special_char_backslash.sql",
      "read_script": "generator/spark-reads-df/verify_2496_special_char_backslash.py",
      "description": "500 rows with backslash patterns in val column. Verifies backslash characters roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:55.898590+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 234,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2497_special_char_single_quotes",
      "num": 2497,
      "name": "special_char_single_quotes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2497_special_char_single_quotes.sql",
      "read_script": "generator/spark-reads-df/verify_2497_special_char_single_quotes.py",
      "description": "500 rows with escaped single quotes in strings. Verifies single-quote characters roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1285,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:57.184340+00:00",
      "read_cold_ms": 778,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2498_special_char_tab_newline",
      "num": 2498,
      "name": "special_char_tab_newline",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2498_special_char_tab_newline.sql",
      "read_script": "generator/spark-reads-df/verify_2498_special_char_tab_newline.py",
      "description": "500 rows with tab/newline marker strings (avoiding CHR()). Uses descriptive text markers instead of actual control chars.",
      "status": "pass",
      "duration_ms": 1291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:58.475854+00:00",
      "read_cold_ms": 723,
      "read_warm_ms": 273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2499_special_char_pipe_delimiter",
      "num": 2499,
      "name": "special_char_pipe_delimiter",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2499_special_char_pipe_delimiter.sql",
      "read_script": "generator/spark-reads-df/verify_2499_special_char_pipe_delimiter.py",
      "description": "500 rows with pipe characters in strings. Verifies pipe delimiter chars roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:36:59.790115+00:00",
      "read_cold_ms": 781,
      "read_warm_ms": 223,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/249_cdc_partition",
      "num": 249,
      "name": "cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/249_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_249_cdc_partition.py",
      "description": "CDC queries with partition filtering and partition pruning",
      "status": "pass",
      "duration_ms": 2394,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:11.382018+00:00",
      "read_cold_ms": 1748,
      "read_warm_ms": 288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 59,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/24_reconciliation_add_remove_sequence",
      "num": 24,
      "name": "reconciliation_add_remove_sequence",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/24_reconciliation_add_remove_sequence.sql",
      "read_script": "generator/spark-reads-df/verify_24_reconciliation_add_remove_sequence.py",
      "description": "Demonstrates action reconciliation with concurrent updates (add/remove sequences).",
      "status": "pass",
      "duration_ms": 5019,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:16.401988+00:00",
      "read_cold_ms": 2847,
      "read_warm_ms": 1073,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 530,
      "write_warm_ms": 483,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2500_special_char_percent_underscore",
      "num": 2500,
      "name": "special_char_percent_underscore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2500_special_char_percent_underscore.sql",
      "read_script": "generator/spark-reads-df/verify_2500_special_char_percent_underscore.py",
      "description": "500 rows with SQL wildcard characters (% and _) in strings. Verifies these special chars roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 1302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:01.093140+00:00",
      "read_cold_ms": 762,
      "read_warm_ms": 239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 91,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2501_unicode_empty_string_vs_null",
      "num": 2501,
      "name": "unicode_empty_string_vs_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2501_unicode_empty_string_vs_null.sql",
      "read_script": "generator/spark-reads-df/verify_2501_unicode_empty_string_vs_null.py",
      "description": "500 rows. Half with val='' (empty string), half with val=NULL. Verifies null_count=250, empty string count=250.",
      "status": "pass",
      "duration_ms": 1240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:02.334117+00:00",
      "read_cold_ms": 710,
      "read_warm_ms": 245,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 59,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2502_unicode_whitespace_only",
      "num": 2502,
      "name": "unicode_whitespace_only",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2502_unicode_whitespace_only.sql",
      "read_script": "generator/spark-reads-df/verify_2502_unicode_whitespace_only.py",
      "description": "500 rows with whitespace-only strings of varying lengths. Verifies whitespace preserved, distinct_count=4.",
      "status": "pass",
      "duration_ms": 1304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:03.639130+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2503_unicode_very_long_string",
      "num": 2503,
      "name": "unicode_very_long_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2503_unicode_very_long_string.sql",
      "read_script": "generator/spark-reads-df/verify_2503_unicode_very_long_string.py",
      "description": "500 rows with val = REPEAT('x', 1000). Each string is 1000 chars. Verifies long strings roundtrip.",
      "status": "pass",
      "duration_ms": 1313,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:04.952599+00:00",
      "read_cold_ms": 788,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2504_unicode_concat_chain",
      "num": 2504,
      "name": "unicode_concat_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2504_unicode_concat_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2504_unicode_concat_chain.py",
      "description": "500 rows with val built from chained CONCATs. Verifies assembled string correctness.",
      "status": "pass",
      "duration_ms": 1275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:06.227973+00:00",
      "read_cold_ms": 743,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2505_unicode_case_sensitivity",
      "num": 2505,
      "name": "unicode_case_sensitivity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2505_unicode_case_sensitivity.sql",
      "read_script": "generator/spark-reads-df/verify_2505_unicode_case_sensitivity.py",
      "description": "500 rows with val alternating between 5 case variants. Verifies distinct_count=5 and case preserved.",
      "status": "pass",
      "duration_ms": 1293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:07.522201+00:00",
      "read_cold_ms": 774,
      "read_warm_ms": 240,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2506_unicode_numeric_strings",
      "num": 2506,
      "name": "unicode_numeric_strings",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2506_unicode_numeric_strings.sql",
      "read_script": "generator/spark-reads-df/verify_2506_unicode_numeric_strings.py",
      "description": "500 rows with val = CAST(i AS STRING). Pure numeric strings. Verifies they stay as strings, not numbers.",
      "status": "pass",
      "duration_ms": 1275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:08.797505+00:00",
      "read_cold_ms": 746,
      "read_warm_ms": 233,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 78,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2507_unicode_leading_trailing_spaces",
      "num": 2507,
      "name": "unicode_leading_trailing_spaces",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2507_unicode_leading_trailing_spaces.sql",
      "read_script": "generator/spark-reads-df/verify_2507_unicode_leading_trailing_spaces.py",
      "description": "500 rows with val = ' N ' (leading+trailing spaces). Verifies spaces are preserved, not trimmed.",
      "status": "pass",
      "duration_ms": 1292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:10.089942+00:00",
      "read_cold_ms": 732,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 28,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2508_unicode_null_string_operations",
      "num": 2508,
      "name": "unicode_null_string_operations",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2508_unicode_null_string_operations.sql",
      "read_script": "generator/spark-reads-df/verify_2508_unicode_null_string_operations.py",
      "description": "500 rows. Every 5th row has val=NULL, rest have CONCAT result. Verifies null_count=100.",
      "status": "pass",
      "duration_ms": 1332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:11.423044+00:00",
      "read_cold_ms": 779,
      "read_warm_ms": 287,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2509_unicode_string_in_partition_prune",
      "num": 2509,
      "name": "unicode_string_in_partition_prune",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2509_unicode_string_in_partition_prune.sql",
      "read_script": "generator/spark-reads-df/verify_2509_unicode_string_in_partition_prune.py",
      "description": "PARTITIONED BY(category). INSERT 500 rows with 5 categories. Filter by category='Books' should partition-prune.",
      "status": "pass",
      "duration_ms": 1611,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:13.034934+00:00",
      "read_cold_ms": 779,
      "read_warm_ms": 220,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/250_cdc_merge",
      "num": 250,
      "name": "cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/250_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_250_cdc_merge.py",
      "description": "MERGE operations create correct CDC records",
      "status": "pass",
      "duration_ms": 2832,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:19.234890+00:00",
      "read_cold_ms": 1826,
      "read_warm_ms": 459,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2510_unicode_string_min_max_stats",
      "num": 2510,
      "name": "unicode_string_min_max_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2510_unicode_string_min_max_stats.sql",
      "read_script": "generator/spark-reads-df/verify_2510_unicode_string_min_max_stats.py",
      "description": "INSERT 500 rows with val from 'aaa_001' to 'aaa_500'. Verifies min/max lexicographic correctness.",
      "status": "pass",
      "duration_ms": 1297,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:14.332507+00:00",
      "read_cold_ms": 742,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2511_unicode_string_distinct_count",
      "num": 2511,
      "name": "unicode_string_distinct_count",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2511_unicode_string_distinct_count.sql",
      "read_script": "generator/spark-reads-df/verify_2511_unicode_string_distinct_count.py",
      "description": "INSERT 500 rows with 10 distinct categories (i%10). Verifies distinct_count=10.",
      "status": "pass",
      "duration_ms": 1267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:15.600443+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2512_unicode_string_group_distribution",
      "num": 2512,
      "name": "unicode_string_group_distribution",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2512_unicode_string_group_distribution.sql",
      "read_script": "generator/spark-reads-df/verify_2512_unicode_string_group_distribution.py",
      "description": "INSERT 500 rows with 5 groups of 100 each. Verifies count per group = 100.",
      "status": "pass",
      "duration_ms": 1262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:16.863266+00:00",
      "read_cold_ms": 769,
      "read_warm_ms": 230,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2513_unicode_update_string_conditional",
      "num": 2513,
      "name": "unicode_update_string_conditional",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2513_unicode_update_string_conditional.sql",
      "read_script": "generator/spark-reads-df/verify_2513_unicode_update_string_conditional.py",
      "description": "INSERT 500 rows, then two UPDATE statements: UPDATE SET status='active' WHERE id > 250 UPDATE SET status='inactive' WHERE id <= 250 Verifies 250 active + 250 inactive.",
      "status": "pass",
      "duration_ms": 1831,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:18.695141+00:00",
      "read_cold_ms": 1021,
      "read_warm_ms": 361,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 130,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2514_unicode_merge_string_key",
      "num": 2514,
      "name": "unicode_merge_string_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2514_unicode_merge_string_key.sql",
      "read_script": "generator/spark-reads-df/verify_2514_unicode_merge_string_key.py",
      "description": "INSERT 300 rows (name as key). MERGE 200 source matching on name. Matched rows (first 100) get val updated. Unmatched (ids 301-400) inserted.",
      "status": "pass",
      "duration_ms": 1733,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:20.428812+00:00",
      "read_cold_ms": 964,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 109,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2515_unicode_constraint_on_string",
      "num": 2515,
      "name": "unicode_constraint_on_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2515_unicode_constraint_on_string.sql",
      "read_script": "generator/spark-reads-df/verify_2515_unicode_constraint_on_string.py",
      "description": "INSERT 500 rows, then ADD CONSTRAINT CHECK (LENGTH(name) > 0). Verifies constraint in log and all names non-empty.",
      "status": "pass",
      "duration_ms": 1544,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:21.973909+00:00",
      "read_cold_ms": 776,
      "read_warm_ms": 239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2516_v2_checkpoint_basic_insert",
      "num": 2516,
      "name": "v2_checkpoint_basic_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2516_v2_checkpoint_basic_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2516_v2_checkpoint_basic_insert.py",
      "description": "V2 checkpoint policy with checkpointInterval=5.",
      "status": "pass",
      "duration_ms": 1820,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:23.794131+00:00",
      "read_cold_ms": 1234,
      "read_warm_ms": 235,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 384,
      "write_warm_ms": 308,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2517_v2_checkpoint_after_merge",
      "num": 2517,
      "name": "v2_checkpoint_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2517_v2_checkpoint_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2517_v2_checkpoint_after_merge.py",
      "description": "V2 checkpoint after INSERT + MERGE + additional INSERTs to trigger checkpoint.",
      "status": "pass",
      "duration_ms": 1884,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:25.679080+00:00",
      "read_cold_ms": 1036,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 253,
      "write_warm_ms": 258,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2518_v2_checkpoint_after_delete",
      "num": 2518,
      "name": "v2_checkpoint_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2518_v2_checkpoint_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2518_v2_checkpoint_after_delete.py",
      "description": "V2 checkpoint after INSERT + DELETE + enough ops to trigger checkpoint.",
      "status": "pass",
      "duration_ms": 2060,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:27.739810+00:00",
      "read_cold_ms": 1238,
      "read_warm_ms": 398,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 201,
      "write_warm_ms": 290,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2519_v2_checkpoint_with_cdc",
      "num": 2519,
      "name": "v2_checkpoint_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2519_v2_checkpoint_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2519_v2_checkpoint_with_cdc.py",
      "description": "V2 checkpoint + CDC. INSERT 500, UPDATE 200, trigger checkpoint.",
      "status": "pass",
      "duration_ms": 2068,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:29.808456+00:00",
      "read_cold_ms": 933,
      "read_warm_ms": 395,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 247,
      "write_warm_ms": 165,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/251_cdc_retention",
      "num": 251,
      "name": "cdc_retention",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/251_cdc_retention.sql",
      "read_script": "generator/spark-reads-df/verify_251_cdc_retention.py",
      "description": "CDC file retention and cleanup behavior",
      "status": "pass",
      "duration_ms": 2419,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:21.654959+00:00",
      "read_cold_ms": 1484,
      "read_warm_ms": 342,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 386,
      "write_warm_ms": 349,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2520_v2_checkpoint_with_colmap",
      "num": 2520,
      "name": "v2_checkpoint_with_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2520_v2_checkpoint_with_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2520_v2_checkpoint_with_colmap.py",
      "description": "V2 checkpoint + column mapping (name mode). INSERT 500 + trigger checkpoint.",
      "status": "pass",
      "duration_ms": 1360,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:31.169173+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 282,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 476,
      "write_warm_ms": 321,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2521_v2_checkpoint_with_dv",
      "num": 2521,
      "name": "v2_checkpoint_with_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2521_v2_checkpoint_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_2521_v2_checkpoint_with_dv.py",
      "description": "V2 checkpoint + deletion vectors. INSERT 500, DELETE 200 (via DV), trigger checkpoint.",
      "status": "pass",
      "duration_ms": 1795,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:32.964517+00:00",
      "read_cold_ms": 989,
      "read_warm_ms": 376,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 295,
      "write_warm_ms": 417,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2522_v2_checkpoint_with_constraints",
      "num": 2522,
      "name": "v2_checkpoint_with_constraints",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2522_v2_checkpoint_with_constraints.sql",
      "read_script": "generator/spark-reads-df/verify_2522_v2_checkpoint_with_constraints.py",
      "description": "V2 checkpoint + CHECK constraint. INSERT 500, ADD CONSTRAINT, trigger checkpoint.",
      "status": "pass",
      "duration_ms": 1313,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:34.278339+00:00",
      "read_cold_ms": 788,
      "read_warm_ms": 239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 275,
      "write_warm_ms": 241,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2523_v2_checkpoint_with_identity",
      "num": 2523,
      "name": "v2_checkpoint_with_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2523_v2_checkpoint_with_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2523_v2_checkpoint_with_identity.py",
      "description": "V2 checkpoint + IDENTITY column. INSERT 500 omitting id, trigger checkpoint.",
      "status": "pass",
      "duration_ms": 1280,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:35.558759+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 496,
      "write_warm_ms": 475,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2524_v2_checkpoint_schema_evolve",
      "num": 2524,
      "name": "v2_checkpoint_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2524_v2_checkpoint_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2524_v2_checkpoint_schema_evolve.py",
      "description": "V2 checkpoint + schema evolution. INSERT 300, ALTER ADD COLUMN, INSERT 200, trigger checkpoint.",
      "status": "pass",
      "duration_ms": 1342,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:37:36.901206+00:00",
      "read_cold_ms": 794,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 220,
      "write_warm_ms": 162,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
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    {
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    {
      "id": "df-writes/delta/2560_widen_with_default",
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        "dml:insert",
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        "delta:default-values",
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        "scale:wide-schema",
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    },
    {
      "id": "df-writes/delta/2561_widen_then_zorder",
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    {
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    {
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        "type:integer",
        "dml:delete",
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    },
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      "read_cold_ms": 721,
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    },
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      "error": null,
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      "read_cold_ms": 725,
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    },
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    },
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2576_truncate_with_generated.py",
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      "error": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2577_truncate_multiple_cycles",
      "num": 2577,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2577_truncate_multiple_cycles.sql",
      "read_script": "generator/spark-reads-df/verify_2577_truncate_multiple_cycles.py",
      "description": "Multiple TRUNCATE cycles.",
      "status": "pass",
      "duration_ms": 1278,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:38:53.785007+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 238,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 406,
      "write_warm_ms": 292,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2578_cor_basic",
      "num": 2578,
      "name": "cor_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2578_cor_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2578_cor_basic.py",
      "description": "CREATE + INSERT + CREATE OR REPLACE same schema + INSERT.",
      "status": "pass",
      "duration_ms": 1245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:38:55.031399+00:00",
      "read_cold_ms": 712,
      "read_warm_ms": 229,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 198,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2579_cor_different_schema",
      "num": 2579,
      "name": "cor_different_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2579_cor_different_schema.sql",
      "read_script": "generator/spark-reads-df/verify_2579_cor_different_schema.py",
      "description": "CREATE (id, val) + INSERT + COR (id, name, score) + INSERT.",
      "status": "pass",
      "duration_ms": 1361,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:38:56.393701+00:00",
      "read_cold_ms": 850,
      "read_warm_ms": 245,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 238,
      "write_warm_ms": 167,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/257_ict_multi_writer",
      "num": 257,
      "name": "ict_multi_writer",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/257_ict_multi_writer.sql",
      "read_script": "generator/spark-reads-df/verify_257_ict_multi_writer.py",
      "description": "## Table Details Tests ICT ordering across concurrent writers (DBX and DeltaForge). DBX creates initial commit, DeltaForge will add next, then DBX again.",
      "status": "pass",
      "duration_ms": 2395,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:38.262949+00:00",
      "read_cold_ms": 1610,
      "read_warm_ms": 397,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 16,
      "write_warm_ms": 15,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2580_cor_with_cdc",
      "num": 2580,
      "name": "cor_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2580_cor_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2580_cor_with_cdc.py",
      "description": "CDC + CREATE + INSERT + COR with CDC + INSERT.",
      "status": "pass",
      "duration_ms": 1517,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:38:57.911772+00:00",
      "read_cold_ms": 764,
      "read_warm_ms": 275,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2581_cor_with_constraints",
      "num": 2581,
      "name": "cor_with_constraints",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2581_cor_with_constraints.sql",
      "read_script": "generator/spark-reads-df/verify_2581_cor_with_constraints.py",
      "description": "CREATE + INSERT + COR + ADD CHECK constraint + INSERT.",
      "status": "pass",
      "duration_ms": 1307,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:38:59.219242+00:00",
      "read_cold_ms": 793,
      "read_warm_ms": 235,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2582_cor_with_identity",
      "num": 2582,
      "name": "cor_with_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2582_cor_with_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2582_cor_with_identity.py",
      "description": "CREATE + INSERT + COR with IDENTITY + INSERT.",
      "status": "pass",
      "duration_ms": 1252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:00.471746+00:00",
      "read_cold_ms": 738,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2583_cor_partitioned",
      "num": 2583,
      "name": "cor_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2583_cor_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_2583_cor_partitioned.py",
      "description": "Partitioned CREATE + INSERT + COR same partition + INSERT.",
      "status": "pass",
      "duration_ms": 1277,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:01.749805+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 122,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2584_cor_then_dml",
      "num": 2584,
      "name": "cor_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2584_cor_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2584_cor_then_dml.py",
      "description": "COR + INSERT + UPDATE + DELETE + MERGE. Full DML chain after COR.",
      "status": "pass",
      "duration_ms": 1690,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:03.440779+00:00",
      "read_cold_ms": 930,
      "read_warm_ms": 358,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 217,
      "write_warm_ms": 265,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2585_cor_then_optimize",
      "num": 2585,
      "name": "cor_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2585_cor_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2585_cor_then_optimize.py",
      "description": "COR + INSERT 1000 in 10 batches + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:04.828924+00:00",
      "read_cold_ms": 853,
      "read_warm_ms": 263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 570,
      "write_warm_ms": 754,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2586_boundary_bigint_min_max",
      "num": 2586,
      "name": "boundary_bigint_min_max",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2586_boundary_bigint_min_max.sql",
      "read_script": "generator/spark-reads-df/verify_2586_boundary_bigint_min_max.py",
      "description": "Avoids exact BIGINT MIN to prevent overflow in arithmetic.",
      "status": "pass",
      "duration_ms": 1296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:06.125404+00:00",
      "read_cold_ms": 783,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 46,
      "tags": [
        "type:boundary",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2587_boundary_int_extremes",
      "num": 2587,
      "name": "boundary_int_extremes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2587_boundary_int_extremes.sql",
      "read_script": "generator/spark-reads-df/verify_2587_boundary_int_extremes.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:07.389843+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 133,
      "tags": [
        "type:boundary",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2588_boundary_decimal_38_0",
      "num": 2588,
      "name": "boundary_decimal_38_0",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2588_boundary_decimal_38_0.sql",
      "read_script": "generator/spark-reads-df/verify_2588_boundary_decimal_38_0.py",
      "description": "DECIMAL(38,0) with large values. 10 rows with varying magnitudes.",
      "status": "pass",
      "duration_ms": 1273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:08.663787+00:00",
      "read_cold_ms": 768,
      "read_warm_ms": 254,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 121,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2589_boundary_decimal_38_18",
      "num": 2589,
      "name": "boundary_decimal_38_18",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2589_boundary_decimal_38_18.sql",
      "read_script": "generator/spark-reads-df/verify_2589_boundary_decimal_38_18.py",
      "description": "DECIMAL(38,18) with max precision. 100 rows with 18 decimal places.",
      "status": "pass",
      "duration_ms": 1265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:09.929946+00:00",
      "read_cold_ms": 725,
      "read_warm_ms": 235,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 71,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/258_ict_preserve",
      "num": 258,
      "name": "ict_preserve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/258_ict_preserve.sql",
      "read_script": "generator/spark-reads-df/verify_258_ict_preserve.py",
      "description": "ICT metadata preservation across operations",
      "status": "pass",
      "duration_ms": 2311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:40.577310+00:00",
      "read_cold_ms": 1462,
      "read_warm_ms": 374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 16,
      "write_warm_ms": 14,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2590_boundary_timestamp_epoch",
      "num": 2590,
      "name": "boundary_timestamp_epoch",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2590_boundary_timestamp_epoch.sql",
      "read_script": "generator/spark-reads-df/verify_2590_boundary_timestamp_epoch.py",
      "description": "Timestamps at epoch (1970-01-01 00:00:00 UTC). 10 rows near epoch.",
      "status": "pass",
      "duration_ms": 1291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:11.221216+00:00",
      "read_cold_ms": 739,
      "read_warm_ms": 266,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 45,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2591_boundary_timestamp_far_future",
      "num": 2591,
      "name": "boundary_timestamp_far_future",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2591_boundary_timestamp_far_future.sql",
      "read_script": "generator/spark-reads-df/verify_2591_boundary_timestamp_far_future.py",
      "description": "Timestamps at year 2099. arrow_cast(4070908800000000) = 2099-01-01 00:00 UTC. 10 rows with offsets of i hours from that base.",
      "status": "pass",
      "duration_ms": 1246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:12.467669+00:00",
      "read_cold_ms": 756,
      "read_warm_ms": 248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 44,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2592_boundary_timestamp_microsecond",
      "num": 2592,
      "name": "boundary_timestamp_microsecond",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2592_boundary_timestamp_microsecond.sql",
      "read_script": "generator/spark-reads-df/verify_2592_boundary_timestamp_microsecond.py",
      "description": "Two rows differing by exactly 1 microsecond. row1: 1704067200000000 (2024-01-01 00:00:00.000000) row2: 1704067200000001 (2024-01-01 00:00:00.000001)",
      "status": "pass",
      "duration_ms": 1387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:13.855090+00:00",
      "read_cold_ms": 782,
      "read_warm_ms": 346,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 31,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2593_boundary_boolean_three_val",
      "num": 2593,
      "name": "boundary_boolean_three_val",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2593_boundary_boolean_three_val.sql",
      "read_script": "generator/spark-reads-df/verify_2593_boundary_boolean_three_val.py",
      "description": "BOOLEAN column with TRUE, FALSE, NULL. 300 rows (100 each).",
      "status": "pass",
      "duration_ms": 1277,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:15.133190+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 238,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 55,
      "tags": [
        "type:boolean",
        "type:boundary",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2594_boundary_empty_string_vs_null",
      "num": 2594,
      "name": "boundary_empty_string_vs_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2594_boundary_empty_string_vs_null.sql",
      "read_script": "generator/spark-reads-df/verify_2594_boundary_empty_string_vs_null.py",
      "description": "STRING col with '' (empty) and NULL. 200 rows, 100 each.",
      "status": "pass",
      "duration_ms": 1283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:16.416960+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 236,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 27,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2595_boundary_single_row_table",
      "num": 2595,
      "name": "boundary_single_row_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2595_boundary_single_row_table.sql",
      "read_script": "generator/spark-reads-df/verify_2595_boundary_single_row_table.py",
      "description": "Table with exactly 1 row. Then UPDATE, then DELETE, then INSERT 1. Tests DML operations on minimal table.",
      "status": "pass",
      "duration_ms": 2018,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:18.436297+00:00",
      "read_cold_ms": 967,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 147,
      "write_warm_ms": 447,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2596_boundary_double_special",
      "num": 2596,
      "name": "boundary_double_special",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2596_boundary_double_special.sql",
      "read_script": "generator/spark-reads-df/verify_2596_boundary_double_special.py",
      "description": "DOUBLE with very small (1e-300) and very large (1e300) values. 10 rows.",
      "status": "pass",
      "duration_ms": 1306,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:19.742805+00:00",
      "read_cold_ms": 778,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 143,
      "write_warm_ms": 42,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2597_boundary_zero_values",
      "num": 2597,
      "name": "boundary_zero_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2597_boundary_zero_values.sql",
      "read_script": "generator/spark-reads-df/verify_2597_boundary_zero_values.py",
      "description": "100 rows.",
      "status": "pass",
      "duration_ms": 1291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:21.034634+00:00",
      "read_cold_ms": 778,
      "read_warm_ms": 262,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 44,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2598_boundary_negative_numbers",
      "num": 2598,
      "name": "boundary_negative_numbers",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2598_boundary_negative_numbers.sql",
      "read_script": "generator/spark-reads-df/verify_2598_boundary_negative_numbers.py",
      "description": null,
      "status": "pass",
      "duration_ms": 1307,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:22.342629+00:00",
      "read_cold_ms": 735,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 76,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2599_boundary_max_columns_30",
      "num": 2599,
      "name": "boundary_max_columns_30",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2599_boundary_max_columns_30.sql",
      "read_script": "generator/spark-reads-df/verify_2599_boundary_max_columns_30.py",
      "description": "30 columns of mixed types. 500 rows. Verify all columns readable.",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:23.694610+00:00",
      "read_cold_ms": 777,
      "read_warm_ms": 289,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 67,
      "tags": [
        "type:boolean",
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/259_ict_cdc",
      "num": 259,
      "name": "ict_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/259_ict_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_259_ict_cdc.py",
      "description": "ICT + CDC - In-Commit Timestamps with Change Data Feed",
      "status": "pass",
      "duration_ms": 4748,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:45.326163+00:00",
      "read_cold_ms": 2558,
      "read_warm_ms": 973,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 34,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/25_table_features_new_table_creation",
      "num": 25,
      "name": "table_features_new_table_creation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/25_table_features_new_table_creation.sql",
      "read_script": "generator/spark-reads-df/verify_25_table_features_new_table_creation.py",
      "description": "Demonstrates table features for new table creation (CDC, DVs, columnMapping).",
      "status": "pass",
      "duration_ms": 3180,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:48.506791+00:00",
      "read_cold_ms": 1825,
      "read_warm_ms": 661,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 398,
      "write_warm_ms": 512,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2600_boundary_null_heavy",
      "num": 2600,
      "name": "boundary_null_heavy",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2600_boundary_null_heavy.sql",
      "read_script": "generator/spark-reads-df/verify_2600_boundary_null_heavy.py",
      "description": "500 rows where 80% of optional columns are NULL. id is never null; a, b, c, d are null when i%5 != 0 (80% null).",
      "status": "pass",
      "duration_ms": 1314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:25.009535+00:00",
      "read_cold_ms": 801,
      "read_warm_ms": 216,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 72,
      "tags": [
        "type:boolean",
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2601_boundary_all_types_one_row",
      "num": 2601,
      "name": "boundary_all_types_one_row",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2601_boundary_all_types_one_row.sql",
      "read_script": "generator/spark-reads-df/verify_2601_boundary_all_types_one_row.py",
      "description": "Single row with every supported type. flt_val FLOAT, bool_val BOOLEAN, dec_val DECIMAL(10,2), ts_val TIMESTAMP",
      "status": "pass",
      "duration_ms": 1270,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:26.280291+00:00",
      "read_cold_ms": 718,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 99,
      "tags": [
        "type:boolean",
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2602_boundary_decimal_zero_scale",
      "num": 2602,
      "name": "boundary_decimal_zero_scale",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2602_boundary_decimal_zero_scale.sql",
      "read_script": "generator/spark-reads-df/verify_2602_boundary_decimal_zero_scale.py",
      "description": "DECIMAL(10,0) - integer-like decimal. 500 rows.",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:27.632257+00:00",
      "read_cold_ms": 821,
      "read_warm_ms": 250,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 25,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2603_boundary_string_length_1",
      "num": 2603,
      "name": "boundary_string_length_1",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2603_boundary_string_length_1.sql",
      "read_script": "generator/spark-reads-df/verify_2603_boundary_string_length_1.py",
      "description": "Single-character strings. 500 rows with chars 'A' to 'Z' cycling (i%26).",
      "status": "pass",
      "duration_ms": 1282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:28.914655+00:00",
      "read_cold_ms": 751,
      "read_warm_ms": 239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 38,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2604_boundary_consecutive_updates",
      "num": 2604,
      "name": "boundary_consecutive_updates",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2604_boundary_consecutive_updates.sql",
      "read_script": "generator/spark-reads-df/verify_2604_boundary_consecutive_updates.py",
      "description": "INSERT 100 rows + 10 sequential UPDATEs to same column. val starts at 0, updated to 1, 2, ..., 10. Final val=10 for all.",
      "status": "pass",
      "duration_ms": 2005,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:30.920155+00:00",
      "read_cold_ms": 990,
      "read_warm_ms": 420,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 636,
      "write_warm_ms": 1230,
      "tags": [
        "type:boundary",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2605_boundary_min_max_after_delete",
      "num": 2605,
      "name": "boundary_min_max_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2605_boundary_min_max_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2605_boundary_min_max_after_delete.py",
      "description": "INSERT 100 rows (1-100) + DELETE WHERE id=1 + DELETE WHERE id=100. Verify min=2, max=99 after deletes.",
      "status": "pass",
      "duration_ms": 1699,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:32.620345+00:00",
      "read_cold_ms": 901,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 505,
      "tags": [
        "type:boundary",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2606_uniform_basic_insert",
      "num": 2606,
      "name": "uniform_basic_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2606_uniform_basic_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2606_uniform_basic_insert.py",
      "description": "UniForm Iceberg enabled + INSERT 500 rows.",
      "status": "pass",
      "duration_ms": 1323,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:33.943829+00:00",
      "read_cold_ms": 741,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 55,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2607_uniform_with_partition",
      "num": 2607,
      "name": "uniform_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2607_uniform_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2607_uniform_with_partition.py",
      "description": "UniForm + PARTITIONED BY(region). INSERT 500 rows. 3 regions: US, EU, APAC distributed round-robin.",
      "status": "pass",
      "duration_ms": 1313,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:35.257669+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 250,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 203,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2608_uniform_after_update",
      "num": 2608,
      "name": "uniform_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2608_uniform_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2608_uniform_after_update.py",
      "description": "UniForm + INSERT 500 + UPDATE 200 rows (id <= 200).",
      "status": "pass",
      "duration_ms": 1767,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:37.025703+00:00",
      "read_cold_ms": 983,
      "read_warm_ms": 393,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 536,
      "write_warm_ms": 302,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2609_uniform_after_delete",
      "num": 2609,
      "name": "uniform_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2609_uniform_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2609_uniform_after_delete.py",
      "description": "UniForm + INSERT 500 + DELETE 200 rows (id <= 200).",
      "status": "pass",
      "duration_ms": 1637,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:38.663268+00:00",
      "read_cold_ms": 898,
      "read_warm_ms": 358,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 179,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/260_invariant_not_null",
      "num": 260,
      "name": "invariant_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/260_invariant_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_260_invariant_not_null.py",
      "description": "NOT NULL constraint enforcement on INSERT.",
      "status": "pass",
      "duration_ms": 2062,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:47:50.570223+00:00",
      "read_cold_ms": 1281,
      "read_warm_ms": 407,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2610_uniform_after_merge",
      "num": 2610,
      "name": "uniform_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2610_uniform_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2610_uniform_after_merge.py",
      "description": "UniForm + INSERT 300 + MERGE 200 (insert new rows 301-500).",
      "status": "pass",
      "duration_ms": 1682,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:39:40.345536+00:00",
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      "manual_tags": [],
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    {
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      "manual_tags": [],
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        "direction:df-writes",
        "format:delta"
      ],
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      "concern_note": null
    },
    {
      "id": "df-writes/delta/2644_combo_cor_cdc_partition",
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      "status": "pass",
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      "error": null,
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        "format:delta"
      ],
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    },
    {
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      "verified_at": "2026-05-14T15:40:40.728516+00:00",
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        "type:integer",
        "dml:insert",
        "delta:constraints",
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        "delta:generated-columns",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2646_combo_dv_colmap_time_travel",
      "num": 2646,
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      "error": null,
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        "type:integer",
        "type:string",
        "dml:delete",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2647_combo_cdc_merge_optimize_vacuum",
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      "duration_ms": 2227,
      "error": null,
      "notes": null,
      "extras": {},
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        "type:integer",
        "type:string",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2648_combo_all_constraints_merge",
      "num": 2648,
      "name": "combo_all_constraints_merge",
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      "reader": "spark",
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      "error": null,
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      "verified_at": "2026-05-14T15:40:47.306730+00:00",
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      "skip_feature": null,
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        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2649_combo_identity_cdc_partition_merge",
      "num": 2649,
      "name": "combo_identity_cdc_partition_merge",
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      "reader": "spark",
      "write_script": "generator/df-sql/2649_combo_identity_cdc_partition_merge.sql",
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      "duration_ms": 1542,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "format:delta"
      ],
      "manual_tags": [],
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    {
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      "description": "Regex pattern matching constraints.",
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      "duration_ms": 2094,
      "error": null,
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      "extras": {},
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      "skip_feature": null,
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2650_combo_colmap_constraint_optimize",
      "num": 2650,
      "name": "combo_colmap_constraint_optimize",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2650_combo_colmap_constraint_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2650_combo_colmap_constraint_optimize.py",
      "description": "colmap + CHECK + OPTIMIZE. INSERT 500 + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 1248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:40:50.098672+00:00",
      "read_cold_ms": 713,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2651_combo_cdc_dv_partition_zorder",
      "num": 2651,
      "name": "combo_cdc_dv_partition_zorder",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2651_combo_cdc_dv_partition_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_2651_combo_cdc_dv_partition_zorder.py",
      "description": "CDC + DV + PARTITION + ZORDER. INSERT 500 + ZORDER +",
      "status": "pass",
      "duration_ms": 1975,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:40:52.074421+00:00",
      "read_cold_ms": 1014,
      "read_warm_ms": 358,
      "skip_feature": null,
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      "write_cold_ms": 110,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2652_combo_identity_default_merge",
      "num": 2652,
      "name": "combo_identity_default_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2652_combo_identity_default_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2652_combo_identity_default_merge.py",
      "description": "IDENTITY + DEFAULT + MERGE. INSERT 300 (omit id) +",
      "status": "pass",
      "duration_ms": 1333,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:40:53.408258+00:00",
      "read_cold_ms": 824,
      "read_warm_ms": 272,
      "skip_feature": null,
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      "write_cold_ms": 102,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2653_combo_checkpoint_cdc_colmap",
      "num": 2653,
      "name": "combo_checkpoint_cdc_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2653_combo_checkpoint_cdc_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2653_combo_checkpoint_cdc_colmap.py",
      "description": "v2 checkpoint + CDC + colmap. INSERT 500 + UPDATE 200 +",
      "status": "pass",
      "duration_ms": 2258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:40:55.667097+00:00",
      "read_cold_ms": 1258,
      "read_warm_ms": 382,
      "skip_feature": null,
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      "write_cold_ms": 339,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
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        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2654_combo_generated_cdc_merge",
      "num": 2654,
      "name": "combo_generated_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2654_combo_generated_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2654_combo_generated_cdc_merge.py",
      "description": "GENERATED(total AS base*2) + CDC + MERGE. INSERT 300 +",
      "status": "pass",
      "duration_ms": 2074,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:40:57.741891+00:00",
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      "read_warm_ms": 359,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2655_combo_partition_identity_cdc_dv",
      "num": 2655,
      "name": "combo_partition_identity_cdc_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2655_combo_partition_identity_cdc_dv.sql",
      "read_script": "generator/spark-reads-df/verify_2655_combo_partition_identity_cdc_dv.py",
      "description": "PARTITION + IDENTITY + CDC + DV. INSERT 300 + DELETE 100 +",
      "status": "pass",
      "duration_ms": 1959,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:40:59.702300+00:00",
      "read_cold_ms": 969,
      "read_warm_ms": 373,
      "skip_feature": null,
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      "tags": [
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        "dml:cdc-write",
        "dml:delete",
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        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2656_etl_scd_type1_overwrite",
      "num": 2656,
      "name": "etl_scd_type1_overwrite",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2656_etl_scd_type1_overwrite.sql",
      "read_script": "generator/spark-reads-df/verify_2656_etl_scd_type1_overwrite.py",
      "description": "Verify latest values for all matched rows.",
      "status": "pass",
      "duration_ms": 1786,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:01.489387+00:00",
      "read_cold_ms": 994,
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      "skip_feature": null,
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      "write_cold_ms": 172,
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      "tags": [
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        "dml:insert",
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        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2657_etl_scd_type2_history",
      "num": 2657,
      "name": "etl_scd_type2_history",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2657_etl_scd_type2_history.sql",
      "read_script": "generator/spark-reads-df/verify_2657_etl_scd_type2_history.py",
      "description": "(100 overlap existing ids 401-500, 100 brand new ids 501-600). Then INSERT new current versions for the 100 matched.",
      "status": "pass",
      "duration_ms": 1744,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:03.233996+00:00",
      "read_cold_ms": 960,
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      "skip_feature": null,
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      "write_cold_ms": 102,
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      "tags": [
        "type:boolean",
        "type:integer",
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2658_etl_incremental_append",
      "num": 2658,
      "name": "etl_incremental_append",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2658_etl_incremental_append.py",
      "description": "5 rounds of INSERT 200 rows each (ids 1-200, 201-400, 401-600, 601-800, 801-1000). Verify 1000 total, min=1, max=1000.",
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      "duration_ms": 1291,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:41:04.525528+00:00",
      "read_cold_ms": 778,
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2659_etl_full_refresh_overwrite",
      "num": 2659,
      "name": "etl_full_refresh_overwrite",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2659_etl_full_refresh_overwrite.sql",
      "read_script": "generator/spark-reads-df/verify_2659_etl_full_refresh_overwrite.py",
      "description": "INSERT 500 rows + INSERT OVERWRITE with 500 completely new rows (ids 501-1000). Verify 500 rows after overwrite, min(id)=501.",
      "status": "pass",
      "duration_ms": 1253,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:41:05.779438+00:00",
      "read_cold_ms": 730,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:insert",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/265_invariant_multi_col",
      "num": 265,
      "name": "invariant_multi_col",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/265_invariant_multi_col.sql",
      "read_script": "generator/spark-reads-df/verify_265_invariant_multi_col.py",
      "description": "Constraints that reference multiple columns.",
      "status": "pass",
      "duration_ms": 2603,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:48:02.206774+00:00",
      "read_cold_ms": 1753,
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      "skip_feature": null,
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      "tags": [
        "type:boundary",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/2660_etl_partition_swap",
      "num": 2660,
      "name": "etl_partition_swap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2660_etl_partition_swap.sql",
      "read_script": "generator/spark-reads-df/verify_2660_etl_partition_swap.py",
      "description": "PARTITIONED BY(region) with 4 values + INSERT 500 + INSERT OVERWRITE for region='na' with 50 new rows. Verify other regions unchanged.",
      "status": "pass",
      "duration_ms": 1330,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:07.110425+00:00",
      "read_cold_ms": 815,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 353,
      "write_warm_ms": 235,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2661_etl_backfill_historical",
      "num": 2661,
      "name": "etl_backfill_historical",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2661_etl_backfill_historical.sql",
      "read_script": "generator/spark-reads-df/verify_2661_etl_backfill_historical.py",
      "description": "INSERT 500 (ids 501-1000) + INSERT 500 (ids 1-500, \"older\" backfill data). Verify 1000 total, all ids 1-1000 present.",
      "status": "pass",
      "duration_ms": 1443,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:08.553927+00:00",
      "read_cold_ms": 851,
      "read_warm_ms": 263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2662_etl_dedup_merge",
      "num": 2662,
      "name": "etl_dedup_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2662_etl_dedup_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2662_etl_dedup_merge.py",
      "description": "INSERT 500 unique rows + MERGE 500 source (all match, ids 1-500). WHEN MATCHED AND src.val > t.val THEN UPDATE SET val=src.val. Keeps max val per id.",
      "status": "pass",
      "duration_ms": 1694,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:10.248884+00:00",
      "read_cold_ms": 933,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 94,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2663_etl_late_arriving",
      "num": 2663,
      "name": "etl_late_arriving",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2663_etl_late_arriving.sql",
      "read_script": "generator/spark-reads-df/verify_2663_etl_late_arriving.py",
      "description": "INSERT 500 (ids 1-500 with ts based on i) + INSERT 200 (ids 501-700 with earlier timestamps). Verify 700 total rows.",
      "status": "pass",
      "duration_ms": 1332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:11.581602+00:00",
      "read_cold_ms": 800,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2664_etl_gdpr_delete",
      "num": 2664,
      "name": "etl_gdpr_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2664_etl_gdpr_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2664_etl_gdpr_delete.py",
      "description": "CDC + INSERT 500 customers + DELETE WHERE id <= 50. Verify 450 rows + CDF has 50 delete records.",
      "status": "pass",
      "duration_ms": 2037,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:13.619283+00:00",
      "read_cold_ms": 1033,
      "read_warm_ms": 359,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2665_etl_schema_migration",
      "num": 2665,
      "name": "etl_schema_migration",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2665_etl_schema_migration.sql",
      "read_script": "generator/spark-reads-df/verify_2665_etl_schema_migration.py",
      "description": "INSERT 500 (id, val) + ALTER ADD COLUMN tag STRING + UPDATE SET tag for ids 1-250. Verify 500 rows, 250 non-null tags, 250 null tags.",
      "status": "pass",
      "duration_ms": 1762,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:15.382260+00:00",
      "read_cold_ms": 974,
      "read_warm_ms": 359,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 141,
      "write_warm_ms": 143,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2666_etl_compact_micro_batch",
      "num": 2666,
      "name": "etl_compact_micro_batch",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2666_etl_compact_micro_batch.sql",
      "read_script": "generator/spark-reads-df/verify_2666_etl_compact_micro_batch.py",
      "description": "50 x INSERT 20 rows + OPTIMIZE. Verify 1000 rows, fewer files after optimize.",
      "status": "pass",
      "duration_ms": 1722,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:17.104573+00:00",
      "read_cold_ms": 1171,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 5798,
      "write_warm_ms": 7813,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2667_etl_cdc_audit_trail",
      "num": 2667,
      "name": "etl_cdc_audit_trail",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2667_etl_cdc_audit_trail.sql",
      "read_script": "generator/spark-reads-df/verify_2667_etl_cdc_audit_trail.py",
      "description": "CDC + INSERT 300 + UPDATE 100 (ids 1-100) + DELETE 50 (ids 201-250). Verify 250 rows + CDF has insert(300) + update images + delete(50).",
      "status": "pass",
      "duration_ms": 1933,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:19.037971+00:00",
      "read_cold_ms": 950,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2668_etl_merge_schema_evolve",
      "num": 2668,
      "name": "etl_merge_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2668_etl_merge_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2668_etl_merge_schema_evolve.py",
      "description": "INSERT 300 (id, val) + ALTER ADD tag STRING + MERGE 200 source (with tag). WHEN MATCHED UPDATE SET tag=src.tag, WHEN NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 1696,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:20.735087+00:00",
      "read_cold_ms": 940,
      "read_warm_ms": 349,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 229,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2669_etl_daily_partition_append",
      "num": 2669,
      "name": "etl_daily_partition_append",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2669_etl_daily_partition_append.sql",
      "read_script": "generator/spark-reads-df/verify_2669_etl_daily_partition_append.py",
      "description": "PARTITIONED BY(day_key STRING) + 10 inserts of 100 rows each with day_key='day_01' through 'day_10'. Verify 1000 rows, 10 partitions.",
      "status": "pass",
      "duration_ms": 1678,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:22.414041+00:00",
      "read_cold_ms": 1132,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 750,
      "write_warm_ms": 1203,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/266_invariant_violation",
      "num": 266,
      "name": "invariant_violation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/266_invariant_violation.sql",
      "read_script": "generator/spark-reads-df/verify_266_invariant_violation.py",
      "description": "Proper error handling when constraints are violated.",
      "status": "pass",
      "duration_ms": 2787,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:04.994629+00:00",
      "read_cold_ms": 2169,
      "read_warm_ms": 321,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 17,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2670_etl_upsert_soft_delete",
      "num": 2670,
      "name": "etl_upsert_soft_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2670_etl_upsert_soft_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2670_etl_upsert_soft_delete.py",
      "description": "INSERT 500 (deleted=false) + MERGE 200 source: WHEN MATCHED AND src.action='delete' THEN UPDATE SET deleted=true WHEN MATCHED THEN UPDATE SET val=src.val",
      "status": "pass",
      "duration_ms": 1740,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:24.154646+00:00",
      "read_cold_ms": 999,
      "read_warm_ms": 327,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 189,
      "write_warm_ms": 118,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2671_etl_cdc_replay_chain",
      "num": 2671,
      "name": "etl_cdc_replay_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2671_etl_cdc_replay_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2671_etl_cdc_replay_chain.py",
      "description": "CDC + INSERT 200 + UPDATE 100 (ids 1-100) + DELETE 50 (ids 151-200) + INSERT 100 (ids 201-300). Full DML chain. Verify 250 rows + CDF covers all operations.",
      "status": "pass",
      "duration_ms": 1821,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:25.975950+00:00",
      "read_cold_ms": 896,
      "read_warm_ms": 356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 312,
      "write_warm_ms": 173,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2672_etl_merge_insert_only",
      "num": 2672,
      "name": "etl_merge_insert_only",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2672_etl_merge_insert_only.sql",
      "read_script": "generator/spark-reads-df/verify_2672_etl_merge_insert_only.py",
      "description": "INSERT 500 + MERGE 500 source (ids 501-1000, no overlap) WHEN NOT MATCHED INSERT. Verify 1000 rows.",
      "status": "pass",
      "duration_ms": 1336,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:27.312642+00:00",
      "read_cold_ms": 782,
      "read_warm_ms": 264,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 238,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2673_etl_merge_update_only",
      "num": 2673,
      "name": "etl_merge_update_only",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2673_etl_merge_update_only.sql",
      "read_script": "generator/spark-reads-df/verify_2673_etl_merge_update_only.py",
      "description": "INSERT 500 + MERGE 500 source (ids 1-500, all overlap) WHEN MATCHED UPDATE SET val=src.val. Verify 500 rows, all updated.",
      "status": "pass",
      "duration_ms": 1726,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:29.039366+00:00",
      "read_cold_ms": 963,
      "read_warm_ms": 343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 248,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2674_etl_merge_conditional_update",
      "num": 2674,
      "name": "etl_merge_conditional_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2674_etl_merge_conditional_update.sql",
      "read_script": "generator/spark-reads-df/verify_2674_etl_merge_conditional_update.py",
      "description": "INSERT 500 (val=i*10) + MERGE 500 source (val=i*7 for all). WHEN MATCHED AND src.val > t.val THEN UPDATE SET val=src.val. i*7 > i*10 is never true, so NO rows get updated. Wait, let's make it interesting: source val = (501-i)*10 so some are bigger.",
      "status": "pass",
      "duration_ms": 1718,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:30.758231+00:00",
      "read_cold_ms": 952,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2675_etl_multi_stage_merge",
      "num": 2675,
      "name": "etl_multi_stage_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2675_etl_multi_stage_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2675_etl_multi_stage_merge.py",
      "description": "INSERT 300 (stage 1) + MERGE 200 (stage 2: 100 update ids 201-300 + 100 insert ids 301-400) + MERGE 200 (stage 3: 100 update ids 301-400 + 100 insert ids 401-500). Verify 500 rows after all stages.",
      "status": "pass",
      "duration_ms": 1730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:32.489323+00:00",
      "read_cold_ms": 1016,
      "read_warm_ms": 328,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121,
      "write_warm_ms": 266,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2676_merge_all_match_update",
      "num": 2676,
      "name": "merge_all_match_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2676_merge_all_match_update.sql",
      "read_script": "generator/spark-reads-df/verify_2676_merge_all_match_update.py",
      "description": "INSERT 500 + MERGE 500 (all match) WHEN MATCHED UPDATE. 100% match rate.",
      "status": "pass",
      "duration_ms": 1682,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:34.172239+00:00",
      "read_cold_ms": 902,
      "read_warm_ms": 335,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 244,
      "write_warm_ms": 309,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2677_merge_no_match_insert",
      "num": 2677,
      "name": "merge_no_match_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2677_merge_no_match_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2677_merge_no_match_insert.py",
      "description": "INSERT 500 + MERGE 500 (ids 501-1000, none match) WHEN NOT MATCHED INSERT. Verify 1000.",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:35.523618+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 251,
      "write_warm_ms": 170,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2678_merge_delete_all",
      "num": 2678,
      "name": "merge_delete_all",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2678_merge_delete_all.sql",
      "read_script": "generator/spark-reads-df/verify_2678_merge_delete_all.py",
      "description": "INSERT 500 + MERGE 500 (all match) WHEN MATCHED THEN DELETE. Verify 0 rows.",
      "status": "pass",
      "duration_ms": 1730,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:37.254746+00:00",
      "read_cold_ms": 982,
      "read_warm_ms": 374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 84,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2679_merge_compound_key",
      "num": 2679,
      "name": "merge_compound_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2679_merge_compound_key.sql",
      "read_script": "generator/spark-reads-df/verify_2679_merge_compound_key.py",
      "description": "INSERT 500 with (a, b) composite key + MERGE ON t.a=s.a AND t.b=s.b. a = i / 10, b = i % 10 (deterministic). Source overlaps first 250.",
      "status": "pass",
      "duration_ms": 1784,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:39.039253+00:00",
      "read_cold_ms": 1007,
      "read_warm_ms": 361,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 358,
      "write_warm_ms": 312,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/267_invariant_preserve",
      "num": 267,
      "name": "invariant_preserve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/267_invariant_preserve.sql",
      "read_script": "generator/spark-reads-df/verify_267_invariant_preserve.py",
      "description": "Constraints survive DeltaForge roundtrip.",
      "status": "pass",
      "duration_ms": 1873,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:06.868685+00:00",
      "read_cold_ms": 1465,
      "read_warm_ms": 141,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 21,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2680_merge_three_clauses",
      "num": 2680,
      "name": "merge_three_clauses",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2680_merge_three_clauses.sql",
      "read_script": "generator/spark-reads-df/verify_2680_merge_three_clauses.py",
      "description": "INSERT 500 + MERGE 500 source (ids 1-500): WHEN MATCHED AND src.flag=true THEN DELETE (ids where i%5=0 -> 100 rows) WHEN MATCHED THEN UPDATE SET val=src.val (remaining 400) Source also has 200 non-matching (ids 501-700): WHEN NOT MATCHED INSERT (200 new rows)",
      "status": "pass",
      "duration_ms": 1755,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:40.795441+00:00",
      "read_cold_ms": 987,
      "read_warm_ms": 343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2681_merge_with_subquery_source",
      "num": 2681,
      "name": "merge_with_subquery_source",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2681_merge_with_subquery_source.sql",
      "read_script": "generator/spark-reads-df/verify_2681_merge_with_subquery_source.py",
      "description": "INSERT 500 + MERGE USING (SELECT from generate_series) as source ON t.id=src.id WHEN MATCHED UPDATE. Source is ids 1-300.",
      "status": "pass",
      "duration_ms": 1656,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:42.451889+00:00",
      "read_cold_ms": 921,
      "read_warm_ms": 332,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 157,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2682_merge_nmbys_update",
      "num": 2682,
      "name": "merge_nmbys_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2682_merge_nmbys_update.sql",
      "read_script": "generator/spark-reads-df/verify_2682_merge_nmbys_update.py",
      "description": "INSERT 500 + MERGE 300 source (ids 1-300): WHEN MATCHED THEN UPDATE SET val=src.val WHEN NOT MATCHED BY SOURCE THEN UPDATE SET val=-1 Verify ids 301-500 have val=-1.",
      "status": "pass",
      "duration_ms": 1785,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:44.238087+00:00",
      "read_cold_ms": 998,
      "read_warm_ms": 358,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 195,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2683_merge_nmbys_delete",
      "num": 2683,
      "name": "merge_nmbys_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2683_merge_nmbys_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2683_merge_nmbys_delete.py",
      "description": "INSERT 500 + MERGE 300 source (ids 1-300): WHEN MATCHED THEN UPDATE SET val=src.val WHEN NOT MATCHED BY SOURCE THEN DELETE Verify 300 rows (ids 301-500 deleted).",
      "status": "pass",
      "duration_ms": 1701,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:45.940102+00:00",
      "read_cold_ms": 951,
      "read_warm_ms": 349,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2684_merge_large_source_small_target",
      "num": 2684,
      "name": "merge_large_source_small_target",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2684_merge_large_source_small_target.sql",
      "read_script": "generator/spark-reads-df/verify_2684_merge_large_source_small_target.py",
      "description": "INSERT 100 + MERGE 1000 source. 100 match (update), 900 not matched (insert). Verify 1000 rows.",
      "status": "pass",
      "duration_ms": 1731,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:47.671915+00:00",
      "read_cold_ms": 922,
      "read_warm_ms": 345,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 79,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2685_merge_small_source_large_target",
      "num": 2685,
      "name": "merge_small_source_large_target",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2685_merge_small_source_large_target.sql",
      "read_script": "generator/spark-reads-df/verify_2685_merge_small_source_large_target.py",
      "description": "INSERT 1000 + MERGE 100 source (ids 1-100, all match). Verify 1000 rows, 100 updated.",
      "status": "pass",
      "duration_ms": 1823,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:49.495982+00:00",
      "read_cold_ms": 971,
      "read_warm_ms": 379,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 256,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2686_merge_self_join_update",
      "num": 2686,
      "name": "merge_self_join_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2686_merge_self_join_update.sql",
      "read_script": "generator/spark-reads-df/verify_2686_merge_self_join_update.py",
      "description": "INSERT 500 + MERGE table with itself ON t.id=s.id WHEN MATCHED UPDATE SET val=val*2. Verify all val doubled.",
      "status": "pass",
      "duration_ms": 1727,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:51.224200+00:00",
      "read_cold_ms": 997,
      "read_warm_ms": 341,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 316,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2687_merge_with_cdc_all_clauses",
      "num": 2687,
      "name": "merge_with_cdc_all_clauses",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2687_merge_with_cdc_all_clauses.sql",
      "read_script": "generator/spark-reads-df/verify_2687_merge_with_cdc_all_clauses.py",
      "description": "CDC + INSERT 300 + MERGE 300 source with all clause types: WHEN MATCHED AND target.id <= 150 THEN DELETE (50 rows) WHEN MATCHED THEN UPDATE SET val=src.val (150 rows: ids 151-300) WHEN NOT MATCHED THEN INSERT (100 rows: ids 301-400)",
      "status": "pass",
      "duration_ms": 2090,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:53.315368+00:00",
      "read_cold_ms": 928,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1519,
      "write_warm_ms": 1075,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2688_merge_partitioned_cross",
      "num": 2688,
      "name": "merge_partitioned_cross",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2688_merge_partitioned_cross.sql",
      "read_script": "generator/spark-reads-df/verify_2688_merge_partitioned_cross.py",
      "description": "PARTITIONED BY(region) + INSERT 500 + MERGE 300 source across different partitions. WHEN MATCHED THEN UPDATE SET val=src.val, region=src.region WHEN NOT MATCHED INSERT (none expected -- all 1-300 exist)",
      "status": "pass",
      "duration_ms": 1704,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:55.019785+00:00",
      "read_cold_ms": 933,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 351,
      "write_warm_ms": 120,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2689_merge_with_identity",
      "num": 2689,
      "name": "merge_with_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2689_merge_with_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2689_merge_with_identity.py",
      "description": "IDENTITY column + INSERT 300 (omit id) + MERGE 200 WHEN NOT MATCHED INSERT (omit id). Verify all rows have unique auto IDs.",
      "status": "pass",
      "duration_ms": 1293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:56.313942+00:00",
      "read_cold_ms": 737,
      "read_warm_ms": 248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 240,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/268_invariant_nested",
      "num": 268,
      "name": "invariant_nested",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/268_invariant_nested.sql",
      "read_script": "generator/spark-reads-df/verify_268_invariant_nested.py",
      "description": "Constraints on struct field values.",
      "status": "pass",
      "duration_ms": 2933,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:09.802989+00:00",
      "read_cold_ms": 1547,
      "read_warm_ms": 412,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 18,
      "write_warm_ms": 18,
      "tags": [
        "type:integer",
        "type:struct",
        "dml:insert",
        "delta:constraints",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2690_merge_update_computed_expr",
      "num": 2690,
      "name": "merge_update_computed_expr",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2690_merge_update_computed_expr.sql",
      "read_script": "generator/spark-reads-df/verify_2690_merge_update_computed_expr.py",
      "description": "INSERT 500 + MERGE 500 WHEN MATCHED UPDATE SET val = t.val + src.val, name = CONCAT(t.name, '_merged'). Verify computed results.",
      "status": "pass",
      "duration_ms": 1771,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:41:58.086070+00:00",
      "read_cold_ms": 970,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 207,
      "write_warm_ms": 472,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2691_restore_basic_to_v1",
      "num": 2691,
      "name": "restore_basic_to_v1",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2691_restore_basic_to_v1.sql",
      "read_script": "generator/spark-reads-df/verify_2691_restore_basic_to_v1.py",
      "description": "Basic RESTORE TO VERSION AS OF 1.",
      "status": "pass",
      "duration_ms": 2327,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:00.413517+00:00",
      "read_cold_ms": 838,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 424,
      "write_warm_ms": 301,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2692_restore_after_delete",
      "num": 2692,
      "name": "restore_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2692_restore_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2692_restore_after_delete.py",
      "description": "RESTORE after DELETE restores deleted rows.",
      "status": "pass",
      "duration_ms": 1561,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:01.975286+00:00",
      "read_cold_ms": 796,
      "read_warm_ms": 273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121,
      "write_warm_ms": 76,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2693_restore_after_merge",
      "num": 2693,
      "name": "restore_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2693_restore_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2693_restore_after_merge.py",
      "description": "RESTORE after MERGE undoes merged rows.",
      "status": "pass",
      "duration_ms": 1560,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:03.535815+00:00",
      "read_cold_ms": 793,
      "read_warm_ms": 238,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 219,
      "write_warm_ms": 276,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2694_restore_after_truncate",
      "num": 2694,
      "name": "restore_after_truncate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2694_restore_after_truncate.sql",
      "read_script": "generator/spark-reads-df/verify_2694_restore_after_truncate.py",
      "description": "RESTORE after TRUNCATE brings all rows back.",
      "status": "pass",
      "duration_ms": 1483,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:05.019190+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 264,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2695_restore_after_optimize",
      "num": 2695,
      "name": "restore_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2695_restore_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2695_restore_after_optimize.py",
      "description": "RESTORE after OPTIMIZE reverts file compaction.",
      "status": "pass",
      "duration_ms": 1488,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:06.507462+00:00",
      "read_cold_ms": 746,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 362,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2696_restore_then_insert",
      "num": 2696,
      "name": "restore_then_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2696_restore_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2696_restore_then_insert.py",
      "description": "INSERT after RESTORE continues from restored state.",
      "status": "pass",
      "duration_ms": 1607,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:08.115152+00:00",
      "read_cold_ms": 792,
      "read_warm_ms": 288,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2697_restore_then_merge",
      "num": 2697,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2697_restore_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2697_restore_then_merge.py",
      "description": "MERGE after RESTORE continues from restored state.",
      "status": "pass",
      "duration_ms": 1528,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:09.644052+00:00",
      "read_cold_ms": 800,
      "read_warm_ms": 244,
      "skip_feature": null,
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      "write_cold_ms": 260,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2698_restore_chain",
      "num": 2698,
      "name": "restore_chain",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2698_restore_chain.sql",
      "read_script": "generator/spark-reads-df/verify_2698_restore_chain.py",
      "description": "RESTORE to an intermediate version in a multi-INSERT chain.",
      "status": "pass",
      "duration_ms": 1524,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:11.168371+00:00",
      "read_cold_ms": 774,
      "read_warm_ms": 226,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2699_restore_with_cdc",
      "num": 2699,
      "name": "restore_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2699_restore_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2699_restore_with_cdc.py",
      "description": "RESTORE on a CDC-enabled table.",
      "status": "pass",
      "duration_ms": 1842,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:13.010992+00:00",
      "read_cold_ms": 815,
      "read_warm_ms": 238,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 307,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/269_conflict_write_write",
      "num": 269,
      "name": "conflict_write_write",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/269_conflict_write_write.sql",
      "read_script": "generator/spark-reads-df/verify_269_conflict_write_write.py",
      "description": "Concurrent write conflict detection.",
      "status": "pass",
      "duration_ms": 2387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:12.191495+00:00",
      "read_cold_ms": 1629,
      "read_warm_ms": 388,
      "skip_feature": null,
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      "write_cold_ms": 31,
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      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/26_table_features_existing_table_upgrade",
      "num": 26,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/26_table_features_existing_table_upgrade.sql",
      "read_script": "generator/spark-reads-df/verify_26_table_features_existing_table_upgrade.py",
      "description": "Demonstrates upgrading existing table with new features (CDC, DVs).",
      "status": "pass",
      "duration_ms": 4478,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:16.670904+00:00",
      "read_cold_ms": 2650,
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      "skip_feature": null,
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      "write_cold_ms": 327,
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      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2700_restore_with_colmap",
      "num": 2700,
      "name": "restore_with_colmap",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2700_restore_with_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2700_restore_with_colmap.py",
      "description": "RESTORE on a column-mapping (name mode) table.",
      "status": "pass",
      "duration_ms": 1483,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:14.494695+00:00",
      "read_cold_ms": 751,
      "read_warm_ms": 264,
      "skip_feature": null,
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      "write_cold_ms": 363,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2701_restore_with_partition",
      "num": 2701,
      "name": "restore_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2701_restore_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2701_restore_with_partition.py",
      "description": "RESTORE on a partitioned table restores deleted partitions.",
      "status": "pass",
      "duration_ms": 1566,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:16.062398+00:00",
      "read_cold_ms": 772,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 200,
      "write_warm_ms": 93,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2702_restore_with_identity",
      "num": 2702,
      "name": "restore_with_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2702_restore_with_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2702_restore_with_identity.py",
      "description": "RESTORE on a table with identity-like column preserves IDs.",
      "status": "pass",
      "duration_ms": 1341,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:17.404363+00:00",
      "read_cold_ms": 792,
      "read_warm_ms": 269,
      "skip_feature": null,
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      "write_cold_ms": 75,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2703_vacuum_basic",
      "num": 2703,
      "name": "vacuum_basic",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2703_vacuum_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2703_vacuum_basic.py",
      "description": "Basic VACUUM after DELETE with deletion vectors.",
      "status": "pass",
      "duration_ms": 1959,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:19.364300+00:00",
      "read_cold_ms": 979,
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      "skip_feature": null,
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      "write_cold_ms": 66,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2704_vacuum_after_optimize",
      "num": 2704,
      "name": "vacuum_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2704_vacuum_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2704_vacuum_after_optimize.py",
      "description": "VACUUM after OPTIMIZE cleans pre-compaction files.",
      "status": "pass",
      "duration_ms": 1499,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:20.863513+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 250,
      "skip_feature": null,
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      "write_cold_ms": 251,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2705_vacuum_after_update",
      "num": 2705,
      "name": "vacuum_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2705_vacuum_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2705_vacuum_after_update.py",
      "description": "VACUUM after UPDATE cleans old row versions.",
      "status": "pass",
      "duration_ms": 1924,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:22.788309+00:00",
      "read_cold_ms": 915,
      "read_warm_ms": 375,
      "skip_feature": null,
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      "write_cold_ms": 131,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2706_vacuum_preserves_cdc",
      "num": 2706,
      "name": "vacuum_preserves_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2706_vacuum_preserves_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2706_vacuum_preserves_cdc.py",
      "description": "VACUUM on CDC-enabled table. DELETE 200 + VACUUM.",
      "status": "pass",
      "duration_ms": 1906,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:24.695247+00:00",
      "read_cold_ms": 934,
      "read_warm_ms": 350,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 420,
      "write_warm_ms": 230,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2707_vacuum_partitioned",
      "num": 2707,
      "name": "vacuum_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2707_vacuum_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_2707_vacuum_partitioned.py",
      "description": "VACUUM on partitioned table after deleting one partition.",
      "status": "pass",
      "duration_ms": 1478,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:26.173853+00:00",
      "read_cold_ms": 764,
      "read_warm_ms": 247,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 346,
      "write_warm_ms": 194,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2708_vacuum_multiple_rounds",
      "num": 2708,
      "name": "vacuum_multiple_rounds",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2708_vacuum_multiple_rounds.sql",
      "read_script": "generator/spark-reads-df/verify_2708_vacuum_multiple_rounds.py",
      "description": "Multiple rounds of DELETE + VACUUM.",
      "status": "pass",
      "duration_ms": 1861,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:28.035502+00:00",
      "read_cold_ms": 932,
      "read_warm_ms": 344,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 499,
      "write_warm_ms": 365,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2709_vacuum_after_schema_evolve",
      "num": 2709,
      "name": "vacuum_after_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2709_vacuum_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2709_vacuum_after_schema_evolve.py",
      "description": "VACUUM after schema evolution (ALTER ADD COLUMN).",
      "status": "pass",
      "duration_ms": 1948,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:42:29.984771+00:00",
      "read_cold_ms": 984,
      "read_warm_ms": 349,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 390,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/270_conflict_serializable",
      "num": 270,
      "name": "conflict_serializable",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/270_conflict_serializable.sql",
      "read_script": "generator/spark-reads-df/verify_270_conflict_serializable.py",
      "description": "Serializable isolation detects read-write conflicts (phantom reads).",
      "status": "pass",
      "duration_ms": 2437,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:19.109740+00:00",
      "read_cold_ms": 1967,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 15,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2710_vacuum_then_insert",
      "num": 2710,
      "name": "vacuum_then_insert",
      "type": "df-writes",
      "format": "delta",
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      "reader": "spark",
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    {
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      "description": "High-cardinality partition key (500 distinct user_ids).",
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        "delta:partitioning",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2729_partition_with_special_chars",
      "num": 2729,
      "name": "partition_with_special_chars",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2729_partition_with_special_chars.py",
      "description": "Partition keys containing underscores and numbers.",
      "status": "pass",
      "duration_ms": 1321,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:12.445539+00:00",
      "read_cold_ms": 785,
      "read_warm_ms": 254,
      "skip_feature": null,
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      "write_cold_ms": 153,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/272_conflict_snapshot",
      "num": 272,
      "name": "conflict_snapshot",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_272_conflict_snapshot.py",
      "description": "Snapshot isolation reads consistent snapshot while allowing appends.",
      "status": "pass",
      "duration_ms": 3161,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:24.805320+00:00",
      "read_cold_ms": 1944,
      "read_warm_ms": 528,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "iceberg:snapshots",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2730_partition_overwrite_one",
      "num": 2730,
      "name": "partition_overwrite_one",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2730_partition_overwrite_one.py",
      "description": "INSERT OVERWRITE targeting a single partition while others unchanged.",
      "status": "pass",
      "duration_ms": 1310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:13.756130+00:00",
      "read_cold_ms": 744,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 255,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2731_partition_delete_entire",
      "num": 2731,
      "name": "partition_delete_entire",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "DELETE all rows from one partition.",
      "status": "pass",
      "duration_ms": 1266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:15.023264+00:00",
      "read_cold_ms": 760,
      "read_warm_ms": 237,
      "skip_feature": null,
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      "write_cold_ms": 69,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2732_partition_merge_cross",
      "num": 2732,
      "name": "partition_merge_cross",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2732_partition_merge_cross.py",
      "description": "MERGE touching rows across all partitions.",
      "status": "pass",
      "duration_ms": 1799,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:16.823099+00:00",
      "read_cold_ms": 969,
      "read_warm_ms": 354,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2733_partition_optimize",
      "num": 2733,
      "name": "partition_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2733_partition_optimize.py",
      "description": "OPTIMIZE on a partitioned table after many small batches.",
      "status": "pass",
      "duration_ms": 1699,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:18.522432+00:00",
      "read_cold_ms": 1142,
      "read_warm_ms": 247,
      "skip_feature": null,
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      "write_cold_ms": 2881,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2734_partition_zorder",
      "num": 2734,
      "name": "partition_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2734_partition_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_2734_partition_zorder.py",
      "description": "ZORDER BY on a partitioned table.",
      "status": "pass",
      "duration_ms": 1365,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:19.888143+00:00",
      "read_cold_ms": 834,
      "read_warm_ms": 256,
      "skip_feature": null,
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      "write_cold_ms": 85,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2735_partition_vacuum",
      "num": 2735,
      "name": "partition_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2735_partition_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_2735_partition_vacuum.py",
      "description": "VACUUM on a partitioned table after deletes.",
      "status": "pass",
      "duration_ms": 1697,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:21.586244+00:00",
      "read_cold_ms": 947,
      "read_warm_ms": 336,
      "skip_feature": null,
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      "write_cold_ms": 290,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2736_partition_schema_evolve",
      "num": 2736,
      "name": "partition_schema_evolve",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2736_partition_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2736_partition_schema_evolve.py",
      "description": "Schema evolution (ALTER ADD COLUMN) on a partitioned table.",
      "status": "pass",
      "duration_ms": 1320,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:22.907124+00:00",
      "read_cold_ms": 762,
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      "skip_feature": null,
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      "write_cold_ms": 470,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2737_partition_cdc",
      "num": 2737,
      "name": "partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2737_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2737_partition_cdc.py",
      "description": "CDC (Change Data Feed) on a partitioned table.",
      "status": "pass",
      "duration_ms": 2010,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:43:24.917856+00:00",
      "read_cold_ms": 986,
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      "skip_feature": null,
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      "write_cold_ms": 335,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2738_partition_restore",
      "num": 2738,
      "name": "partition_restore",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2738_partition_restore.sql",
      "read_script": "generator/spark-reads-df/verify_2738_partition_restore.py",
      "description": "RESTORE on a partitioned table.",
      "status": "pass",
      "duration_ms": 1290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:26.208995+00:00",
      "read_cold_ms": 704,
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      "skip_feature": null,
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      "write_cold_ms": 259,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2739_partition_identity",
      "num": 2739,
      "name": "partition_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2739_partition_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2739_partition_identity.py",
      "description": "IDENTITY column on a partitioned table.",
      "status": "pass",
      "duration_ms": 1358,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:27.567633+00:00",
      "read_cold_ms": 811,
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      "skip_feature": null,
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      "write_cold_ms": 241,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/273_conflict_file_level",
      "num": 273,
      "name": "conflict_file_level",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/273_conflict_file_level.sql",
      "read_script": "generator/spark-reads-df/verify_273_conflict_file_level.py",
      "description": "Detection when same physical file is modified concurrently.",
      "status": "pass",
      "duration_ms": 3741,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:28.547790+00:00",
      "read_cold_ms": 2474,
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      "skip_feature": null,
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      "write_cold_ms": 19,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2740_partition_colmap",
      "num": 2740,
      "name": "partition_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2740_partition_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2740_partition_colmap.py",
      "description": "Column mapping mode='name' on a partitioned table.",
      "status": "pass",
      "duration_ms": 1365,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:28.933768+00:00",
      "read_cold_ms": 778,
      "read_warm_ms": 257,
      "skip_feature": null,
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      "write_cold_ms": 55,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2741_dv_delete_single_row",
      "num": 2741,
      "name": "dv_delete_single_row",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2741_dv_delete_single_row.sql",
      "read_script": "generator/spark-reads-df/verify_2741_dv_delete_single_row.py",
      "description": "Deletion vector for a single row delete.",
      "status": "pass",
      "duration_ms": 1799,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:30.733134+00:00",
      "read_cold_ms": 1008,
      "read_warm_ms": 393,
      "skip_feature": null,
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      "write_cold_ms": 108,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2742_dv_delete_last_row",
      "num": 2742,
      "name": "dv_delete_last_row",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2742_dv_delete_last_row.sql",
      "read_script": "generator/spark-reads-df/verify_2742_dv_delete_last_row.py",
      "description": "DV delete of the last row (highest id).",
      "status": "pass",
      "duration_ms": 1745,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:32.478430+00:00",
      "read_cold_ms": 935,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2743_dv_delete_first_row",
      "num": 2743,
      "name": "dv_delete_first_row",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2743_dv_delete_first_row.sql",
      "read_script": "generator/spark-reads-df/verify_2743_dv_delete_first_row.py",
      "description": "DV delete of the first row (lowest id).",
      "status": "pass",
      "duration_ms": 1671,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:34.149954+00:00",
      "read_cold_ms": 921,
      "read_warm_ms": 349,
      "skip_feature": null,
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      "write_cold_ms": 65,
      "write_warm_ms": 38,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2744_dv_delete_all_rows",
      "num": 2744,
      "name": "dv_delete_all_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2744_dv_delete_all_rows.sql",
      "read_script": "generator/spark-reads-df/verify_2744_dv_delete_all_rows.py",
      "description": "DV delete of all rows in the table.",
      "status": "pass",
      "duration_ms": 1642,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:35.792503+00:00",
      "read_cold_ms": 925,
      "read_warm_ms": 335,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2745_dv_delete_then_reinsert",
      "num": 2745,
      "name": "dv_delete_then_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2745_dv_delete_then_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_2745_dv_delete_then_reinsert.py",
      "description": "DELETE half the rows then INSERT new rows.",
      "status": "pass",
      "duration_ms": 1726,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:43:37.519504+00:00",
      "read_cold_ms": 949,
      "read_warm_ms": 360,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2746_dv_multiple_deletes",
      "num": 2746,
      "name": "dv_multiple_deletes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2746_dv_multiple_deletes.sql",
      "read_script": "generator/spark-reads-df/verify_2746_dv_multiple_deletes.py",
      "description": "Multiple sequential deletes stacking DVs.",
      "status": "pass",
      "duration_ms": 1692,
      "error": null,
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    {
      "id": "df-writes/delta/2764_edge_float_special_values",
      "num": 2764,
      "name": "edge_float_special_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2764_edge_float_special_values.sql",
      "read_script": "generator/spark-reads-df/verify_2764_edge_float_special_values.py",
      "description": "Float and double boundary and near-zero values",
      "status": "pass",
      "duration_ms": 1317,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:08.868921+00:00",
      "read_cold_ms": 760,
      "read_warm_ms": 295,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 34,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2765_edge_empty_string_vs_null",
      "num": 2765,
      "name": "edge_empty_string_vs_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2765_edge_empty_string_vs_null.sql",
      "read_script": "generator/spark-reads-df/verify_2765_edge_empty_string_vs_null.py",
      "description": "Distinguishing NULL, empty string, whitespace-only string, and normal string",
      "status": "pass",
      "duration_ms": 1349,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:10.218984+00:00",
      "read_cold_ms": 820,
      "read_warm_ms": 245,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 30,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2766_edge_unicode_data_values",
      "num": 2766,
      "name": "edge_unicode_data_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2766_edge_unicode_data_values.sql",
      "read_script": "generator/spark-reads-df/verify_2766_edge_unicode_data_values.py",
      "description": "Strings with varying lengths generated deterministically via generate_series",
      "status": "pass",
      "duration_ms": 1324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:11.543891+00:00",
      "read_cold_ms": 794,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2767_edge_very_long_string",
      "num": 2767,
      "name": "edge_very_long_string",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2767_edge_very_long_string.sql",
      "read_script": "generator/spark-reads-df/verify_2767_edge_very_long_string.py",
      "description": "Storing very large string values (1K, 10K, 100K characters) in a Delta table",
      "status": "pass",
      "duration_ms": 1318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:12.862306+00:00",
      "read_cold_ms": 766,
      "read_warm_ms": 277,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2768_edge_decimal_38_18_extremes",
      "num": 2768,
      "name": "edge_decimal_38_18_extremes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2768_edge_decimal_38_18_extremes.sql",
      "read_script": "generator/spark-reads-df/verify_2768_edge_decimal_38_18_extremes.py",
      "description": "DECIMAL(38,18) extreme values including max positive, max negative, near-zero, and fractional",
      "status": "pass",
      "duration_ms": 1386,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:14.248804+00:00",
      "read_cold_ms": 814,
      "read_warm_ms": 306,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 75,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2769_edge_date_extremes",
      "num": 2769,
      "name": "edge_date_extremes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2769_edge_date_extremes.sql",
      "read_script": "generator/spark-reads-df/verify_2769_edge_date_extremes.py",
      "description": "DATE values spanning Unix epoch, far future, far past, and a mid-century date",
      "status": "pass",
      "duration_ms": 1340,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:15.589385+00:00",
      "read_cold_ms": 800,
      "read_warm_ms": 264,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 34,
      "tags": [
        "type:boundary",
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/276_conflict_auto_resolve",
      "num": 276,
      "name": "conflict_auto_resolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/276_conflict_auto_resolve.sql",
      "read_script": "generator/spark-reads-df/verify_276_conflict_auto_resolve.py",
      "description": "Automatic conflict resolution with rebase strategy.",
      "status": "pass",
      "duration_ms": 1898,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:35.167956+00:00",
      "read_cold_ms": 1430,
      "read_warm_ms": 179,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2770_edge_timestamp_microsecond_precision",
      "num": 2770,
      "name": "edge_timestamp_microsecond_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2770_edge_timestamp_microsecond_precision.sql",
      "read_script": "generator/spark-reads-df/verify_2770_edge_timestamp_microsecond_precision.py",
      "description": "TIMESTAMP microsecond precision with values differing by 1us, 123456us, 60s, and 1 day",
      "status": "pass",
      "duration_ms": 1348,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:16.938172+00:00",
      "read_cold_ms": 794,
      "read_warm_ms": 300,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2771_edge_all_nulls_every_column",
      "num": 2771,
      "name": "edge_all_nulls_every_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2771_edge_all_nulls_every_column.sql",
      "read_script": "generator/spark-reads-df/verify_2771_edge_all_nulls_every_column.py",
      "description": "Every nullable column holds NULL for every row; id column is NOT NULL",
      "status": "pass",
      "duration_ms": 1266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:18.205350+00:00",
      "read_cold_ms": 760,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 213,
      "write_warm_ms": 90,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2772_edge_binary_empty_and_large",
      "num": 2772,
      "name": "edge_binary_empty_and_large",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2772_edge_binary_empty_and_large.sql",
      "read_script": "generator/spark-reads-df/verify_2772_edge_binary_empty_and_large.py",
      "description": "BINARY column with empty, single-byte, 100-byte, and 10000-byte values",
      "status": "pass",
      "duration_ms": 1358,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:19.563632+00:00",
      "read_cold_ms": 830,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 92,
      "tags": [
        "type:binary",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2773_edge_boolean_all_true",
      "num": 2773,
      "name": "edge_boolean_all_true",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2773_edge_boolean_all_true.sql",
      "read_script": "generator/spark-reads-df/verify_2773_edge_boolean_all_true.py",
      "description": "BOOLEAN column where every row is true",
      "status": "pass",
      "duration_ms": 1333,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:20.896996+00:00",
      "read_cold_ms": 792,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 126,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2774_edge_boolean_all_false",
      "num": 2774,
      "name": "edge_boolean_all_false",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2774_edge_boolean_all_false.sql",
      "read_script": "generator/spark-reads-df/verify_2774_edge_boolean_all_false.py",
      "description": "BOOLEAN column where every row is false",
      "status": "pass",
      "duration_ms": 1354,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:22.251306+00:00",
      "read_cold_ms": 801,
      "read_warm_ms": 280,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 35,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2775_edge_single_partition_value",
      "num": 2775,
      "name": "edge_single_partition_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2775_edge_single_partition_value.sql",
      "read_script": "generator/spark-reads-df/verify_2775_edge_single_partition_value.py",
      "description": "Partitioned table where all rows share the same partition value",
      "status": "pass",
      "duration_ms": 1277,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:23.528930+00:00",
      "read_cold_ms": 753,
      "read_warm_ms": 258,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 30,
      "write_warm_ms": 90,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2776_edge_hundred_partitions",
      "num": 2776,
      "name": "edge_hundred_partitions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2776_edge_hundred_partitions.sql",
      "read_script": "generator/spark-reads-df/verify_2776_edge_hundred_partitions.py",
      "description": "Partitioned table with 100 distinct partition values (one row per partition)",
      "status": "pass",
      "duration_ms": 1510,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:25.039542+00:00",
      "read_cold_ms": 842,
      "read_warm_ms": 305,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 916,
      "write_warm_ms": 1053,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2777_edge_wide_table_50_cols",
      "num": 2777,
      "name": "edge_wide_table_50_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2777_edge_wide_table_50_cols.sql",
      "read_script": "generator/spark-reads-df/verify_2777_edge_wide_table_50_cols.py",
      "description": "Wide table with 50 columns (id + 49 integer columns)",
      "status": "pass",
      "duration_ms": 1385,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:26.425590+00:00",
      "read_cold_ms": 818,
      "read_warm_ms": 300,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2778_edge_wide_table_100_cols",
      "num": 2778,
      "name": "edge_wide_table_100_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2778_edge_wide_table_100_cols.sql",
      "read_script": "generator/spark-reads-df/verify_2778_edge_wide_table_100_cols.py",
      "description": "Wide table with 100 columns (id + 99 integer columns)",
      "status": "pass",
      "duration_ms": 1436,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:27.862515+00:00",
      "read_cold_ms": 834,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2779_edge_single_column_table",
      "num": 2779,
      "name": "edge_single_column_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2779_edge_single_column_table.sql",
      "read_script": "generator/spark-reads-df/verify_2779_edge_single_column_table.py",
      "description": "Table with a single column and no nullable or typed extras",
      "status": "pass",
      "duration_ms": 1359,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:29.222468+00:00",
      "read_cold_ms": 806,
      "read_warm_ms": 291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 79,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/277_conflict_retry",
      "num": 277,
      "name": "conflict_retry",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/277_conflict_retry.sql",
      "read_script": "generator/spark-reads-df/verify_277_conflict_retry.py",
      "description": "Configurable retry behavior on conflicts.",
      "status": "pass",
      "duration_ms": 1644,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:36.812787+00:00",
      "read_cold_ms": 1093,
      "read_warm_ms": 330,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 14,
      "write_warm_ms": 14,
      "tags": [
        "type:integer",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2780_edge_delete_all_rows",
      "num": 2780,
      "name": "edge_delete_all_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2780_edge_delete_all_rows.sql",
      "read_script": "generator/spark-reads-df/verify_2780_edge_delete_all_rows.py",
      "description": "DELETE WHERE removes every row; deletion vectors mark all 50 rows deleted",
      "status": "pass",
      "duration_ms": 2239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:31.462022+00:00",
      "read_cold_ms": 1033,
      "read_warm_ms": 364,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 181,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2781_edge_delete_all_reinsert",
      "num": 2781,
      "name": "edge_delete_all_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2781_edge_delete_all_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_2781_edge_delete_all_reinsert.py",
      "description": "Delete all rows via Deletion Vectors then reinsert new rows; old data must be fully absent",
      "status": "pass",
      "duration_ms": 1787,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:33.249477+00:00",
      "read_cold_ms": 1037,
      "read_warm_ms": 337,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 141,
      "write_warm_ms": 230,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2782_edge_update_no_matching_rows",
      "num": 2782,
      "name": "edge_update_no_matching_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2782_edge_update_no_matching_rows.sql",
      "read_script": "generator/spark-reads-df/verify_2782_edge_update_no_matching_rows.py",
      "description": "UPDATE with a predicate that matches zero rows leaves data unchanged",
      "status": "pass",
      "duration_ms": 1300,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:34.550201+00:00",
      "read_cold_ms": 776,
      "read_warm_ms": 247,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2783_edge_insert_duplicate_ids",
      "num": 2783,
      "name": "edge_insert_duplicate_ids",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2783_edge_insert_duplicate_ids.sql",
      "read_script": "generator/spark-reads-df/verify_2783_edge_insert_duplicate_ids.py",
      "description": "Delta tables allow duplicate primary-key-like id values across two inserts",
      "status": "pass",
      "duration_ms": 1357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:35.908073+00:00",
      "read_cold_ms": 768,
      "read_warm_ms": 301,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 238,
      "write_warm_ms": 182,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2784_edge_many_small_versions",
      "num": 2784,
      "name": "edge_many_small_versions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2784_edge_many_small_versions.sql",
      "read_script": "generator/spark-reads-df/verify_2784_edge_many_small_versions.py",
      "description": "20 single-row INSERT statements produce 20 Delta log versions; table reads correctly across all",
      "status": "pass",
      "duration_ms": 1725,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:37.633332+00:00",
      "read_cold_ms": 1146,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1799,
      "write_warm_ms": 1614,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2785_edge_partition_null_key",
      "num": 2785,
      "name": "edge_partition_null_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2785_edge_partition_null_key.sql",
      "read_script": "generator/spark-reads-df/verify_2785_edge_partition_null_key.py",
      "description": "Partitioned table where some partition key values are NULL; Spark must read the null partition correctly",
      "status": "pass",
      "duration_ms": 1690,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:39.324484+00:00",
      "read_cold_ms": 814,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 84,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2786_edge_string_with_delimiters",
      "num": 2786,
      "name": "edge_string_with_delimiters",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2786_edge_string_with_delimiters.sql",
      "read_script": "generator/spark-reads-df/verify_2786_edge_string_with_delimiters.py",
      "description": "String values containing delimiter characters (comma, quote, semicolon, pipe, backslash, equals)",
      "status": "pass",
      "duration_ms": 1275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:40.599839+00:00",
      "read_cold_ms": 763,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2787_edge_decimal_zero_scale",
      "num": 2787,
      "name": "edge_decimal_zero_scale",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2787_edge_decimal_zero_scale.sql",
      "read_script": "generator/spark-reads-df/verify_2787_edge_decimal_zero_scale.py",
      "description": "DECIMAL(10,0) columns store whole numbers without fractional part; min/max/sum read correctly",
      "status": "pass",
      "duration_ms": 1442,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:42.042655+00:00",
      "read_cold_ms": 839,
      "read_warm_ms": 297,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 65,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2788_edge_insert_empty_batch",
      "num": 2788,
      "name": "edge_insert_empty_batch",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2788_edge_insert_empty_batch.sql",
      "read_script": "generator/spark-reads-df/verify_2788_edge_insert_empty_batch.py",
      "description": "An INSERT that selects zero rows via a false WHERE clause creates a new version but adds no data",
      "status": "pass",
      "duration_ms": 1332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:43.375204+00:00",
      "read_cold_ms": 801,
      "read_warm_ms": 279,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2789_edge_tinyint_overflow_boundary",
      "num": 2789,
      "name": "edge_tinyint_overflow_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2789_edge_tinyint_overflow_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_2789_edge_tinyint_overflow_boundary.py",
      "description": "TINYINT boundary values -128 and 127 round-trip correctly through Delta and Parquet",
      "status": "pass",
      "duration_ms": 1296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:44.671617+00:00",
      "read_cold_ms": 780,
      "read_warm_ms": 265,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 58,
      "tags": [
        "type:boundary",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/278_decimal_basic",
      "num": 278,
      "name": "decimal_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/278_decimal_basic.sql",
      "read_script": "generator/spark-reads-df/verify_278_decimal_basic.py",
      "description": "Standard DECIMAL(10,2) handling with common precision",
      "status": "pass",
      "duration_ms": 1365,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:38.179220+00:00",
      "read_cold_ms": 1060,
      "read_warm_ms": 123,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 18,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2790_edge_smallint_boundaries",
      "num": 2790,
      "name": "edge_smallint_boundaries",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2790_edge_smallint_boundaries.sql",
      "read_script": "generator/spark-reads-df/verify_2790_edge_smallint_boundaries.py",
      "description": "SMALLINT boundary values -32768 and 32767 round-trip correctly through Delta and Parquet",
      "status": "pass",
      "duration_ms": 1368,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:46.040164+00:00",
      "read_cold_ms": 821,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 47,
      "tags": [
        "type:boundary",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2791_map_string_string_basic",
      "num": 2791,
      "name": "map_string_string_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2791_map_string_string_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2791_map_string_string_basic.py",
      "description": "Basic MAP<STRING, STRING> column with 2 keys per row",
      "status": "pass",
      "duration_ms": 1316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:47.356506+00:00",
      "read_cold_ms": 770,
      "read_warm_ms": 241,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2792_map_string_int_with_nulls",
      "num": 2792,
      "name": "map_string_int_with_nulls",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2792_map_string_int_with_nulls.sql",
      "read_script": "generator/spark-reads-df/verify_2792_map_string_int_with_nulls.py",
      "description": "MAP<STRING, INT> column with NULL maps every 3rd row",
      "status": "pass",
      "duration_ms": 1377,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:48.734132+00:00",
      "read_cold_ms": 830,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 217,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2793_map_after_update",
      "num": 2793,
      "name": "map_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2793_map_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2793_map_after_update.py",
      "description": "MAP<STRING, STRING> column survives UPDATE on adjacent INT column",
      "status": "pass",
      "duration_ms": 1778,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:50.512908+00:00",
      "read_cold_ms": 1021,
      "read_warm_ms": 374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 142,
      "write_warm_ms": 237,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2794_map_after_delete",
      "num": 2794,
      "name": "map_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2794_map_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2794_map_after_delete.py",
      "description": "MAP<STRING, INT> rows deleted via Deletion Vectors; absent IDs verified",
      "status": "pass",
      "duration_ms": 1786,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:52.299847+00:00",
      "read_cold_ms": 1014,
      "read_warm_ms": 365,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 58,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2795_map_with_cdc",
      "num": 2795,
      "name": "map_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2795_map_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2795_map_with_cdc.py",
      "description": "MAP<STRING, STRING> with Change Data Feed enabled; CDF captures insert + update change types",
      "status": "pass",
      "duration_ms": 2132,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:54.432187+00:00",
      "read_cold_ms": 1011,
      "read_warm_ms": 357,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 392,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2796_map_with_partition",
      "num": 2796,
      "name": "map_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2796_map_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2796_map_with_partition.py",
      "description": "MAP<STRING, STRING> column in a partitioned Delta table; 3 region partitions",
      "status": "pass",
      "duration_ms": 1352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:55.784918+00:00",
      "read_cold_ms": 804,
      "read_warm_ms": 250,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 236,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2797_map_int_double",
      "num": 2797,
      "name": "map_int_double",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2797_map_int_double.sql",
      "read_script": "generator/spark-reads-df/verify_2797_map_int_double.py",
      "description": "MAP<INT, DOUBLE> column with numeric key and double precision values",
      "status": "pass",
      "duration_ms": 1376,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:57.161172+00:00",
      "read_cold_ms": 820,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 31,
      "tags": [
        "type:floating",
        "type:integer",
        "type:map",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2798_map_empty_map",
      "num": 2798,
      "name": "map_empty_map",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2798_map_empty_map.sql",
      "read_script": "generator/spark-reads-df/verify_2798_map_empty_map.py",
      "description": "MAP<STRING, STRING> with mixed NULL, single-entry, and two-entry maps",
      "status": "pass",
      "duration_ms": 1358,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:58.519449+00:00",
      "read_cold_ms": 805,
      "read_warm_ms": 289,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2799_array_int_basic",
      "num": 2799,
      "name": "array_int_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2799_array_int_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2799_array_int_basic.py",
      "description": "Basic ARRAY<INT> column with 3 deterministic integer elements per row",
      "status": "pass",
      "duration_ms": 1329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:44:59.849460+00:00",
      "read_cold_ms": 813,
      "read_warm_ms": 236,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 46,
      "tags": [
        "type:array",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/279_zorder_comprehensive",
      "num": 279,
      "name": "zorder_comprehensive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/279_zorder_comprehensive.sql",
      "read_script": "generator/spark-reads-df/verify_279_zorder_comprehensive.py",
      "description": "Smart City IoT sensor data with OPTIMIZE ZORDER. Schema (16 columns): reading_id, sensor_id, district, sensor_type, metric_name, value, unit, timestamp, reading_date, reading_hour, alert_level, manufacturer, firmware_version, latitude, longitude, metadata",
      "status": "pass",
      "duration_ms": 3333,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:41.512536+00:00",
      "read_cold_ms": 1572,
      "read_warm_ms": 821,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 385,
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      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/27_table_features_supported_enumeration",
      "num": 27,
      "name": "table_features_supported_enumeration",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/27_table_features_supported_enumeration.sql",
      "read_script": "generator/spark-reads-df/verify_27_table_features_supported_enumeration.py",
      "description": "Demonstrates multiple Delta features working together: CDC, DV, column mapping.",
      "status": "pass",
      "duration_ms": 3349,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:44.862650+00:00",
      "read_cold_ms": 1731,
      "read_warm_ms": 672,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 378,
      "write_warm_ms": 149,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2800_array_string_basic",
      "num": 2800,
      "name": "array_string_basic",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2800_array_string_basic.sql",
      "read_script": "generator/spark-reads-df/verify_2800_array_string_basic.py",
      "description": "Basic ARRAY<STRING> column with 2 deterministic string elements per row",
      "status": "pass",
      "duration_ms": 1336,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:01.186030+00:00",
      "read_cold_ms": 801,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 54,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2801_array_after_update",
      "num": 2801,
      "name": "array_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2801_array_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2801_array_after_update.py",
      "description": "ARRAY columns survive UPDATE operations with deletion vectors.",
      "status": "pass",
      "duration_ms": 1813,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:03.000139+00:00",
      "read_cold_ms": 1090,
      "read_warm_ms": 318,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 145,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2802_array_after_delete",
      "num": 2802,
      "name": "array_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2802_array_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2802_array_after_delete.py",
      "description": "ARRAY columns survive DELETE operations; deletion vectors",
      "status": "pass",
      "duration_ms": 1803,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:04.803523+00:00",
      "read_cold_ms": 1047,
      "read_warm_ms": 375,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 139,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2803_array_with_nulls",
      "num": 2803,
      "name": "array_with_nulls",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2803_array_with_nulls.sql",
      "read_script": "generator/spark-reads-df/verify_2803_array_with_nulls.py",
      "description": "ARRAY<INT> columns that contain NULL elements at known",
      "status": "pass",
      "duration_ms": 3286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:32:59.663486+00:00",
      "read_cold_ms": 2377,
      "read_warm_ms": 375,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 70,
      "tags": [
        "type:array",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2804_array_empty_vs_null",
      "num": 2804,
      "name": "array_empty_vs_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2804_array_empty_vs_null.sql",
      "read_script": "generator/spark-reads-df/verify_2804_array_empty_vs_null.py",
      "description": "Distinction between a NULL array, an array containing a single",
      "status": "pass",
      "duration_ms": 1328,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:07.423165+00:00",
      "read_cold_ms": 775,
      "read_warm_ms": 254,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 34,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2805_array_with_cdc",
      "num": 2805,
      "name": "array_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2805_array_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2805_array_with_cdc.py",
      "description": "ARRAY<INT> columns with Change Data Feed enabled. The CDF log",
      "status": "pass",
      "duration_ms": 1849,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:09.272887+00:00",
      "read_cold_ms": 963,
      "read_warm_ms": 351,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 305,
      "tags": [
        "type:array",
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2806_struct_three_level_nested",
      "num": 2806,
      "name": "struct_three_level_nested",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2806_struct_three_level_nested.sql",
      "read_script": "generator/spark-reads-df/verify_2806_struct_three_level_nested.py",
      "description": "Three levels of STRUCT nesting: data.a.b.c (INT) and",
      "status": "pass",
      "duration_ms": 1361,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:10.634600+00:00",
      "read_cold_ms": 837,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 93,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2807_struct_with_array_field",
      "num": 2807,
      "name": "struct_with_array_field",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2807_struct_with_array_field.sql",
      "read_script": "generator/spark-reads-df/verify_2807_struct_with_array_field.py",
      "description": "STRUCT that contains an ARRAY<INT> field alongside a STRING",
      "status": "pass",
      "duration_ms": 1360,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:11.994811+00:00",
      "read_cold_ms": 797,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 178,
      "write_warm_ms": 74,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2808_struct_with_map_field",
      "num": 2808,
      "name": "struct_with_map_field",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2808_struct_with_map_field.sql",
      "read_script": "generator/spark-reads-df/verify_2808_struct_with_map_field.py",
      "description": "STRUCT containing a MAP<STRING, STRING> field. The map value",
      "status": "pass",
      "duration_ms": 1428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:13.423541+00:00",
      "read_cold_ms": 847,
      "read_warm_ms": 296,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2809_struct_after_schema_evolve",
      "num": 2809,
      "name": "struct_after_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2809_struct_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2809_struct_after_schema_evolve.py",
      "description": "Schema evolution adding a top-level STRING column to a table",
      "status": "pass",
      "duration_ms": 1335,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:14.759151+00:00",
      "read_cold_ms": 819,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/280_decimal_scale_widen",
      "num": 280,
      "name": "decimal_scale_widen",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/280_decimal_scale_widen.sql",
      "read_script": "generator/spark-reads-df/verify_280_decimal_scale_widen.py",
      "description": "Safe precision+scale widening from DECIMAL(10,2) to DECIMAL(15,6)",
      "status": "pass",
      "duration_ms": 1816,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:46.679614+00:00",
      "read_cold_ms": 1407,
      "read_warm_ms": 216,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 56,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2810_struct_after_update",
      "num": 2810,
      "name": "struct_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2810_struct_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_2810_struct_after_update.py",
      "description": "STRUCT columns survive an UPDATE that replaces the entire",
      "status": "pass",
      "duration_ms": 1824,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:16.584233+00:00",
      "read_cold_ms": 1037,
      "read_warm_ms": 379,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2811_struct_all_null_fields",
      "num": 2811,
      "name": "struct_all_null_fields",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2811_struct_all_null_fields.sql",
      "read_script": "generator/spark-reads-df/verify_2811_struct_all_null_fields.py",
      "description": "STRUCT where every inner field is NULL for the first batch of",
      "status": "pass",
      "duration_ms": 1322,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:17.906764+00:00",
      "read_cold_ms": 824,
      "read_warm_ms": 236,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 191,
      "write_warm_ms": 271,
      "tags": [
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2812_array_of_struct",
      "num": 2812,
      "name": "array_of_struct",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2812_array_of_struct.sql",
      "read_script": "generator/spark-reads-df/verify_2812_array_of_struct.py",
      "description": "ARRAY<STRUCT<...>>; each row holds an array of two struct",
      "status": "pass",
      "duration_ms": 1389,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:19.295978+00:00",
      "read_cold_ms": 826,
      "read_warm_ms": 270,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 166,
      "write_warm_ms": 105,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2813_map_of_array_values",
      "num": 2813,
      "name": "map_of_array_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2813_map_of_array_values.sql",
      "read_script": "generator/spark-reads-df/verify_2813_map_of_array_values.py",
      "description": "MAP<STRING, ARRAY<INT>>; each row carries a map with two",
      "status": "pass",
      "duration_ms": 1389,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:20.685887+00:00",
      "read_cold_ms": 818,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 71,
      "tags": [
        "type:array",
        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2814_struct_with_colmap",
      "num": 2814,
      "name": "struct_with_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2814_struct_with_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2814_struct_with_colmap.py",
      "description": "STRUCT column in a table with Delta column mapping mode=name.",
      "status": "pass",
      "duration_ms": 1397,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:22.084013+00:00",
      "read_cold_ms": 829,
      "read_warm_ms": 256,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 46,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2815_array_with_partition",
      "num": 2815,
      "name": "array_with_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2815_array_with_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2815_array_with_partition.py",
      "description": "ARRAY<INT> column in a partitioned Delta table. Four",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:23.435635+00:00",
      "read_cold_ms": 798,
      "read_warm_ms": 282,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121,
      "write_warm_ms": 117,
      "tags": [
        "type:array",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2816_cdc_dv_merge_triple",
      "num": 2816,
      "name": "cdc_dv_merge_triple",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2833_widen_cdc_partition",
      "num": 2833,
      "name": "widen_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2833_widen_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2833_widen_cdc_partition.py",
      "description": "ALTER COLUMN type widening (INT->BIGINT) + CDC + partitioned table; large values post-widen",
      "status": "pass",
      "duration_ms": 1425,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:53.429130+00:00",
      "read_cold_ms": 792,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 184,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:type-widening",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2834_rowtrack_cdc_merge",
      "num": 2834,
      "name": "rowtrack_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2834_rowtrack_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2834_rowtrack_cdc_merge.py",
      "description": "Row tracking + CDC + MERGE (update matched + insert not-matched); CDF captures all operations",
      "status": "pass",
      "duration_ms": 2006,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:55.436363+00:00",
      "read_cold_ms": 1048,
      "read_warm_ms": 365,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2835_ict_cdc_partition",
      "num": 2835,
      "name": "ict_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2835_ict_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2835_ict_cdc_partition.py",
      "description": "InCommitTimestamps + CDC + partitioned table; UPDATE captured in CDF",
      "status": "pass",
      "duration_ms": 1939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:57.376151+00:00",
      "read_cold_ms": 996,
      "read_warm_ms": 397,
      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2836_default_constraint_cdc",
      "num": 2836,
      "name": "default_constraint_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2836_default_constraint_cdc.py",
      "description": "Column DEFAULT + CHECK constraint + CDC; default applied on insert, constraint enforced",
      "status": "pass",
      "duration_ms": 1938,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:45:59.314847+00:00",
      "read_cold_ms": 1027,
      "read_warm_ms": 389,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 171,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2837_truncate_cdc_identity",
      "num": 2837,
      "name": "truncate_cdc_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2837_truncate_cdc_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2837_truncate_cdc_identity.py",
      "description": "TRUNCATE TABLE + CDC + IDENTITY; TRUNCATE clears data, IDENTITY HWM continues",
      "status": "pass",
      "duration_ms": 1628,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:00.943419+00:00",
      "read_cold_ms": 887,
      "read_warm_ms": 253,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2838_append_only_constraint_cdc",
      "num": 2838,
      "name": "append_only_constraint_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2838_append_only_constraint_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2838_append_only_constraint_cdc.py",
      "description": "appendOnly table property + CHECK constraint + CDC; only INSERT allowed",
      "status": "pass",
      "duration_ms": 1515,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:02.459121+00:00",
      "read_cold_ms": 830,
      "read_warm_ms": 255,
      "skip_feature": null,
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      "write_cold_ms": 166,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2839_zorder_cdc_colmap",
      "num": 2839,
      "name": "zorder_cdc_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2839_zorder_cdc_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2839_zorder_cdc_colmap.py",
      "description": "OPTIMIZE ZORDER BY + CDC + column mapping; data intact post-zorder, logical names preserved",
      "status": "pass",
      "duration_ms": 1448,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:03.907858+00:00",
      "read_cold_ms": 799,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 66,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/283_decimal_edge_cases",
      "num": 283,
      "name": "decimal_edge_cases",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/283_decimal_edge_cases.sql",
      "read_script": "generator/spark-reads-df/verify_283_decimal_edge_cases.py",
      "description": "Maximum/minimum decimal value handling",
      "status": "pass",
      "duration_ms": 3758,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:48:54.078009+00:00",
      "read_cold_ms": 2197,
      "read_warm_ms": 854,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 24,
      "write_warm_ms": 38,
      "tags": [
        "type:decimal",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2840_restore_cdc_identity",
      "num": 2840,
      "name": "restore_cdc_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2840_restore_cdc_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2840_restore_cdc_identity.py",
      "description": "RESTORE TABLE + CDC + IDENTITY; restore to version 1 undoes DELETE",
      "status": "pass",
      "duration_ms": 1635,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:05.544008+00:00",
      "read_cold_ms": 788,
      "read_warm_ms": 306,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 125,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2841_vacuum_cdc_colmap",
      "num": 2841,
      "name": "vacuum_cdc_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2841_vacuum_cdc_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2841_vacuum_cdc_colmap.py",
      "description": "VACUUM + CDC + column mapping; deleted rows absent after VACUUM, logical names intact",
      "status": "pass",
      "duration_ms": 1965,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:07.509944+00:00",
      "read_cold_ms": 1008,
      "read_warm_ms": 374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 245,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2842_merge_update_delete_insert_all_clauses",
      "num": 2842,
      "name": "merge_update_delete_insert_all_clauses",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2842_merge_update_delete_insert_all_clauses.sql",
      "read_script": "generator/spark-reads-df/verify_2842_merge_update_delete_insert_all_clauses.py",
      "description": "MERGE with all three clauses (DELETE matched, UPDATE matched, INSERT not-matched)",
      "status": "pass",
      "duration_ms": 1873,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:09.383466+00:00",
      "read_cold_ms": 1082,
      "read_warm_ms": 354,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
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      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2843_colmap_evolve_rename",
      "num": 2843,
      "name": "colmap_evolve_rename",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2843_colmap_evolve_rename.sql",
      "read_script": "generator/spark-reads-df/verify_2843_colmap_evolve_rename.py",
      "description": "Column mapping + RENAME COLUMN; old_name renamed to new_name, data preserved",
      "status": "pass",
      "duration_ms": 1391,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:10.774782+00:00",
      "read_cold_ms": 828,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 72,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2844_dv_cdc_time_travel",
      "num": 2844,
      "name": "dv_cdc_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2844_dv_cdc_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_2844_dv_cdc_time_travel.py",
      "description": "Deletion Vectors + CDC + time travel; version 1 has 20 rows, current has 15 after DELETE",
      "status": "pass",
      "duration_ms": 2940,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:13.715572+00:00",
      "read_cold_ms": 985,
      "read_warm_ms": 371,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2845_identity_optimize_vacuum",
      "num": 2845,
      "name": "identity_optimize_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2845_identity_optimize_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_2845_identity_optimize_vacuum.py",
      "description": "IDENTITY + OPTIMIZE + VACUUM; IDENTITY HWM survives compaction, final insert gets unique IDs",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:15.067082+00:00",
      "read_cold_ms": 823,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 262,
      "write_warm_ms": 341,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2846_gencol_colmap_cdc",
      "num": 2846,
      "name": "gencol_colmap_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2846_gencol_colmap_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2846_gencol_colmap_cdc.py",
      "description": "Generated column + column mapping + CDC; tax=price*0.1 auto-computed, UPDATE recalculates",
      "status": "pass",
      "duration_ms": 1961,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:17.029329+00:00",
      "read_cold_ms": 1024,
      "read_warm_ms": 394,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 155,
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      "tags": [
        "type:floating",
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2847_constraint_partition_optimize",
      "num": 2847,
      "name": "constraint_partition_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2847_constraint_partition_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_2847_constraint_partition_optimize.py",
      "description": "CHECK constraint + partitioned table + OPTIMIZE; constraint enforced, partitions intact post-optimize",
      "status": "pass",
      "duration_ms": 1502,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:18.531737+00:00",
      "read_cold_ms": 903,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 995,
      "write_warm_ms": 388,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2848_widen_identity_cdc",
      "num": 2848,
      "name": "widen_identity_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2848_widen_identity_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2848_widen_identity_cdc.py",
      "description": "Type widening (INT->BIGINT) + IDENTITY column + CDC; large values readable post-widen",
      "status": "pass",
      "duration_ms": 1469,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:20.001510+00:00",
      "read_cold_ms": 831,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2849_default_colmap_merge",
      "num": 2849,
      "name": "default_colmap_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2849_default_colmap_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2849_default_colmap_merge.py",
      "description": "Column DEFAULT values + column mapping + MERGE inserts; defaults applied to omitted columns",
      "status": "pass",
      "duration_ms": 1381,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:21.383527+00:00",
      "read_cold_ms": 831,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 183,
      "write_warm_ms": 124,
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        "type:integer",
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    },
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      "id": "df-writes/delta/284_decimal_comparison",
      "num": 284,
      "name": "decimal_comparison",
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      "read_script": "generator/spark-reads-df/verify_284_decimal_comparison.py",
      "description": "Semantic equality across different decimal representations",
      "status": "pass",
      "duration_ms": 3362,
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      "verified_at": "2026-05-20T00:48:57.442416+00:00",
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        "type:decimal",
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        "dml:insert",
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      ],
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    },
    {
      "id": "df-writes/delta/2850_partition_dv_zorder_vacuum",
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      "name": "partition_dv_zorder_vacuum",
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      "description": "Partitioned table + Deletion Vectors + OPTIMIZE ZORDER BY + VACUUM",
      "status": "pass",
      "duration_ms": 1807,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:23.191417+00:00",
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2851_rowtrack_colmap_merge",
      "num": 2851,
      "name": "rowtrack_colmap_merge",
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      "reader": "spark",
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      "status": "pass",
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      ],
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    },
    {
      "id": "df-writes/delta/2852_ict_identity_merge",
      "num": 2852,
      "name": "ict_identity_merge",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2852_ict_identity_merge.py",
      "description": "InCommitTimestamps + IDENTITY column + MERGE inserts new rows",
      "status": "pass",
      "duration_ms": 1361,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:25.987761+00:00",
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        "type:integer",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2853_gencol_default_constraint",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2853_gencol_default_constraint.py",
      "description": "Generated column + column DEFAULT + CHECK constraint; tax auto-computed from default price",
      "status": "pass",
      "duration_ms": 1399,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:27.387030+00:00",
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      "tags": [
        "type:floating",
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        "dml:insert",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2854_colmap_dv_optimize_vacuum",
      "num": 2854,
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      "description": "Column mapping + Deletion Vectors + OPTIMIZE + VACUUM; logical names intact post-vacuum",
      "status": "pass",
      "duration_ms": 1882,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:29.270103+00:00",
      "read_cold_ms": 1052,
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        "type:floating",
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        "dml:delete",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2855_cdc_five_dml_ops",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2855_cdc_five_dml_ops.py",
      "description": "CDC captures five distinct DML operations (INSERT, UPDATE, DELETE, INSERT, UPDATE)",
      "status": "pass",
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      "extras": {},
      "verified_at": "2026-05-14T15:46:31.553589+00:00",
      "read_cold_ms": 1028,
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      "tags": [
        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2856_time_travel_after_schema_evolve",
      "num": 2856,
      "name": "time_travel_after_schema_evolve",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2856_time_travel_after_schema_evolve.sql",
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      "description": "Reading an older version of a table before a column was added via ALTER TABLE",
      "status": "pass",
      "duration_ms": 2251,
      "error": null,
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      "verified_at": "2026-05-14T15:46:33.804899+00:00",
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      "tags": [
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        "dml:insert",
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        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2857_time_travel_after_merge",
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2857_time_travel_after_merge.py",
      "description": "Time travel before a MERGE operation that updated and inserted rows",
      "status": "pass",
      "duration_ms": 2616,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 987,
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      "tags": [
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2858_time_travel_after_truncate",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2858_time_travel_after_truncate.sql",
      "read_script": "generator/spark-reads-df/verify_2858_time_travel_after_truncate.py",
      "description": "Time travel back to data that existed before a TRUNCATE emptied the table",
      "status": "pass",
      "duration_ms": 2243,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:38.665370+00:00",
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      "skip_feature": null,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2859_time_travel_with_identity",
      "num": 2859,
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      "reader": "spark",
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      "description": "Time travel on a table with an IDENTITY column after a partial delete",
      "status": "pass",
      "duration_ms": 2581,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:41.246588+00:00",
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      "write_cold_ms": 214,
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      "tags": [
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        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/285_evolve_add_end",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3568,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:49:01.011607+00:00",
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2860_time_travel_with_default",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2860_time_travel_with_default.sql",
      "read_script": "generator/spark-reads-df/verify_2860_time_travel_with_default.py",
      "description": "Time travel on a table that had a column added with a DEFAULT value",
      "status": "pass",
      "duration_ms": 2246,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:43.492867+00:00",
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      "tags": [
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        "delta:time-travel",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2861_time_travel_version_zero",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2861_time_travel_version_zero.sql",
      "read_script": "generator/spark-reads-df/verify_2861_time_travel_version_zero.py",
      "description": "Time travel to version 0 (the empty table right after CREATE)",
      "status": "pass",
      "duration_ms": 2631,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:46.124406+00:00",
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2862_time_travel_five_versions",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2862_time_travel_five_versions.py",
      "description": "Time travel across five distinct DML versions (insert, insert, delete, update, insert)",
      "status": "pass",
      "duration_ms": 6443,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:52.568024+00:00",
      "read_cold_ms": 1096,
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      "skip_feature": null,
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      "write_cold_ms": 276,
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      "tags": [
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        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2863_restore_after_optimize",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2863_restore_after_optimize.py",
      "description": "RESTORE to pre-OPTIMIZE version still returns correct data",
      "status": "pass",
      "duration_ms": 1427,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:53.995535+00:00",
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      "tags": [
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      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/2864_restore_after_schema_evolve",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2864_restore_after_schema_evolve.py",
      "description": "RESTORE to a version before schema evolution drops the added column",
      "status": "pass",
      "duration_ms": 1363,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:55.359700+00:00",
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      "tags": [
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2865_restore_with_identity",
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      "read_script": "generator/spark-reads-df/verify_2865_restore_with_identity.py",
      "description": "RESTORE on a table with an IDENTITY column recovers all original rows",
      "status": "pass",
      "duration_ms": 1352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:46:56.712313+00:00",
      "read_cold_ms": 818,
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      "tags": [
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        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2866_restore_after_vacuum",
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      "name": "restore_after_vacuum",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2866_restore_after_vacuum.py",
      "description": "RESTORE to the most recent pre-vacuum version remains safe (current files untouched)",
      "status": "pass",
      "duration_ms": 1449,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:46:58.162249+00:00",
      "read_cold_ms": 888,
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        "type:integer",
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        "dml:insert",
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        "delta:time-travel",
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      "id": "df-writes/delta/2867_restore_then_dml",
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        "type:integer",
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        "dml:delete",
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      "id": "df-writes/delta/2868_restore_twice",
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      "status": "pass",
      "duration_ms": 1481,
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      "verified_at": "2026-05-14T15:47:01.103050+00:00",
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      "id": "df-writes/delta/2869_time_travel_with_colmap",
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      "id": "df-writes/delta/286_evolve_add_middle",
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      "id": "df-writes/delta/2870_time_travel_with_constraint",
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      "id": "df-writes/delta/2871_restore_partitioned",
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      "id": "df-writes/delta/2875_time_travel_many_versions",
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    },
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2883_maint_partition_optimize_vacuum",
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      "description": "Partitioned table with DELETE, OPTIMIZE, and VACUUM",
      "status": "pass",
      "duration_ms": 1978,
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      "verified_at": "2026-05-14T15:47:33.154703+00:00",
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:partitioning",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2884_maint_colmap_optimize_zorder",
      "num": 2884,
      "name": "maint_colmap_optimize_zorder",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1458,
      "error": null,
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      "verified_at": "2026-05-14T15:47:34.613728+00:00",
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "delta:optimize",
        "delta:z-order",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2885_maint_identity_optimize_vacuum_insert",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1377,
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        "type:integer",
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        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2886_maint_overwrite_optimize_vacuum",
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      "reader": "spark",
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      "description": "INSERT OVERWRITE replaces all data, then OPTIMIZE and VACUUM",
      "status": "pass",
      "duration_ms": 1426,
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      "extras": {},
      "verified_at": "2026-05-14T15:47:37.418967+00:00",
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        "type:integer",
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        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2887_maint_truncate_insert_optimize",
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      "reader": "spark",
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      "status": "pass",
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      "extras": {},
      "verified_at": "2026-05-14T15:47:38.798210+00:00",
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    },
    {
      "id": "df-writes/delta/2888_maint_interleaved_dml_optimize",
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      "duration_ms": 1481,
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        "type:integer",
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        "dml:delete",
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        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2889_maint_restore_then_optimize",
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      "reader": "spark",
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      "description": "RESTORE TABLE to an earlier version then OPTIMIZE the restored state",
      "status": "pass",
      "duration_ms": 1497,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:47:41.777876+00:00",
      "read_cold_ms": 944,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:optimize",
        "delta:time-travel",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/288_evolve_drop",
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      "read_script": "generator/spark-reads-df/verify_288_evolve_drop.py",
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      "status": "pass",
      "duration_ms": 2777,
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      "extras": {},
      "verified_at": "2026-05-20T00:49:09.066660+00:00",
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        "schema:add-column",
        "schema:drop-column",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2890_maint_dv_zorder_checkpoint",
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      "duration_ms": 2343,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2891_maint_three_vacuums",
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      "reader": "spark",
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      "duration_ms": 1961,
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      "extras": {},
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        "delta:deletion-vectors",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2892_maint_merge_zorder_merge",
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      "reader": "spark",
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      "description": "Two MERGE-insert rounds with ZORDER OPTIMIZE between them",
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      "duration_ms": 1478,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2893_maint_evolve_optimize_evolve",
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        "dml:insert",
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      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/delta/2894_maint_partition_rebalance",
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      "duration_ms": 1412,
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      "extras": {},
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2895_maint_cdc_vacuum_retention",
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      "reader": "spark",
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      "description": "CDC table with DELETE then VACUUM -- verifies deleted rows are absent",
      "status": "pass",
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      "extras": {},
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        "type:integer",
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      "manual_tags": [],
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    },
    {
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1539,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:47:54.044616+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2897_identity_after_optimize",
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      "reader": "spark",
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      "duration_ms": 5446,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:47:59.491617+00:00",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2898_identity_after_truncate_reinsert",
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      "reader": "spark",
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      "duration_ms": 1322,
      "error": null,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2899_identity_two_identity_cols",
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      "reader": "spark",
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      "description": "Two IDENTITY columns in the same table with different START WITH values",
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      "duration_ms": 1354,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:48:02.168824+00:00",
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        "dml:insert",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/289_evolve_nested_add",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_289_evolve_nested_add.py",
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      "status": "pass",
      "duration_ms": 10810,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T00:49:19.878706+00:00",
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        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/28_table_features_active_dependencies",
      "num": 28,
      "name": "table_features_active_dependencies",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/28_table_features_active_dependencies.sql",
      "read_script": "generator/spark-reads-df/verify_28_table_features_active_dependencies.py",
      "description": "Demonstrates active features and their dependencies - CDC, DV, column mapping.",
      "status": "pass",
      "duration_ms": 3838,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:23.718610+00:00",
      "read_cold_ms": 2402,
      "read_warm_ms": 686,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 251,
      "write_warm_ms": 290,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2900_identity_with_constraint",
      "num": 2900,
      "name": "identity_with_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2900_identity_with_constraint.py",
      "description": "IDENTITY column combined with a CHECK constraint on another column",
      "status": "pass",
      "duration_ms": 1374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:03.543674+00:00",
      "read_cold_ms": 800,
      "read_warm_ms": 280,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 195,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2901_identity_after_delete_gap",
      "num": 2901,
      "name": "identity_after_delete_gap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2901_identity_after_delete_gap.sql",
      "read_script": "generator/spark-reads-df/verify_2901_identity_after_delete_gap.py",
      "description": "IDENTITY HWM continues past a deleted ID range; gaps appear in sequence",
      "status": "pass",
      "duration_ms": 1746,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:05.290093+00:00",
      "read_cold_ms": 927,
      "read_warm_ms": 380,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 103,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2902_identity_with_gencol",
      "num": 2902,
      "name": "identity_with_gencol",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2902_identity_with_gencol.sql",
      "read_script": "generator/spark-reads-df/verify_2902_identity_with_gencol.py",
      "description": "IDENTITY column alongside a GENERATED ALWAYS AS computed column",
      "status": "pass",
      "duration_ms": 1420,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:06.711001+00:00",
      "read_cold_ms": 873,
      "read_warm_ms": 261,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 83,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2903_identity_with_colmap",
      "num": 2903,
      "name": "identity_with_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2903_identity_with_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2903_identity_with_colmap.py",
      "description": "IDENTITY column with column mapping mode enabled",
      "status": "pass",
      "duration_ms": 1302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:08.014185+00:00",
      "read_cold_ms": 767,
      "read_warm_ms": 259,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2904_identity_partition_merge",
      "num": 2904,
      "name": "identity_partition_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2904_identity_partition_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2904_identity_partition_merge.py",
      "description": "IDENTITY column on a partitioned table with MERGE inserting new rows",
      "status": "pass",
      "duration_ms": 1363,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:09.377467+00:00",
      "read_cold_ms": 840,
      "read_warm_ms": 252,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 192,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2905_identity_large_batch",
      "num": 2905,
      "name": "identity_large_batch",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2905_identity_large_batch.sql",
      "read_script": "generator/spark-reads-df/verify_2905_identity_large_batch.py",
      "description": "IDENTITY column correctness with a large single-batch insert of 1000 rows",
      "status": "pass",
      "duration_ms": 1438,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:10.815681+00:00",
      "read_cold_ms": 885,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2906_identity_after_restore",
      "num": 2906,
      "name": "identity_after_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2906_identity_after_restore.sql",
      "read_script": "generator/spark-reads-df/verify_2906_identity_after_restore.py",
      "description": "IDENTITY column HWM after RESTORE TABLE to an earlier version then re-insert",
      "status": "pass",
      "duration_ms": 1406,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:12.222641+00:00",
      "read_cold_ms": 834,
      "read_warm_ms": 298,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 355,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2907_identity_with_default",
      "num": 2907,
      "name": "identity_with_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2907_identity_with_default.sql",
      "read_script": "generator/spark-reads-df/verify_2907_identity_with_default.py",
      "description": "IDENTITY column alongside a column with a DEFAULT value expression",
      "status": "pass",
      "duration_ms": 1383,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:13.606249+00:00",
      "read_cold_ms": 845,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 81,
      "tags": [
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2908_identity_multi_batch_insert",
      "num": 2908,
      "name": "identity_multi_batch_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2908_identity_multi_batch_insert.sql",
      "read_script": "generator/spark-reads-df/verify_2908_identity_multi_batch_insert.py",
      "description": "IDENTITY uniqueness across five separate INSERT batches",
      "status": "pass",
      "duration_ms": 1381,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:14.988273+00:00",
      "read_cold_ms": 815,
      "read_warm_ms": 291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 275,
      "write_warm_ms": 379,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "scale:many-batches",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2909_identity_with_dv_delete",
      "num": 2909,
      "name": "identity_with_dv_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2909_identity_with_dv_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2909_identity_with_dv_delete.py",
      "description": "IDENTITY column with deletion vectors; IDs divisible by 3 removed",
      "status": "pass",
      "duration_ms": 1977,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:16.966132+00:00",
      "read_cold_ms": 1109,
      "read_warm_ms": 363,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/290_evolve_nested_rename",
      "num": 290,
      "name": "evolve_nested_rename",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/290_evolve_nested_rename.sql",
      "read_script": "generator/spark-reads-df/verify_290_evolve_nested_rename.py",
      "description": "Rename nested struct field with column mapping",
      "status": "pass",
      "duration_ms": 6936,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:30.657114+00:00",
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      "read_warm_ms": 314,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
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      "tags": [
        "type:integer",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2910_identity_with_rowtrack",
      "num": 2910,
      "name": "identity_with_rowtrack",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2910_identity_with_rowtrack.sql",
      "read_script": "generator/spark-reads-df/verify_2910_identity_with_rowtrack.py",
      "description": "IDENTITY column with row tracking enabled; UPDATE preserves uniqueness",
      "status": "pass",
      "duration_ms": 1910,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:18.876590+00:00",
      "read_cold_ms": 1056,
      "read_warm_ms": 412,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 124,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2911_gencol_multiple_generated",
      "num": 2911,
      "name": "gencol_multiple_generated",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2911_gencol_multiple_generated.sql",
      "read_script": "generator/spark-reads-df/verify_2911_gencol_multiple_generated.py",
      "description": "Multiple generated columns in one table -- total, tax, and",
      "status": "pass",
      "duration_ms": 1447,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:20.324154+00:00",
      "read_cold_ms": 916,
      "read_warm_ms": 228,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
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      "tags": [
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2912_gencol_after_delete",
      "num": 2912,
      "name": "gencol_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2912_gencol_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2912_gencol_after_delete.py",
      "description": "Generated column values survive DELETE operations. Rows with",
      "status": "pass",
      "duration_ms": 1970,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:22.295341+00:00",
      "read_cold_ms": 1125,
      "read_warm_ms": 428,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 258,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2913_gencol_after_merge",
      "num": 2913,
      "name": "gencol_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2913_gencol_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2913_gencol_after_merge.py",
      "description": "Generated column tax = price * 0.1 is recalculated correctly",
      "status": "pass",
      "duration_ms": 1926,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:24.222103+00:00",
      "read_cold_ms": 1074,
      "read_warm_ms": 379,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 126,
      "tags": [
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        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2914_gencol_with_identity",
      "num": 2914,
      "name": "gencol_with_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2914_gencol_with_identity.sql",
      "read_script": "generator/spark-reads-df/verify_2914_gencol_with_identity.py",
      "description": "Combining an IDENTITY column (auto-increment) with a separate",
      "status": "pass",
      "duration_ms": 1423,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:25.645316+00:00",
      "read_cold_ms": 878,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 23,
      "write_warm_ms": 52,
      "tags": [
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2915_gencol_string_upper",
      "num": 2915,
      "name": "gencol_string_upper",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2915_gencol_string_upper.sql",
      "read_script": "generator/spark-reads-df/verify_2915_gencol_string_upper.py",
      "description": "Generated column using a string function (UPPER). The engine",
      "status": "pass",
      "duration_ms": 1404,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:27.050493+00:00",
      "read_cold_ms": 855,
      "read_warm_ms": 263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2916_gencol_with_dv_delete",
      "num": 2916,
      "name": "gencol_with_dv_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2916_gencol_with_dv_delete.sql",
      "read_script": "generator/spark-reads-df/verify_2916_gencol_with_dv_delete.py",
      "description": "Generated column computed = val + 100 combined with Deletion",
      "status": "pass",
      "duration_ms": 1880,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:28.931564+00:00",
      "read_cold_ms": 970,
      "read_warm_ms": 452,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2917_gencol_date_diff",
      "num": 2917,
      "name": "gencol_date_diff",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2917_gencol_date_diff.sql",
      "read_script": "generator/spark-reads-df/verify_2917_gencol_date_diff.py",
      "description": "Generated column with a simple arithmetic expression on an",
      "status": "pass",
      "duration_ms": 1338,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:30.270562+00:00",
      "read_cold_ms": 804,
      "read_warm_ms": 246,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 26,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2918_gencol_boolean_expression",
      "num": 2918,
      "name": "gencol_boolean_expression",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2918_gencol_boolean_expression.sql",
      "read_script": "generator/spark-reads-df/verify_2918_gencol_boolean_expression.py",
      "description": "Generated column using a boolean comparison expression.",
      "status": "pass",
      "duration_ms": 1431,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:31.701836+00:00",
      "read_cold_ms": 862,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 24,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2919_gencol_with_colmap",
      "num": 2919,
      "name": "gencol_with_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2919_gencol_with_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_2919_gencol_with_colmap.py",
      "description": "Generated column doubled = val * 2 combined with column",
      "status": "pass",
      "duration_ms": 1422,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:33.124809+00:00",
      "read_cold_ms": 847,
      "read_warm_ms": 250,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/291_multi_evolve",
      "num": 291,
      "name": "multi_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/291_multi_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_291_multi_evolve.py",
      "description": "Multiple schema changes in sequence",
      "status": "pass",
      "duration_ms": 2790,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:33.448202+00:00",
      "read_cold_ms": 1949,
      "read_warm_ms": 354,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 28,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2920_gencol_optimize_preserves",
      "num": 2920,
      "name": "gencol_optimize_preserves",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2920_gencol_optimize_preserves.sql",
      "read_script": "generator/spark-reads-df/verify_2920_gencol_optimize_preserves.py",
      "description": "OPTIMIZE compaction does not corrupt generated column values.",
      "status": "pass",
      "duration_ms": 1409,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:34.534597+00:00",
      "read_cold_ms": 879,
      "read_warm_ms": 248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 367,
      "write_warm_ms": 356,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2921_gencol_evolve_add_gencol",
      "num": 2921,
      "name": "gencol_evolve_add_gencol",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2921_gencol_evolve_add_gencol.sql",
      "read_script": "generator/spark-reads-df/verify_2921_gencol_evolve_add_gencol.py",
      "description": "Schema evolution -- adding a GENERATED ALWAYS AS column to an",
      "status": "pass",
      "duration_ms": 1464,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:35.999277+00:00",
      "read_cold_ms": 872,
      "read_warm_ms": 283,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 196,
      "write_warm_ms": 271,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2922_gencol_decimal_round",
      "num": 2922,
      "name": "gencol_decimal_round",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2922_gencol_decimal_round.sql",
      "read_script": "generator/spark-reads-df/verify_2922_gencol_decimal_round.py",
      "description": "Generated column using ROUND on a DECIMAL column.",
      "status": "pass",
      "duration_ms": 1515,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:37.515221+00:00",
      "read_cold_ms": 928,
      "read_warm_ms": 282,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 110,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2923_gencol_modulo_partition",
      "num": 2923,
      "name": "gencol_modulo_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2923_gencol_modulo_partition.sql",
      "read_script": "generator/spark-reads-df/verify_2923_gencol_modulo_partition.py",
      "description": "Generated column used as the partition key. bucket = val % 4",
      "status": "pass",
      "duration_ms": 1460,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:38.976328+00:00",
      "read_cold_ms": 889,
      "read_warm_ms": 276,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 198,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2924_gencol_timestamp_extract",
      "num": 2924,
      "name": "gencol_timestamp_extract",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2924_gencol_timestamp_extract.sql",
      "read_script": "generator/spark-reads-df/verify_2924_gencol_timestamp_extract.py",
      "description": "Generated column that combines two integer fields into a",
      "status": "pass",
      "duration_ms": 1518,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:40.494985+00:00",
      "read_cold_ms": 890,
      "read_warm_ms": 334,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2925_gencol_after_update_verify",
      "num": 2925,
      "name": "gencol_after_update_verify",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2925_gencol_after_update_verify.sql",
      "read_script": "generator/spark-reads-df/verify_2925_gencol_after_update_verify.py",
      "description": "Generated column doubled = val * 2 is recalculated correctly",
      "status": "pass",
      "duration_ms": 1802,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:42.298064+00:00",
      "read_cold_ms": 1004,
      "read_warm_ms": 382,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 177,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2926_string_case_merge",
      "num": 2926,
      "name": "string_case_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2926_string_case_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2926_string_case_merge.py",
      "description": "Case-sensitive string matching in MERGE (simulates collation behavior)",
      "status": "pass",
      "duration_ms": 1908,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:44.206516+00:00",
      "read_cold_ms": 1102,
      "read_warm_ms": 387,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 181,
      "write_warm_ms": 88,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2927_string_upper_after_evolve",
      "num": 2927,
      "name": "string_upper_after_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2927_string_upper_after_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2927_string_upper_after_evolve.py",
      "description": "Schema evolution followed by UPPER() string function backfill via UPDATE",
      "status": "pass",
      "duration_ms": 1941,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:46.147972+00:00",
      "read_cold_ms": 1065,
      "read_warm_ms": 411,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 84,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2928_string_ops_with_cdc",
      "num": 2928,
      "name": "string_ops_with_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2928_string_ops_with_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2928_string_ops_with_cdc.py",
      "description": "String LOWER() operations with Change Data Feed enabled",
      "status": "pass",
      "duration_ms": 1985,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:48.133810+00:00",
      "read_cold_ms": 1140,
      "read_warm_ms": 412,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2929_string_length_constraint",
      "num": 2929,
      "name": "string_length_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2929_string_length_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_2929_string_length_constraint.py",
      "description": "CHECK constraint on string LENGTH enforced at write time",
      "status": "pass",
      "duration_ms": 1442,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:49.576567+00:00",
      "read_cold_ms": 916,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 73,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/292_evolve_cdc",
      "num": 292,
      "name": "evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/292_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_292_evolve_cdc.py",
      "description": "CDC works correctly across schema changes",
      "status": "pass",
      "duration_ms": 2312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:35.761590+00:00",
      "read_cold_ms": 1587,
      "read_warm_ms": 405,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 30,
      "write_warm_ms": 29,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2930_string_multi_column_ops",
      "num": 2930,
      "name": "string_multi_column_ops",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2930_string_multi_column_ops.sql",
      "read_script": "generator/spark-reads-df/verify_2930_string_multi_column_ops.py",
      "description": "Multiple string columns with concatenation to form a derived full_name column",
      "status": "pass",
      "duration_ms": 1487,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:51.064456+00:00",
      "read_cold_ms": 926,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 40,
      "tags": [
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2931_domain_rowtrack_after_dml",
      "num": 2931,
      "name": "domain_rowtrack_after_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2931_domain_rowtrack_after_dml.sql",
      "read_script": "generator/spark-reads-df/verify_2931_domain_rowtrack_after_dml.py",
      "description": "Row tracking domain metadata remains consistent after UPDATE + DELETE DML",
      "status": "pass",
      "duration_ms": 1913,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:52.977839+00:00",
      "read_cold_ms": 1092,
      "read_warm_ms": 381,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 235,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2932_domain_rowtrack_after_merge",
      "num": 2932,
      "name": "domain_rowtrack_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2932_domain_rowtrack_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_2932_domain_rowtrack_after_merge.py",
      "description": "Row tracking survives a MERGE that inserts new rows",
      "status": "pass",
      "duration_ms": 1519,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:54.498033+00:00",
      "read_cold_ms": 926,
      "read_warm_ms": 309,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 224,
      "write_warm_ms": 347,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2933_domain_rowtrack_vacuum",
      "num": 2933,
      "name": "domain_rowtrack_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2933_domain_rowtrack_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_2933_domain_rowtrack_vacuum.py",
      "description": "Row tracking metadata survives DELETE + VACUUM cycle",
      "status": "pass",
      "duration_ms": 1915,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:48:56.414015+00:00",
      "read_cold_ms": 1116,
      "read_warm_ms": 375,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2934_domain_rowtrack_cdc",
      "num": 2934,
      "name": "domain_rowtrack_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2934_domain_rowtrack_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_2934_domain_rowtrack_cdc.py",
      "description": "Row tracking and Change Data Feed coexist; UPDATE visible via CDC",
      "status": "pass",
      "duration_ms": 1922,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/2935_tblprops_custom_metadata",
      "num": 2935,
      "name": "tblprops_custom_metadata",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Data integrity with metadata STRING column alongside standard TBLPROPERTIES",
      "status": "pass",
      "duration_ms": 1385,
      "error": null,
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      "verified_at": "2026-05-14T15:48:59.722650+00:00",
      "read_cold_ms": 827,
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        "type:integer",
        "type:string",
        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2936_string_partition_sort",
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      "name": "string_partition_sort",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "STRING column used as partition key; 4 distinct partition directories",
      "status": "pass",
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      "extras": {},
      "verified_at": "2026-05-14T15:49:01.139771+00:00",
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2937_string_identity_combined",
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      "status": "pass",
      "duration_ms": 1501,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:02.640996+00:00",
      "read_cold_ms": 895,
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        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2938_rowtrack_with_colmap",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2938_rowtrack_with_colmap.py",
      "description": "Row tracking and column mapping (name mode) coexist; logical names remain readable",
      "status": "pass",
      "duration_ms": 1454,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:04.096044+00:00",
      "read_cold_ms": 883,
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        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2939_string_optimize_preserves",
      "num": 2939,
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "OPTIMIZE compacts files without altering string data values",
      "status": "pass",
      "duration_ms": 1434,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:05.530979+00:00",
      "read_cold_ms": 855,
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        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/293_evolve_cluster",
      "num": 293,
      "name": "evolve_cluster",
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/293_evolve_cluster.sql",
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      "description": "Clustering configuration survives schema evolution",
      "status": "pass",
      "duration_ms": 2938,
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      "extras": {},
      "verified_at": "2026-05-20T00:49:38.700805+00:00",
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        "dml:insert",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2940_rowtrack_after_schema_evolve",
      "num": 2940,
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      "reader": "spark",
      "write_script": "generator/df-sql/2940_rowtrack_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2940_rowtrack_after_schema_evolve.py",
      "description": "Row tracking survives schema evolution; original rows have NULL for new column",
      "status": "pass",
      "duration_ms": 1442,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:06.973979+00:00",
      "read_cold_ms": 873,
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2941_append_only_after_optimize",
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      "reader": "spark",
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      "description": "Append-only table survives OPTIMIZE without data loss",
      "status": "pass",
      "duration_ms": 1395,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:08.369843+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2942_append_only_with_partition",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 1347,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:09.717054+00:00",
      "read_cold_ms": 823,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2943_append_only_with_identity",
      "num": 2943,
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2943_append_only_with_identity.py",
      "description": "Append-only table with IDENTITY column auto-increment",
      "status": "pass",
      "duration_ms": 1441,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:11.158440+00:00",
      "read_cold_ms": 886,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2944_append_only_with_colmap",
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      "description": "Append-only table with column mapping (name mode)",
      "status": "pass",
      "duration_ms": 1431,
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    },
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      "id": "df-writes/delta/2945_append_only_large_batch",
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      "duration_ms": 1451,
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      "verified_at": "2026-05-14T15:49:14.042023+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2946_append_only_schema_evolve",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2946_append_only_schema_evolve.py",
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      "duration_ms": 1423,
      "error": null,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2947_append_only_with_gencol",
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      "description": "Append-only table with generated column (val * 2)",
      "status": "pass",
      "duration_ms": 1404,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:16.870088+00:00",
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      "manual_tags": [],
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    },
    {
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      "read_script": "generator/spark-reads-df/verify_2948_protocol_writer_v2.py",
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      "duration_ms": 1377,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:18.248180+00:00",
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    },
    {
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      "description": "Protocol writerVersion=4 required by generated column",
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      "duration_ms": 1451,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-14T15:49:19.699835+00:00",
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      "manual_tags": [],
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      "duration_ms": 2675,
      "error": null,
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      "verified_at": "2026-05-20T00:49:41.377551+00:00",
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    },
    {
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      "read_script": "generator/spark-reads-df/verify_2950_protocol_writer_v5_colmap.py",
      "description": "Protocol writerVersion=5, readerVersion=2 required by column mapping",
      "status": "pass",
      "duration_ms": 1464,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:21.164280+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2951_protocol_reader_v2",
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      "read_script": "generator/spark-reads-df/verify_2951_protocol_reader_v2.py",
      "description": "Explicit protocol readerVersion=2 with column mapping",
      "status": "pass",
      "duration_ms": 1480,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:22.644502+00:00",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2952_protocol_feature_flags",
      "num": 2952,
      "name": "protocol_feature_flags",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
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        "format:delta"
      ],
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    },
    {
      "id": "df-writes/delta/2953_protocol_upgrade_enable_dv",
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      "reader": "spark",
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      "status": "pass",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:25.978380+00:00",
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        "type:integer",
        "type:string",
        "dml:delete",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2954_protocol_minimal_v1v1",
      "num": 2954,
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      "verified_at": "2026-05-14T15:49:27.067190+00:00",
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    },
    {
      "id": "df-writes/delta/2955_protocol_cdc_colmap_combined",
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      "error": null,
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        "type:integer",
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        "dml:cdc-write",
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      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/delta/2956_scale_5000_rows",
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      "duration_ms": 1560,
      "error": null,
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      "verified_at": "2026-05-14T15:49:30.557725+00:00",
      "read_cold_ms": 886,
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      "skip_feature": null,
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        "type:boolean",
        "type:floating",
        "type:integer",
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        "format:delta"
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      "manual_tags": [],
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    },
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      "id": "df-writes/delta/2957_scale_100_versions",
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      "description": "100 separate single-row inserts creating 100 Delta log versions",
      "status": "pass",
      "duration_ms": 1902,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:32.460379+00:00",
      "read_cold_ms": 1207,
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      "skip_feature": null,
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        "type:integer",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2958_scale_200_cols",
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      "status": "pass",
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      "extras": {},
      "verified_at": "2026-05-14T15:49:34.246190+00:00",
      "read_cold_ms": 995,
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        "type:integer",
        "dml:insert",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2959_scale_1000_rows_with_dml",
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      "description": "1000-row table with UPDATE on first 200 rows, DELETE of last 100 rows",
      "status": "pass",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:36.222937+00:00",
      "read_cold_ms": 1105,
      "read_warm_ms": 402,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/295_delete_basic",
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      "writer": "delta-forge",
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      "read_script": "generator/spark-reads-df/verify_295_delete_basic.py",
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      "status": "pass",
      "duration_ms": 3078,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:44.456888+00:00",
      "read_cold_ms": 1754,
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      "skip_feature": null,
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      "write_cold_ms": 28,
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        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2960_scale_many_partitions_50",
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      "reader": "spark",
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      "description": "Table with 50 partitions, 20 rows per partition (1000 total)",
      "status": "pass",
      "duration_ms": 1602,
      "error": null,
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      "verified_at": "2026-05-14T15:49:37.826063+00:00",
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        "dml:insert",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2961_widen_restore_int_to_long",
      "num": 2961,
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2961_widen_restore_int_to_long.py",
      "description": "Type widening INT->BIGINT then RESTORE TO VERSION 1.",
      "status": "pass",
      "duration_ms": 2549,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:40.376163+00:00",
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      "tags": [
        "type:integer",
        "dml:insert",
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        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2962_widen_restore_decimal_precision",
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      "reader": "spark",
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      "description": "Type widening DECIMAL(10,2)->DECIMAL(18,4) then RESTORE TO VERSION 1.",
      "status": "pass",
      "duration_ms": 2256,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-14T15:49:42.633093+00:00",
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
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        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2963_widen_vacuum_int_to_long",
      "num": 2963,
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_2963_widen_vacuum_int_to_long.py",
      "description": "Type widening INT->BIGINT followed by VACUUM.",
      "status": "pass",
      "duration_ms": 1509,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:44.142615+00:00",
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      "write_cold_ms": 245,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2964_widen_vacuum_float_to_double",
      "num": 2964,
      "name": "widen_vacuum_float_to_double",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2964_widen_vacuum_float_to_double.sql",
      "read_script": "generator/spark-reads-df/verify_2964_widen_vacuum_float_to_double.py",
      "description": "Type widening FLOAT->DOUBLE, UPDATE, then VACUUM.",
      "status": "pass",
      "duration_ms": 2038,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:46.180933+00:00",
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      "skip_feature": null,
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        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
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        "schema:type-widening",
        "scale:wide-schema",
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        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/2965_widen_colmap_int_to_long",
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      "reader": "spark",
      "write_script": "generator/df-sql/2965_widen_colmap_int_to_long.sql",
      "read_script": "generator/spark-reads-df/verify_2965_widen_colmap_int_to_long.py",
      "description": "Column mapping (name mode) + type widening INT->BIGINT.",
      "status": "pass",
      "duration_ms": 1550,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:47.731693+00:00",
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      "skip_feature": null,
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2966_widen_colmap_decimal_scale",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2966_widen_colmap_decimal_scale.sql",
      "read_script": "generator/spark-reads-df/verify_2966_widen_colmap_decimal_scale.py",
      "description": "Column mapping (name mode) + type widening DECIMAL(10,2)->DECIMAL(18,4).",
      "status": "pass",
      "duration_ms": 1512,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:49.244310+00:00",
      "read_cold_ms": 946,
      "read_warm_ms": 263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 114,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2967_default_restore_literal",
      "num": 2967,
      "name": "default_restore_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2967_default_restore_literal.sql",
      "read_script": "generator/spark-reads-df/verify_2967_default_restore_literal.py",
      "description": "DEFAULT literal value + RESTORE.",
      "status": "pass",
      "duration_ms": 2838,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:52.083050+00:00",
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2968_default_restore_after_evolve",
      "num": 2968,
      "name": "default_restore_after_evolve",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2968_default_restore_after_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_2968_default_restore_after_evolve.py",
      "description": "Schema evolution (ADD COLUMN with DEFAULT) then RESTORE.",
      "status": "pass",
      "duration_ms": 2199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:54.282698+00:00",
      "read_cold_ms": 847,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2969_gencol_restore_computed",
      "num": 2969,
      "name": "gencol_restore_computed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2969_gencol_restore_computed.sql",
      "read_script": "generator/spark-reads-df/verify_2969_gencol_restore_computed.py",
      "description": "Generated column (doubled = base * 2) + UPDATE + RESTORE.",
      "status": "pass",
      "duration_ms": 2709,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:49:56.992165+00:00",
      "read_cold_ms": 822,
      "read_warm_ms": 262,
      "skip_feature": null,
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        "type:integer",
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      "read_cold_ms": 813,
      "read_warm_ms": 264,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2987_identity_vacuum_hwm_preserved",
      "num": 2987,
      "name": "identity_vacuum_hwm_preserved",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2987_identity_vacuum_hwm_preserved.sql",
      "read_script": "generator/spark-reads-df/verify_2987_identity_vacuum_hwm_preserved.py",
      "description": "IDENTITY column high-water-mark (HWM) is preserved after",
      "status": "pass",
      "duration_ms": 2153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:38.244505+00:00",
      "read_cold_ms": 1082,
      "read_warm_ms": 394,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 362,
      "write_warm_ms": 264,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2988_identity_constraint_range_check",
      "num": 2988,
      "name": "identity_constraint_range_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2988_identity_constraint_range_check.sql",
      "read_script": "generator/spark-reads-df/verify_2988_identity_constraint_range_check.py",
      "description": "IDENTITY column combined with CHECK constraint on a range.",
      "status": "pass",
      "duration_ms": 1580,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:39.825459+00:00",
      "read_cold_ms": 995,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 116,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2989_identity_constraint_positive",
      "num": 2989,
      "name": "identity_constraint_positive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2989_identity_constraint_positive.sql",
      "read_script": "generator/spark-reads-df/verify_2989_identity_constraint_positive.py",
      "description": "IDENTITY column with explicit START WITH 1 INCREMENT BY 1,",
      "status": "pass",
      "duration_ms": 1999,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:41.824891+00:00",
      "read_cold_ms": 1174,
      "read_warm_ms": 403,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 166,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/298_update_multi_col",
      "num": 298,
      "name": "update_multi_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/298_update_multi_col.sql",
      "read_script": "generator/spark-reads-df/verify_298_update_multi_col.py",
      "description": "UPDATE setting multiple columns at once",
      "status": "pass",
      "duration_ms": 3523,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:53.582450+00:00",
      "read_cold_ms": 2147,
      "read_warm_ms": 807,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2990_constraint_vacuum_check_preserved",
      "num": 2990,
      "name": "constraint_vacuum_check_preserved",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2990_constraint_vacuum_check_preserved.sql",
      "read_script": "generator/spark-reads-df/verify_2990_constraint_vacuum_check_preserved.py",
      "description": "CHECK constraint metadata survives VACUUM. After DELETE and",
      "status": "pass",
      "duration_ms": 2097,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:43.922543+00:00",
      "read_cold_ms": 1041,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 118,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2991_constraint_vacuum_multi_check",
      "num": 2991,
      "name": "constraint_vacuum_multi_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2991_constraint_vacuum_multi_check.sql",
      "read_script": "generator/spark-reads-df/verify_2991_constraint_vacuum_multi_check.py",
      "description": "Multiple CHECK constraints both survive OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 2205,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:46.128680+00:00",
      "read_cold_ms": 1144,
      "read_warm_ms": 399,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 194,
      "write_warm_ms": 207,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2992_gencol_merge_upsert",
      "num": 2992,
      "name": "gencol_merge_upsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2992_gencol_merge_upsert.sql",
      "read_script": "generator/spark-reads-df/verify_2992_gencol_merge_upsert.py",
      "description": "Generated column total = price * qty is recalculated correctly",
      "status": "pass",
      "duration_ms": 1986,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:48.115620+00:00",
      "read_cold_ms": 1122,
      "read_warm_ms": 390,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 154,
      "write_warm_ms": 206,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2993_gencol_merge_delete_reinsert",
      "num": 2993,
      "name": "gencol_merge_delete_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2993_gencol_merge_delete_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_2993_gencol_merge_delete_reinsert.py",
      "description": "Generated column doubled = base * 2 is correctly computed",
      "status": "pass",
      "duration_ms": 1876,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:49.992683+00:00",
      "read_cold_ms": 1021,
      "read_warm_ms": 413,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 132,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2994_gencol_colmap_computed",
      "num": 2994,
      "name": "gencol_colmap_computed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2994_gencol_colmap_computed.sql",
      "read_script": "generator/spark-reads-df/verify_2994_gencol_colmap_computed.py",
      "description": "Generated column sum_ab = a + b combined with column mapping",
      "status": "pass",
      "duration_ms": 1472,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:51.464978+00:00",
      "read_cold_ms": 873,
      "read_warm_ms": 288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 73,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2995_uniform_merge_upsert",
      "num": 2995,
      "name": "uniform_merge_upsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2995_uniform_merge_upsert.sql",
      "read_script": "generator/spark-reads-df/verify_2995_uniform_merge_upsert.py",
      "description": "UniForm Iceberg format combined with MERGE upsert.",
      "status": "pass",
      "duration_ms": 1886,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:53.351573+00:00",
      "read_cold_ms": 1055,
      "read_warm_ms": 421,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 500,
      "write_warm_ms": 231,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2996_uniform_cdc_tracking",
      "num": 2996,
      "name": "uniform_cdc_tracking",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2996_uniform_cdc_tracking.sql",
      "read_script": "generator/spark-reads-df/verify_2996_uniform_cdc_tracking.py",
      "description": "UniForm Iceberg combined with CDC (Change Data Feed).",
      "status": "pass",
      "duration_ms": 2114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:55.466762+00:00",
      "read_cold_ms": 1023,
      "read_warm_ms": 386,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 470,
      "write_warm_ms": 438,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2997_uniform_optimize_compaction",
      "num": 2997,
      "name": "uniform_optimize_compaction",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2997_uniform_optimize_compaction.sql",
      "read_script": "generator/spark-reads-df/verify_2997_uniform_optimize_compaction.py",
      "description": "UniForm Iceberg combined with OPTIMIZE compaction. Five",
      "status": "pass",
      "duration_ms": 1735,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:57.202573+00:00",
      "read_cold_ms": 891,
      "read_warm_ms": 288,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 660,
      "write_warm_ms": 556,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2998_ict_vacuum_timestamp_survive",
      "num": 2998,
      "name": "ict_vacuum_timestamp_survive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2998_ict_vacuum_timestamp_survive.sql",
      "read_script": "generator/spark-reads-df/verify_2998_ict_vacuum_timestamp_survive.py",
      "description": "In-Commit Timestamp (ICT) metadata survives VACUUM.",
      "status": "pass",
      "duration_ms": 2146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:50:59.349580+00:00",
      "read_cold_ms": 1103,
      "read_warm_ms": 396,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 250,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/2999_ict_vacuum_multi_version",
      "num": 2999,
      "name": "ict_vacuum_multi_version",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/2999_ict_vacuum_multi_version.sql",
      "read_script": "generator/spark-reads-df/verify_2999_ict_vacuum_multi_version.py",
      "description": "ICT across many versions survives VACUUM. Ten separate INSERT",
      "status": "pass",
      "duration_ms": 2042,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-14T15:51:01.392591+00:00",
      "read_cold_ms": 1262,
      "read_warm_ms": 278,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1072,
      "write_warm_ms": 1014,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/299_delete_cdc",
      "num": 299,
      "name": "delete_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/299_delete_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_299_delete_cdc.py",
      "description": "DELETE with CDC (Change Data Capture) enabled - captures pre-images",
      "status": "pass",
      "duration_ms": 2747,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:49:56.331352+00:00",
      "read_cold_ms": 1720,
      "read_warm_ms": 504,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 38,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/29_column_mapping_id_mode_physical",
      "num": 29,
      "name": "column_mapping_id_mode_physical",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/29_column_mapping_id_mode_physical.sql",
      "read_script": "generator/spark-reads-df/verify_29_column_mapping_id_mode_physical.py",
      "description": "Demonstrates column mapping with ID mode (physical names). In ID mode, columns are referenced by unique IDs, allowing rename/reorder without rewriting data. Physical names in Parquet files remain unchanged.",
      "status": "pass",
      "duration_ms": 5211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:01.545639+00:00",
      "read_cold_ms": 2695,
      "read_warm_ms": 1380,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 406,
      "write_warm_ms": 274,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3000_rowtrack_vacuum_metadata",
      "num": 3000,
      "name": "rowtrack_vacuum_metadata",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3000_rowtrack_vacuum_metadata.sql",
      "read_script": "generator/spark-reads-df/verify_3000_rowtrack_vacuum_metadata.py",
      "description": "Row tracking metadata survives DELETE + VACUUM. After INSERT 80",
      "status": "pass",
      "duration_ms": 11755,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:50:51.969805+00:00",
      "read_cold_ms": 9437,
      "read_warm_ms": 701,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 247,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3001_rowtrack_vacuum_optimize",
      "num": 3001,
      "name": "rowtrack_vacuum_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3001_rowtrack_vacuum_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_3001_rowtrack_vacuum_optimize.py",
      "description": "Row tracking metadata survives OPTIMIZE + VACUUM. Five",
      "status": "pass",
      "duration_ms": 2740,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:50:54.710374+00:00",
      "read_cold_ms": 1503,
      "read_warm_ms": 366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 201,
      "write_warm_ms": 253,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:row-tracking",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3002_truncate_restore_version",
      "num": 3002,
      "name": "truncate_restore_version",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3002_truncate_restore_version.sql",
      "read_script": "generator/spark-reads-df/verify_3002_truncate_restore_version.py",
      "description": "TRUNCATE TABLE followed by RESTORE TO VERSION 1 brings back",
      "status": "pass",
      "duration_ms": 4263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:50:58.974282+00:00",
      "read_cold_ms": 1379,
      "read_warm_ms": 405,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3003_truncate_restore_multi_truncate",
      "num": 3003,
      "name": "truncate_restore_multi_truncate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3003_truncate_restore_multi_truncate.sql",
      "read_script": "generator/spark-reads-df/verify_3003_truncate_restore_multi_truncate.py",
      "description": "Multiple TRUNCATE operations followed by RESTORE TO VERSION 1.",
      "status": "pass",
      "duration_ms": 4107,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:03.081848+00:00",
      "read_cold_ms": 1412,
      "read_warm_ms": 682,
      "skip_feature": null,
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      "write_cold_ms": 203,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3004_default_colmap_literal",
      "num": 3004,
      "name": "default_colmap_literal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3004_default_colmap_literal.sql",
      "read_script": "generator/spark-reads-df/verify_3004_default_colmap_literal.py",
      "description": "DEFAULT literal value combined with column mapping in 'name' mode.",
      "status": "pass",
      "duration_ms": 2136,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:05.218408+00:00",
      "read_cold_ms": 1386,
      "read_warm_ms": 371,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3005_default_vacuum_after_evolve",
      "num": 3005,
      "name": "default_vacuum_after_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3005_default_vacuum_after_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_3005_default_vacuum_after_evolve.py",
      "description": "A column added via ALTER TABLE with DEFAULT 0 after initial",
      "status": "pass",
      "duration_ms": 2235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:07.454413+00:00",
      "read_cold_ms": 1349,
      "read_warm_ms": 258,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 234,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:vacuum",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3006_pushdown_dv_predicate",
      "num": 3006,
      "name": "pushdown_dv_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3006_pushdown_dv_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_3006_pushdown_dv_predicate.py",
      "description": "Predicate pushdown combined with deletion vectors. After",
      "status": "pass",
      "duration_ms": 3016,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:10.470718+00:00",
      "read_cold_ms": 1492,
      "read_warm_ms": 466,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 315,
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      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3007_pushdown_cdc_partition",
      "num": 3007,
      "name": "pushdown_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3007_pushdown_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3007_pushdown_cdc_partition.py",
      "description": "Predicate pushdown combined with CDC and partitioning.",
      "status": "pass",
      "duration_ms": 3079,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:13.550660+00:00",
      "read_cold_ms": 1374,
      "read_warm_ms": 451,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 197,
      "write_warm_ms": 136,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3008_conflict_cdc_write_write",
      "num": 3008,
      "name": "conflict_cdc_write_write",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3008_conflict_cdc_write_write.sql",
      "read_script": "generator/spark-reads-df/verify_3008_conflict_cdc_write_write.py",
      "description": "Two sequential UPDATE operations with overlapping row ranges",
      "status": "pass",
      "duration_ms": 2147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:15.698200+00:00",
      "read_cold_ms": 1218,
      "read_warm_ms": 459,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 299,
      "write_warm_ms": 209,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3009_colmap_restore_name_mode",
      "num": 3009,
      "name": "colmap_restore_name_mode",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3009_colmap_restore_name_mode.sql",
      "read_script": "generator/spark-reads-df/verify_3009_colmap_restore_name_mode.py",
      "description": "Column mapping (name mode) combined with UPDATE then RESTORE",
      "status": "pass",
      "duration_ms": 2761,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:18.460150+00:00",
      "read_cold_ms": 1002,
      "read_warm_ms": 200,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 276,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/300_update_cdc",
      "num": 300,
      "name": "update_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/300_update_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_300_update_cdc.py",
      "description": "UPDATE with CDC (Change Data Capture) - captures pre/post images",
      "status": "pass",
      "duration_ms": 2579,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:04.126389+00:00",
      "read_cold_ms": 1694,
      "read_warm_ms": 265,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 16,
      "write_warm_ms": 40,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3010_default_constraint_check_default",
      "num": 3010,
      "name": "default_constraint_check_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3010_default_constraint_check_default.sql",
      "read_script": "generator/spark-reads-df/verify_3010_default_constraint_check_default.py",
      "description": "DEFAULT value combined with CHECK constraint on the same column.",
      "status": "pass",
      "duration_ms": 1319,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:19.780149+00:00",
      "read_cold_ms": 828,
      "read_warm_ms": 269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 204,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3011_merge_three_clause_all_types",
      "num": 3011,
      "name": "merge_three_clause_all_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3011_merge_three_clause_all_types.sql",
      "read_script": "generator/spark-reads-df/verify_3011_merge_three_clause_all_types.py",
      "description": "MERGE with three WHEN clauses covering all Delta data types: WHEN MATCHED AND id<=20 THEN UPDATE, WHEN MATCHED AND id<=30 THEN DELETE, WHEN NOT MATCHED THEN INSERT. Source overlaps partially with target.",
      "status": "pass",
      "duration_ms": 2093,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:21.874021+00:00",
      "read_cold_ms": 1387,
      "read_warm_ms": 395,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 126,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3012_merge_update_all_rows",
      "num": 3012,
      "name": "merge_update_all_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3012_merge_update_all_rows.sql",
      "read_script": "generator/spark-reads-df/verify_3012_merge_update_all_rows.py",
      "description": "MERGE where every source row matches a target row and triggers an UPDATE. All 50 rows updated from source values, no inserts or deletes.",
      "status": "pass",
      "duration_ms": 1749,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:23.623795+00:00",
      "read_cold_ms": 1106,
      "read_warm_ms": 353,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3013_merge_cdc_colmap_three_clause",
      "num": 3013,
      "name": "merge_cdc_colmap_three_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3013_merge_cdc_colmap_three_clause.sql",
      "read_script": "generator/spark-reads-df/verify_3013_merge_cdc_colmap_three_clause.py",
      "description": "MERGE with three WHEN clauses on a table with CDC and column mapping enabled. Verifies that CDF records all three change types (update_preimage, update_postimage, delete, insert) and that logical column names remain intact.",
      "status": "pass",
      "duration_ms": 1570,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:25.194418+00:00",
      "read_cold_ms": 987,
      "read_warm_ms": 289,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 295,
      "write_warm_ms": 243,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3014_merge_identity_hwm_continue",
      "num": 3014,
      "name": "merge_identity_hwm_continue",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3014_merge_identity_hwm_continue.sql",
      "read_script": "generator/spark-reads-df/verify_3014_merge_identity_hwm_continue.py",
      "description": "MERGE inserts new rows into a table with a GENERATED BY DEFAULT AS IDENTITY column. Verifies the high-watermark continues correctly after the merge (HWM >= 50).",
      "status": "pass",
      "duration_ms": 1331,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:26.526433+00:00",
      "read_cold_ms": 766,
      "read_warm_ms": 338,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 76,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3015_merge_identity_update_no_change",
      "num": 3015,
      "name": "merge_identity_update_no_change",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3015_merge_identity_update_no_change.sql",
      "read_script": "generator/spark-reads-df/verify_3015_merge_identity_update_no_change.py",
      "description": "MERGE updates a non-identity column on a table with a GENERATED BY DEFAULT AS IDENTITY primary key. Verifies identity column values are unchanged after UPDATE.",
      "status": "pass",
      "duration_ms": 1698,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:28.225327+00:00",
      "read_cold_ms": 1030,
      "read_warm_ms": 292,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 102,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3016_merge_default_not_matched",
      "num": 3016,
      "name": "merge_default_not_matched",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3016_merge_default_not_matched.sql",
      "read_script": "generator/spark-reads-df/verify_3016_merge_default_not_matched.py",
      "description": "MERGE inserts rows via NOT MATCHED clause omitting the DEFAULT column (status). Rows inserted through MERGE should receive the column default ('pending'). Pre-existing rows retain status='active'.",
      "status": "pass",
      "duration_ms": 1312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:29.538160+00:00",
      "read_cold_ms": 883,
      "read_warm_ms": 199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3017_merge_constraint_check",
      "num": 3017,
      "name": "merge_constraint_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3017_merge_constraint_check.sql",
      "read_script": "generator/spark-reads-df/verify_3017_merge_constraint_check.py",
      "description": "MERGE on a table with a CHECK constraint. All source values satisfy the constraint. Verifies the constraint metadata is present in the Delta log and all merged values pass the check (score >= 0).",
      "status": "pass",
      "duration_ms": 1727,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:31.265606+00:00",
      "read_cold_ms": 978,
      "read_warm_ms": 368,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 260,
      "write_warm_ms": 291,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3018_merge_partition_cross_partition",
      "num": 3018,
      "name": "merge_partition_cross_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3018_merge_partition_cross_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3018_merge_partition_cross_partition.py",
      "description": "MERGE updates a partition key column, causing rows to move between partitions. Initial data is split across US/EU/APAC. After MERGE the first 20 rows move to LATAM.",
      "status": "pass",
      "duration_ms": 1651,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:32.916875+00:00",
      "read_cold_ms": 1008,
      "read_warm_ms": 342,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 258,
      "write_warm_ms": 110,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3019_merge_flat_struct_update",
      "num": 3019,
      "name": "merge_flat_struct_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3019_merge_flat_struct_update.sql",
      "read_script": "generator/spark-reads-df/verify_3019_merge_flat_struct_update.py",
      "description": "MERGE updates a subset of flat columns that represent \"nested\" info fields. info_name and info_score are unchanged for ids 21-40.",
      "status": "pass",
      "duration_ms": 1727,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:34.645014+00:00",
      "read_cold_ms": 1070,
      "read_warm_ms": 380,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 153,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/301_delete_partition",
      "num": 301,
      "name": "delete_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/301_delete_partition.sql",
      "read_script": "generator/spark-reads-df/verify_301_delete_partition.py",
      "description": "DELETE with partition pruning",
      "status": "pass",
      "duration_ms": 3431,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:07.558702+00:00",
      "read_cold_ms": 1225,
      "read_warm_ms": 620,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 76,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3020_merge_ten_versions",
      "num": 3020,
      "name": "merge_ten_versions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3020_merge_ten_versions.sql",
      "read_script": "generator/spark-reads-df/verify_3020_merge_ten_versions.py",
      "description": "Ten successive MERGE operations each updating all 50 rows. After 10 rounds, val = sum(1..10) = 55 and round = 10. Verifies correctness across many Delta versions.",
      "status": "pass",
      "duration_ms": 1981,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:36.626377+00:00",
      "read_cold_ms": 1225,
      "read_warm_ms": 341,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1179,
      "write_warm_ms": 991,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3021_merge_large_1000_rows",
      "num": 3021,
      "name": "merge_large_1000_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3021_merge_large_1000_rows.sql",
      "read_script": "generator/spark-reads-df/verify_3021_merge_large_1000_rows.py",
      "description": "MERGE on a 1000-row table. Source updates 500 existing rows, inserts 500 new rows.",
      "status": "pass",
      "duration_ms": 1602,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:38.228744+00:00",
      "read_cold_ms": 956,
      "read_warm_ms": 283,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3022_merge_delete_all_reinsert",
      "num": 3022,
      "name": "merge_delete_all_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3022_merge_delete_all_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_3022_merge_delete_all_reinsert.py",
      "description": "MERGE that deletes every existing row (ids 1-40) and inserts entirely new rows (ids 41-80) in a single operation. Validates that the table is completely replaced.",
      "status": "pass",
      "duration_ms": 1503,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:39.732806+00:00",
      "read_cold_ms": 937,
      "read_warm_ms": 274,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 141,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3023_merge_gencol_auto_compute",
      "num": 3023,
      "name": "merge_gencol_auto_compute",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3023_merge_gencol_auto_compute.sql",
      "read_script": "generator/spark-reads-df/verify_3023_merge_gencol_auto_compute.py",
      "description": "MERGE on a table with a GENERATED ALWAYS AS column (total = price * qty). MERGE updates price for ids 1-15 and inserts ids 31-40. The generated column must auto-recompute for all affected rows.",
      "status": "pass",
      "duration_ms": 1743,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:41.476566+00:00",
      "read_cold_ms": 1113,
      "read_warm_ms": 352,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3024_merge_evolve_add_col_then_merge",
      "num": 3024,
      "name": "merge_evolve_add_col_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3024_merge_evolve_add_col_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3024_merge_evolve_add_col_then_merge.py",
      "description": "MERGE updates ids 1-20 with a score value, inserts ids 41-55 with score. Ids 21-40 survive with score=NULL (pre-evolution rows).",
      "status": "pass",
      "duration_ms": 2376,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:43.853683+00:00",
      "read_cold_ms": 1300,
      "read_warm_ms": 409,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 233,
      "write_warm_ms": 78,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3025_merge_widen_then_merge_bigint",
      "num": 3025,
      "name": "merge_widen_then_merge_bigint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3025_merge_widen_then_merge_bigint.sql",
      "read_script": "generator/spark-reads-df/verify_3025_merge_widen_then_merge_bigint.py",
      "description": "then MERGE with values exceeding INT range (3000000000).",
      "status": "pass",
      "duration_ms": 2751,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:46.605645+00:00",
      "read_cold_ms": 1822,
      "read_warm_ms": 450,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3026_merge_cdc_dv_partition_quad",
      "num": 3026,
      "name": "merge_cdc_dv_partition_quad",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3026_merge_cdc_dv_partition_quad.sql",
      "read_script": "generator/spark-reads-df/verify_3026_merge_cdc_dv_partition_quad.py",
      "description": "MERGE on a partitioned table (4 regions) with CDC and Deletion Vectors enabled. Verifies CDF contains all three change types across partitions.",
      "status": "pass",
      "duration_ms": 2592,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:49.198723+00:00",
      "read_cold_ms": 1649,
      "read_warm_ms": 328,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 227,
      "write_warm_ms": 185,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3027_merge_optimize_then_merge",
      "num": 3027,
      "name": "merge_optimize_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3027_merge_optimize_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3027_merge_optimize_then_merge.py",
      "description": "Two MERGE operations with an OPTIMIZE compaction between them. Verifies that compacted files are transparently read/written by the second MERGE.",
      "status": "pass",
      "duration_ms": 3185,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:52.384334+00:00",
      "read_cold_ms": 1660,
      "read_warm_ms": 672,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 387,
      "write_warm_ms": 499,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3028_merge_vacuum_then_merge",
      "num": 3028,
      "name": "merge_vacuum_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3028_merge_vacuum_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3028_merge_vacuum_then_merge.py",
      "description": "DELETE rows, VACUUM to remove old files, then MERGE to verify the table is still fully writable after vacuum. Final state has 50 rows (30 updated + 20 new).",
      "status": "pass",
      "duration_ms": 1815,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:54.200466+00:00",
      "read_cold_ms": 1195,
      "read_warm_ms": 275,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 220,
      "write_warm_ms": 109,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3029_merge_colmap_cdc_evolve_quad",
      "num": 3029,
      "name": "merge_colmap_cdc_evolve_quad",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3029_merge_colmap_cdc_evolve_quad.sql",
      "read_script": "generator/spark-reads-df/verify_3029_merge_colmap_cdc_evolve_quad.py",
      "description": "INSERT 40 rows, ADD COLUMN score INT, then MERGE updating ids 1-20 and inserting ids 41-55. Verifies all four features interact correctly.",
      "status": "pass",
      "duration_ms": 1841,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:56.042473+00:00",
      "read_cold_ms": 1007,
      "read_warm_ms": 406,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 274,
      "write_warm_ms": 301,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/302_delete_large",
      "num": 302,
      "name": "delete_large",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/302_delete_large.sql",
      "read_script": "generator/spark-reads-df/verify_302_delete_large.py",
      "description": "DELETE removing majority of data (90%)",
      "status": "pass",
      "duration_ms": 3990,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:11.550364+00:00",
      "read_cold_ms": 2588,
      "read_warm_ms": 555,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 19,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3030_merge_ict_rowtrack",
      "num": 3030,
      "name": "merge_ict_rowtrack",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3030_merge_ict_rowtrack.sql",
      "read_script": "generator/spark-reads-df/verify_3030_merge_ict_rowtrack.py",
      "description": "MERGE on a table with In-Commit Timestamps and Row Tracking enabled. Verifies both protocol features are recorded in the Delta log and that the MERGE result is correct.",
      "status": "pass",
      "duration_ms": 2024,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:51:58.067049+00:00",
      "read_cold_ms": 1248,
      "read_warm_ms": 350,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 183,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3031_merge_string_partition_key",
      "num": 3031,
      "name": "merge_string_partition_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3031_merge_string_partition_key.sql",
      "read_script": "generator/spark-reads-df/verify_3031_merge_string_partition_key.py",
      "description": "MERGE on a table partitioned by a STRING date column. Rows span 28 distinct partition values (2024-01-01 through 2024-01-28). MERGE updates val for ids 1-30; partition dirs are verified post-merge.",
      "status": "pass",
      "duration_ms": 4592,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:52:02.662006+00:00",
      "read_cold_ms": 2709,
      "read_warm_ms": 941,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 707,
      "write_warm_ms": 733,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3032_merge_decimal_38_18_precision",
      "num": 3032,
      "name": "merge_decimal_38_18_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3032_merge_decimal_38_18_precision.sql",
      "read_script": "generator/spark-reads-df/verify_3032_merge_decimal_38_18_precision.py",
      "description": "MERGE on a table with DECIMAL(38,18) amounts. Update doubles the amount for ids 1-15; insert ids 31-40 with new decimals. Verifies full decimal precision is preserved through MERGE.",
      "status": "pass",
      "duration_ms": 4299,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:52:06.961614+00:00",
      "read_cold_ms": 2569,
      "read_warm_ms": 1047,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 194,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3033_merge_boolean_predicate",
      "num": 3033,
      "name": "merge_boolean_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3033_merge_boolean_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_3033_merge_boolean_predicate.py",
      "description": "MERGE with WHEN MATCHED conditions that branch on a BOOLEAN column. Active rows (is_active=true) get score+=999; inactive rows get score=0.",
      "status": "pass",
      "duration_ms": 4368,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:52:11.330836+00:00",
      "read_cold_ms": 2643,
      "read_warm_ms": 986,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 242,
      "write_warm_ms": 348,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3034_merge_null_handling_all_columns",
      "num": 3034,
      "name": "merge_null_handling_all_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3034_merge_null_handling_all_columns.sql",
      "read_script": "generator/spark-reads-df/verify_3034_merge_null_handling_all_columns.py",
      "description": "MERGE that deliberately sets columns to NULL via UPDATE and inserts rows where optional columns are NULL. Verifies NULL propagation is correct.",
      "status": "pass",
      "duration_ms": 3222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:52:14.553975+00:00",
      "read_cold_ms": 1915,
      "read_warm_ms": 675,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 187,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3035_merge_string_special_chars",
      "num": 3035,
      "name": "merge_string_special_chars",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3035_merge_string_special_chars.sql",
      "read_script": "generator/spark-reads-df/verify_3035_merge_string_special_chars.py",
      "description": "MERGE that updates string columns with special characters (quotes, spaces, slashes). Verifies Parquet string encoding handles non-alphanumeric values correctly.",
      "status": "pass",
      "duration_ms": 3820,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:52:18.375537+00:00",
      "read_cold_ms": 2187,
      "read_warm_ms": 830,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 268,
      "write_warm_ms": 180,
      "tags": [
        "type:integer",
        "type:string",
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    {
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        "type:integer",
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      "manual_tags": [],
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3052_maint_optimize_struct_table",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3052_maint_optimize_struct_table.sql",
      "read_script": "generator/spark-reads-df/verify_3052_maint_optimize_struct_table.py",
      "description": "OPTIMIZE on a table with multiple flat columns (simulating struct-like",
      "status": "pass",
      "duration_ms": 2825,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:06.739809+00:00",
      "read_cold_ms": 1713,
      "read_warm_ms": 459,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 551,
      "write_warm_ms": 440,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3053_maint_vacuum_partition_prune",
      "num": 3053,
      "name": "maint_vacuum_partition_prune",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3053_maint_vacuum_partition_prune.sql",
      "read_script": "generator/spark-reads-df/verify_3053_maint_vacuum_partition_prune.py",
      "description": "VACUUM after deleting entire partitions (LATAM and MEA) removes",
      "status": "pass",
      "duration_ms": 2699,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:09.441005+00:00",
      "read_cold_ms": 1720,
      "read_warm_ms": 321,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
      "write_warm_ms": 332,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3054_maint_optimize_map_columns",
      "num": 3054,
      "name": "maint_optimize_map_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3054_maint_optimize_map_columns.sql",
      "read_script": "generator/spark-reads-df/verify_3054_maint_optimize_map_columns.py",
      "description": "OPTIMIZE on a table with key-value style columns (label + score patterns",
      "status": "pass",
      "duration_ms": 2318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:11.759951+00:00",
      "read_cold_ms": 1609,
      "read_warm_ms": 431,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 483,
      "write_warm_ms": 212,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3055_maint_vacuum_dv_cleanup",
      "num": 3055,
      "name": "maint_vacuum_dv_cleanup",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3055_maint_vacuum_dv_cleanup.sql",
      "read_script": "generator/spark-reads-df/verify_3055_maint_vacuum_dv_cleanup.py",
      "description": "Full DV lifecycle -- DELETE via DVs, OPTIMIZE applies DVs (rewrites without",
      "status": "pass",
      "duration_ms": 3379,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:15.140413+00:00",
      "read_cold_ms": 1397,
      "read_warm_ms": 791,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 212,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3056_cdc_merge_three_clause_cdf",
      "num": 3056,
      "name": "cdc_merge_three_clause_cdf",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3056_cdc_merge_three_clause_cdf.sql",
      "read_script": "generator/spark-reads-df/verify_3056_cdc_merge_three_clause_cdf.py",
      "description": "MERGE with three WHEN clauses on a CDC+DV table. ids 1-20 matched and UPDATED, ids 21-30 matched and DELETED, ids 61-70 not matched and INSERTED.",
      "status": "pass",
      "duration_ms": 4393,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:19.534316+00:00",
      "read_cold_ms": 2344,
      "read_warm_ms": 937,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 316,
      "write_warm_ms": 185,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3057_cdc_truncate_cdf_record",
      "num": 3057,
      "name": "cdc_truncate_cdf_record",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3057_cdc_truncate_cdf_record.sql",
      "read_script": "generator/spark-reads-df/verify_3057_cdc_truncate_cdf_record.py",
      "description": "INSERT 50 rows then TRUNCATE TABLE. Verifies CDF records the truncate/delete event. After truncate, no rows remain. CDF should have 50 inserts plus truncate records.",
      "status": "pass",
      "duration_ms": 3480,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:23.015240+00:00",
      "read_cold_ms": 1624,
      "read_warm_ms": 622,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 201,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3058_cdc_schema_evolve_cdf_columns",
      "num": 3058,
      "name": "cdc_schema_evolve_cdf_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3058_cdc_schema_evolve_cdf_columns.sql",
      "read_script": "generator/spark-reads-df/verify_3058_cdc_schema_evolve_cdf_columns.py",
      "description": "CDC table with schema evolution. INSERT 30 rows, ADD COLUMN score INT, INSERT 20 more rows with score, UPDATE first 10 rows to set score=999. CDF must contain all change types including records with the new score column.",
      "status": "pass",
      "duration_ms": 4330,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:27.346199+00:00",
      "read_cold_ms": 2367,
      "read_warm_ms": 912,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 524,
      "write_warm_ms": 116,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3059_cdc_delete_all_cdf_complete",
      "num": 3059,
      "name": "cdc_delete_all_cdf_complete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3059_cdc_delete_all_cdf_complete.sql",
      "read_script": "generator/spark-reads-df/verify_3059_cdc_delete_all_cdf_complete.py",
      "description": "INSERT 100 rows, then DELETE all rows. CDF must capture all 100 inserts and all 100 deletes. Final table has 0 rows.",
      "status": "pass",
      "duration_ms": 4102,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:31.449240+00:00",
      "read_cold_ms": 2043,
      "read_warm_ms": 504,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 227,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/305_zorder_comprehensive_basic",
      "num": 305,
      "name": "zorder_comprehensive_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/305_zorder_comprehensive_basic.sql",
      "read_script": "generator/spark-reads-df/verify_305_zorder_comprehensive_basic.py",
      "description": "Basic Z-ORDER coexistence test - DBX creates -> DeltaForge Z-ORDER -> DBX verifies",
      "status": "pass",
      "duration_ms": 3199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:20.704507+00:00",
      "read_cold_ms": 1656,
      "read_warm_ms": 604,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 301,
      "write_warm_ms": 392,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3060_cdc_update_same_row_10x",
      "num": 3060,
      "name": "cdc_update_same_row_10x",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3060_cdc_update_same_row_10x.sql",
      "read_script": "generator/spark-reads-df/verify_3060_cdc_update_same_row_10x.py",
      "description": "INSERT 1 row, then UPDATE the same row 10 times sequentially. Each update increments counter by 1 and updates the label. CDF must record 1 insert + 10 update_preimage + 10 update_postimage = 21 records.",
      "status": "pass",
      "duration_ms": 4625,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:36.075783+00:00",
      "read_cold_ms": 2499,
      "read_warm_ms": 805,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1170,
      "write_warm_ms": 1000,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3061_cdc_partition_cdf_per_partition",
      "num": 3061,
      "name": "cdc_partition_cdf_per_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3061_cdc_partition_cdf_per_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3061_cdc_partition_cdf_per_partition.py",
      "description": "Partitioned CDC table. INSERT 60 rows across 3 regions, DELETE all 'US' rows. CDF delete records must all have region='US'. Final: 40 rows (EU + APAC).",
      "status": "pass",
      "duration_ms": 2736,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:38.812963+00:00",
      "read_cold_ms": 1675,
      "read_warm_ms": 378,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 120,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3062_cdc_identity_cdf_id_tracking",
      "num": 3062,
      "name": "cdc_identity_cdf_id_tracking",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3062_cdc_identity_cdf_id_tracking.sql",
      "read_script": "generator/spark-reads-df/verify_3062_cdc_identity_cdf_id_tracking.py",
      "description": "Identity column with CDC. INSERT 30 rows, DELETE ids 1-10, INSERT 10 more. CDF must track auto-assigned identity IDs in insert/delete records.",
      "status": "pass",
      "duration_ms": 3045,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:41.858797+00:00",
      "read_cold_ms": 1751,
      "read_warm_ms": 589,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 312,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3063_cdc_optimize_cdf_survives",
      "num": 3063,
      "name": "cdc_optimize_cdf_survives",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3063_cdc_optimize_cdf_survives.sql",
      "read_script": "generator/spark-reads-df/verify_3063_cdc_optimize_cdf_survives.py",
      "description": "CDC table with UPDATE, DELETE, then OPTIMIZE. Verifies CDF is still readable after OPTIMIZE compacts files. OPTIMIZE must not corrupt or clear CDF records.",
      "status": "pass",
      "duration_ms": 2965,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:44.824155+00:00",
      "read_cold_ms": 1253,
      "read_warm_ms": 639,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 321,
      "write_warm_ms": 217,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3064_cdc_default_col_cdf",
      "num": 3064,
      "name": "cdc_default_col_cdf",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3064_cdc_default_col_cdf.sql",
      "read_script": "generator/spark-reads-df/verify_3064_cdc_default_col_cdf.py",
      "description": "CDC table with a DEFAULT column value. INSERT 30 rows omitting 'status' (defaults to 'new'). UPDATE first 10 rows to set status='processed'. CDF pre-images have status='new', post-images have status='processed'.",
      "status": "pass",
      "duration_ms": 3712,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:48.536712+00:00",
      "read_cold_ms": 2170,
      "read_warm_ms": 747,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 189,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3065_cdc_struct_column_changes",
      "num": 3065,
      "name": "cdc_struct_column_changes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3065_cdc_struct_column_changes.sql",
      "read_script": "generator/spark-reads-df/verify_3065_cdc_struct_column_changes.py",
      "description": "CDC table with flat columns simulating struct-like before/after change capture. UPDATE first 15 rows to modify detail_a and detail_b. CDF must capture full before/after for both detail columns.",
      "status": "pass",
      "duration_ms": 3425,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:51.962900+00:00",
      "read_cold_ms": 2227,
      "read_warm_ms": 563,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 67,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3066_cdc_colmap_cdf_logical_names",
      "num": 3066,
      "name": "cdc_colmap_cdf_logical_names",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3066_cdc_colmap_cdf_logical_names.sql",
      "read_script": "generator/spark-reads-df/verify_3066_cdc_colmap_cdf_logical_names.py",
      "description": "CDC table with column mapping mode=name. CDF records must expose logical column names (user_name, score) not physical UUIDs. UPDATE first 20 rows score += 500.",
      "status": "pass",
      "duration_ms": 3119,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:55.083126+00:00",
      "read_cold_ms": 1514,
      "read_warm_ms": 733,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3067_cdc_constraint_valid_merge",
      "num": 3067,
      "name": "cdc_constraint_valid_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3067_cdc_constraint_valid_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3067_cdc_constraint_valid_merge.py",
      "description": "CDC table with a CHECK constraint. All MERGE operations produce valid scores (>= 0). MERGE updates ids 1-15 (score += 100) and inserts ids 31-40 (score = (id-30)*10). Constraint must be respected throughout. CDF tracks all changes.",
      "status": "pass",
      "duration_ms": 2739,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:53:57.823147+00:00",
      "read_cold_ms": 1355,
      "read_warm_ms": 553,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 307,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3068_cdc_restore_cdf_history",
      "num": 3068,
      "name": "cdc_restore_cdf_history",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3068_cdc_restore_cdf_history.sql",
      "read_script": "generator/spark-reads-df/verify_3068_cdc_restore_cdf_history.py",
      "description": "CDC table with RESTORE. INSERT 30 rows (tag='v1'), UPDATE 10 rows (tag='v2'), then RESTORE to version 1. CDF history includes all operations including restore.",
      "status": "pass",
      "duration_ms": 2648,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:00.472464+00:00",
      "read_cold_ms": 1178,
      "read_warm_ms": 343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 129,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3069_cdc_widen_cdf_type_change",
      "num": 3069,
      "name": "cdc_widen_cdf_type_change",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3069_cdc_widen_cdf_type_change.sql",
      "read_script": "generator/spark-reads-df/verify_3069_cdc_widen_cdf_type_change.py",
      "description": "CDC table with type widening. INSERT 30 rows (val INT), ALTER val to BIGINT, UPDATE ids 1-10 setting val to large BIGINT values. CDF records post-widen updates.",
      "status": "pass",
      "duration_ms": 3131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:03.603835+00:00",
      "read_cold_ms": 1877,
      "read_warm_ms": 535,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 195,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/306_zorder_numeric_columns",
      "num": 306,
      "name": "zorder_numeric_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/306_zorder_numeric_columns.sql",
      "read_script": "generator/spark-reads-df/verify_306_zorder_numeric_columns.py",
      "description": "Z-ORDER on numeric columns (INT, BIGINT, DOUBLE, DECIMAL)",
      "status": "pass",
      "duration_ms": 3960,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:24.665346+00:00",
      "read_cold_ms": 2697,
      "read_warm_ms": 523,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 224,
      "write_warm_ms": 156,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3070_cdc_many_versions_50",
      "num": 3070,
      "name": "cdc_many_versions_50",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3070_cdc_many_versions_50.sql",
      "read_script": "generator/spark-reads-df/verify_3070_cdc_many_versions_50.py",
      "description": "CDC table with many versions. 10 separate INSERT batches of 10 rows each, batch column tracks which version each row came from. CDF has 100 insert records across 10 version batches.",
      "status": "pass",
      "duration_ms": 3545,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:07.149994+00:00",
      "read_cold_ms": 2093,
      "read_warm_ms": 522,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 371,
      "write_warm_ms": 1033,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3071_cdc_dv_delete_cdf_correct",
      "num": 3071,
      "name": "cdc_dv_delete_cdf_correct",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3071_cdc_dv_delete_cdf_correct.sql",
      "read_script": "generator/spark-reads-df/verify_3071_cdc_dv_delete_cdf_correct.py",
      "description": "Deletion Vectors + CDC. INSERT 60 rows, DELETE ids 1-20 (via DV). CDF must correctly record the 20 deletions. Deletion vectors mark rows as deleted without rewriting data files. Final: 40 rows.",
      "status": "pass",
      "duration_ms": 2826,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:09.978209+00:00",
      "read_cold_ms": 1646,
      "read_warm_ms": 473,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 199,
      "write_warm_ms": 299,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3072_cdc_map_column_update",
      "num": 3072,
      "name": "cdc_map_column_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3072_cdc_map_column_update.sql",
      "read_script": "generator/spark-reads-df/verify_3072_cdc_map_column_update.py",
      "description": "CDC table simulating key-value update patterns with label+score+extra columns. UPDATE first 10 rows changing score and extra. CDF captures pre/post images with old and new values for all modified columns.",
      "status": "pass",
      "duration_ms": 2955,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:12.934024+00:00",
      "read_cold_ms": 1836,
      "read_warm_ms": 460,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 134,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3073_cdc_gencol_cdf_computed",
      "num": 3073,
      "name": "cdc_gencol_cdf_computed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3073_cdc_gencol_cdf_computed.sql",
      "read_script": "generator/spark-reads-df/verify_3073_cdc_gencol_cdf_computed.py",
      "description": "CDC table with a GENERATED ALWAYS AS computed column (total = price * qty). UPDATE first 15 rows to increment price by 100. CDF post-images must have recomputed total = (price+100)*qty. Verifies generated columns in CDF records.",
      "status": "pass",
      "duration_ms": 3097,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:16.031575+00:00",
      "read_cold_ms": 1685,
      "read_warm_ms": 492,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 88,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3074_evolve_drop_col_then_merge",
      "num": 3074,
      "name": "evolve_drop_col_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3074_evolve_drop_col_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3074_evolve_drop_col_then_merge.py",
      "description": "DROP a column then run MERGE; verify absent column and merge correctness",
      "status": "pass",
      "duration_ms": 2755,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:18.787444+00:00",
      "read_cold_ms": 1743,
      "read_warm_ms": 473,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 127,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3075_evolve_rename_col_then_merge",
      "num": 3075,
      "name": "evolve_rename_col_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3075_evolve_rename_col_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3075_evolve_rename_col_then_merge.py",
      "description": "RENAME a column then run MERGE; verify renamed column and merge correctness",
      "status": "pass",
      "duration_ms": 3072,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:21.860608+00:00",
      "read_cold_ms": 1668,
      "read_warm_ms": 452,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 179,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3076_evolve_add_three_cols_sequential",
      "num": 3076,
      "name": "evolve_add_three_cols_sequential",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3076_evolve_add_three_cols_sequential.sql",
      "read_script": "generator/spark-reads-df/verify_3076_evolve_add_three_cols_sequential.py",
      "description": "ADD three columns one at a time; verify NULL backfill patterns per batch",
      "status": "pass",
      "duration_ms": 2424,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:24.285805+00:00",
      "read_cold_ms": 1587,
      "read_warm_ms": 390,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 426,
      "write_warm_ms": 301,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3077_evolve_drop_add_same_name",
      "num": 3077,
      "name": "evolve_drop_add_same_name",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3077_evolve_drop_add_same_name.sql",
      "read_script": "generator/spark-reads-df/verify_3077_evolve_drop_add_same_name.py",
      "description": "DROP column x then ADD COLUMN x with different type (INT -> STRING)",
      "status": "pass",
      "duration_ms": 2556,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:26.843510+00:00",
      "read_cold_ms": 1411,
      "read_warm_ms": 555,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 186,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3078_evolve_add_col_with_cdc_colmap",
      "num": 3078,
      "name": "evolve_add_col_with_cdc_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3078_evolve_add_col_with_cdc_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_3078_evolve_add_col_with_cdc_colmap.py",
      "description": "ADD COLUMN with CDC + colmap enabled; verify CDF sees new column",
      "status": "pass",
      "duration_ms": 3857,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:30.702099+00:00",
      "read_cold_ms": 1951,
      "read_warm_ms": 781,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 239,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3079_evolve_add_not_null_col_default",
      "num": 3079,
      "name": "evolve_add_not_null_col_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3079_evolve_add_not_null_col_default.sql",
      "read_script": "generator/spark-reads-df/verify_3079_evolve_add_not_null_col_default.py",
      "description": "ADD COLUMN with DEFAULT value after existing rows; verify NULL vs default behaviour",
      "status": "pass",
      "duration_ms": 3240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:33.942977+00:00",
      "read_cold_ms": 2035,
      "read_warm_ms": 606,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 276,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/307_zorder_string_columns",
      "num": 307,
      "name": "zorder_string_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/307_zorder_string_columns.sql",
      "read_script": "generator/spark-reads-df/verify_307_zorder_string_columns.py",
      "description": "Z-ORDER on STRING columns with varying cardinality",
      "status": "pass",
      "duration_ms": 2621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:27.287216+00:00",
      "read_cold_ms": 1363,
      "read_warm_ms": 357,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 606,
      "write_warm_ms": 895,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3080_evolve_add_struct_column",
      "num": 3080,
      "name": "evolve_add_struct_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3080_evolve_add_struct_column.sql",
      "read_script": "generator/spark-reads-df/verify_3080_evolve_add_struct_column.py",
      "description": "ADD two detail columns simulating struct field expansion; verify NULL backfill",
      "status": "pass",
      "duration_ms": 2882,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:36.825788+00:00",
      "read_cold_ms": 1727,
      "read_warm_ms": 373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 236,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3081_evolve_add_array_column",
      "num": 3081,
      "name": "evolve_add_array_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3081_evolve_add_array_column.sql",
      "read_script": "generator/spark-reads-df/verify_3081_evolve_add_array_column.py",
      "description": "ADD a STRING column (simulating array/list) after existing rows; verify NULL backfill",
      "status": "pass",
      "duration_ms": 3078,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:39.905095+00:00",
      "read_cold_ms": 1872,
      "read_warm_ms": 604,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3082_evolve_add_map_column",
      "num": 3082,
      "name": "evolve_add_map_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3082_evolve_add_map_column.sql",
      "read_script": "generator/spark-reads-df/verify_3082_evolve_add_map_column.py",
      "description": "ADD a STRING column (simulating map/kv) after existing rows; verify NULL backfill",
      "status": "pass",
      "duration_ms": 2568,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:42.474679+00:00",
      "read_cold_ms": 1541,
      "read_warm_ms": 465,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3083_evolve_multiple_renames",
      "num": 3083,
      "name": "evolve_multiple_renames",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3083_evolve_multiple_renames.sql",
      "read_script": "generator/spark-reads-df/verify_3083_evolve_multiple_renames.py",
      "description": "RENAME the same column twice (col_a -> col_b -> col_c); verify final name",
      "status": "pass",
      "duration_ms": 3148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:45.623769+00:00",
      "read_cold_ms": 1916,
      "read_warm_ms": 656,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 372,
      "write_warm_ms": 501,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3084_evolve_widen_then_add_col",
      "num": 3084,
      "name": "evolve_widen_then_add_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3084_evolve_widen_then_add_col.sql",
      "read_script": "generator/spark-reads-df/verify_3084_evolve_widen_then_add_col.py",
      "description": "Widen INT column to BIGINT via type widening, then ADD COLUMN label STRING",
      "status": "pass",
      "duration_ms": 2234,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:47.859702+00:00",
      "read_cold_ms": 1367,
      "read_warm_ms": 305,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 157,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3085_evolve_colmap_rename_then_query",
      "num": 3085,
      "name": "evolve_colmap_rename_then_query",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3085_evolve_colmap_rename_then_query.sql",
      "read_script": "generator/spark-reads-df/verify_3085_evolve_colmap_rename_then_query.py",
      "description": "RENAME column with colmap=name; verify logical name propagation in reads",
      "status": "pass",
      "duration_ms": 3024,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:50.886185+00:00",
      "read_cold_ms": 1878,
      "read_warm_ms": 541,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 167,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3086_evolve_constraint_after_add_col",
      "num": 3086,
      "name": "evolve_constraint_after_add_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3086_evolve_constraint_after_add_col.sql",
      "read_script": "generator/spark-reads-df/verify_3086_evolve_constraint_after_add_col.py",
      "description": "ADD COLUMN then ADD CONSTRAINT CHECK on that column; verify constraint in log",
      "status": "pass",
      "duration_ms": 2347,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:53.233682+00:00",
      "read_cold_ms": 1635,
      "read_warm_ms": 342,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 186,
      "write_warm_ms": 78,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3087_evolve_identity_add_col",
      "num": 3087,
      "name": "evolve_identity_add_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3087_evolve_identity_add_col.sql",
      "read_script": "generator/spark-reads-df/verify_3087_evolve_identity_add_col.py",
      "description": "IDENTITY column auto-increments; ADD COLUMN score after existing rows",
      "status": "pass",
      "duration_ms": 2460,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:55.694914+00:00",
      "read_cold_ms": 1427,
      "read_warm_ms": 346,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 118,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3088_evolve_five_operations_sequence",
      "num": 3088,
      "name": "evolve_five_operations_sequence",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3088_evolve_five_operations_sequence.sql",
      "read_script": "generator/spark-reads-df/verify_3088_evolve_five_operations_sequence.py",
      "description": null,
      "status": "pass",
      "duration_ms": 3558,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:54:59.253893+00:00",
      "read_cold_ms": 2407,
      "read_warm_ms": 535,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 809,
      "write_warm_ms": 488,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3089_partition_null_key_merge",
      "num": 3089,
      "name": "partition_null_key_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3089_partition_null_key_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3089_partition_null_key_merge.py",
      "description": "Partitioned table with NULL partition key values.",
      "status": "pass",
      "duration_ms": 4008,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:03.262534+00:00",
      "read_cold_ms": 2357,
      "read_warm_ms": 891,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/308_zorder_datetime_columns",
      "num": 308,
      "name": "zorder_datetime_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/308_zorder_datetime_columns.sql",
      "read_script": "generator/spark-reads-df/verify_308_zorder_datetime_columns.py",
      "description": "Z-ORDER on DATE and TIMESTAMP columns",
      "status": "pass",
      "duration_ms": 3496,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:30.784493+00:00",
      "read_cold_ms": 2403,
      "read_warm_ms": 493,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 166,
      "write_warm_ms": 148,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3090_partition_high_cardinality_50",
      "num": 3090,
      "name": "partition_high_cardinality_50",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3090_partition_high_cardinality_50.sql",
      "read_script": "generator/spark-reads-df/verify_3090_partition_high_cardinality_50.py",
      "description": "High-cardinality partitioned table with 50 distinct",
      "status": "pass",
      "duration_ms": 2406,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:05.669450+00:00",
      "read_cold_ms": 1664,
      "read_warm_ms": 359,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 636,
      "write_warm_ms": 558,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3091_partition_special_chars_key",
      "num": 3091,
      "name": "partition_special_chars_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3091_partition_special_chars_key.sql",
      "read_script": "generator/spark-reads-df/verify_3091_partition_special_chars_key.py",
      "description": "Partitioned table with special characters in the",
      "status": "pass",
      "duration_ms": 4587,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:10.257709+00:00",
      "read_cold_ms": 2094,
      "read_warm_ms": 507,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 121,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:partition-spec",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3092_partition_boolean_key",
      "num": 3092,
      "name": "partition_boolean_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3092_partition_boolean_key.sql",
      "read_script": "generator/spark-reads-df/verify_3092_partition_boolean_key.py",
      "description": "BOOLEAN partition key. Delta encodes boolean partitions",
      "status": "pass",
      "duration_ms": 4516,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:14.777210+00:00",
      "read_cold_ms": 1985,
      "read_warm_ms": 535,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 46,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3093_partition_date_key_daily",
      "num": 3093,
      "name": "partition_date_key_daily",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3093_partition_date_key_daily.sql",
      "read_script": "generator/spark-reads-df/verify_3093_partition_date_key_daily.py",
      "description": "Date-string partition key with daily granularity.",
      "status": "pass",
      "duration_ms": 3295,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:18.074083+00:00",
      "read_cold_ms": 1965,
      "read_warm_ms": 770,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 314,
      "write_warm_ms": 409,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3094_partition_timestamp_key",
      "num": 3094,
      "name": "partition_timestamp_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3094_partition_timestamp_key.sql",
      "read_script": "generator/spark-reads-df/verify_3094_partition_timestamp_key.py",
      "description": "Timestamp-string partition key (hourly granularity).",
      "status": "pass",
      "duration_ms": 3151,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:21.226829+00:00",
      "read_cold_ms": 1906,
      "read_warm_ms": 595,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 233,
      "write_warm_ms": 249,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3095_partition_optimize_per_partition",
      "num": 3095,
      "name": "partition_optimize_per_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3095_partition_optimize_per_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3095_partition_optimize_per_partition.py",
      "description": "OPTIMIZE on a partitioned table with multiple small files",
      "status": "pass",
      "duration_ms": 2995,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:24.223362+00:00",
      "read_cold_ms": 1892,
      "read_warm_ms": 419,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 534,
      "write_warm_ms": 780,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3096_partition_vacuum_selective",
      "num": 3096,
      "name": "partition_vacuum_selective",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3096_partition_vacuum_selective.sql",
      "read_script": "generator/spark-reads-df/verify_3096_partition_vacuum_selective.py",
      "description": "VACUUM after selective DELETE removes a partition's",
      "status": "pass",
      "duration_ms": 2966,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:27.190085+00:00",
      "read_cold_ms": 1842,
      "read_warm_ms": 523,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 121,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3097_partition_cdc_per_partition_cdf",
      "num": 3097,
      "name": "partition_cdc_per_partition_cdf",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3097_partition_cdc_per_partition_cdf.sql",
      "read_script": "generator/spark-reads-df/verify_3097_partition_cdc_per_partition_cdf.py",
      "description": "CDC (Change Data Feed) on a partitioned table.",
      "status": "pass",
      "duration_ms": 4395,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:31.586159+00:00",
      "read_cold_ms": 2425,
      "read_warm_ms": 868,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3098_partition_identity_per_partition",
      "num": 3098,
      "name": "partition_identity_per_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3098_partition_identity_per_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3098_partition_identity_per_partition.py",
      "description": "IDENTITY column on a partitioned table. Three separate",
      "status": "pass",
      "duration_ms": 3019,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:34.606263+00:00",
      "read_cold_ms": 1765,
      "read_warm_ms": 607,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 103,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3099_partition_colmap_directory_names",
      "num": 3099,
      "name": "partition_colmap_directory_names",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3099_partition_colmap_directory_names.sql",
      "read_script": "generator/spark-reads-df/verify_3099_partition_colmap_directory_names.py",
      "description": "Column mapping (name mode) combined with partitioning.",
      "status": "pass",
      "duration_ms": 3167,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:55:37.773925+00:00",
      "read_cold_ms": 1898,
      "read_warm_ms": 649,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/309_zorder_partitioned_table",
      "num": 309,
      "name": "zorder_partitioned_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/309_zorder_partitioned_table.sql",
      "read_script": "generator/spark-reads-df/verify_309_zorder_partitioned_table.py",
      "description": "Z-ORDER within partitions",
      "status": "pass",
      "duration_ms": 3601,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:34.386993+00:00",
      "read_cold_ms": 1918,
      "read_warm_ms": 578,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 356,
      "write_warm_ms": 328,
      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/30_column_mapping_name_mode_logical",
      "num": 30,
      "name": "column_mapping_name_mode_logical",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/30_column_mapping_name_mode_logical.sql",
      "read_script": "generator/spark-reads-df/verify_30_column_mapping_name_mode_logical.py",
      "description": "Demonstrates column mapping with name mode (logical names). In name mode, columns are referenced by name. Allows special characters and case sensitivity in column names.",
      "status": "pass",
      "duration_ms": 4259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:38.647393+00:00",
      "read_cold_ms": 3017,
      "read_warm_ms": 686,
      "skip_feature": null,
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      "write_cold_ms": 208,
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        "type:floating",
        "type:integer",
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    },
    {
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    },
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      "reader": "spark",
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      "description": "Delta statistics for DECIMAL(38,18) -- maximum precision.",
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    {
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      "description": "Delta statistics for a column with many NULLs.",
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      "read_cold_ms": 1904,
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      "description": "Delta file statistics are correct after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 3243,
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      "write_cold_ms": 1040,
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    {
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      "read_script": "generator/spark-reads-df/verify_310_zorder_after_spark_optimize.py",
      "description": "DeltaForge Z-ORDER on already Spark-optimized table",
      "status": "pass",
      "duration_ms": 3268,
      "error": null,
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      "manual_tags": [],
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    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3110_pushdown_int_range_with_dv.sql",
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      "description": "Integer range pushdown with Deletion Vectors.",
      "status": "pass",
      "duration_ms": 5438,
      "error": null,
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      "verified_at": "2026-05-19T16:56:15.245496+00:00",
      "read_cold_ms": 2203,
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    {
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      "reader": "spark",
      "write_script": "generator/df-sql/3111_pushdown_string_prefix.sql",
      "read_script": "generator/spark-reads-df/verify_3111_pushdown_string_prefix.py",
      "description": "String prefix distribution in Delta table.",
      "status": "pass",
      "duration_ms": 3937,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T16:56:19.184558+00:00",
      "read_cold_ms": 1913,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3112_pushdown_timestamp_range",
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      "reader": "spark",
      "write_script": "generator/df-sql/3112_pushdown_timestamp_range.sql",
      "read_script": "generator/spark-reads-df/verify_3112_pushdown_timestamp_range.py",
      "description": "Timestamp-like string range pushdown.",
      "status": "pass",
      "duration_ms": 3559,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T16:56:22.745240+00:00",
      "read_cold_ms": 1834,
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      "manual_tags": [],
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    },
    {
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      "reader": "spark",
      "write_script": "generator/df-sql/3113_pushdown_decimal_range.sql",
      "read_script": "generator/spark-reads-df/verify_3113_pushdown_decimal_range.py",
      "description": "DECIMAL range pushdown. Delta statistics track",
      "status": "pass",
      "duration_ms": 3846,
      "error": null,
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      "read_cold_ms": 1896,
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    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3114_pushdown_null_is_null.sql",
      "read_script": "generator/spark-reads-df/verify_3114_pushdown_null_is_null.py",
      "description": "IS NULL / IS NOT NULL pushdown with Delta statistics.",
      "status": "pass",
      "duration_ms": 3786,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T16:56:30.380685+00:00",
      "read_cold_ms": 1425,
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      "manual_tags": [],
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      "duration_ms": 3295,
      "error": null,
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      "verified_at": "2026-05-19T16:56:33.676420+00:00",
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      "reader": "spark",
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      "id": "df-writes/delta/3117_identity_delete_all_reinsert_hwm",
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      "description": "DELETE all rows then re-INSERT to verify the identity HWM is preserved across a full delete. New inserts must get ids 51-100, not restart from 1.",
      "status": "pass",
      "duration_ms": 4858,
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      "verified_at": "2026-05-19T16:56:41.163239+00:00",
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        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3118_identity_by_default_explicit_values",
      "num": 3118,
      "name": "identity_by_default_explicit_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3118_identity_by_default_explicit_values.sql",
      "read_script": "generator/spark-reads-df/verify_3118_identity_by_default_explicit_values.py",
      "description": "GENERATED BY DEFAULT AS IDENTITY allows explicit id values. This test inserts auto-generated ids (1-20), then 10 explicit large ids (100,200,...,1000), then more auto-generated ids that continue past the HWM.",
      "status": "pass",
      "duration_ms": 3218,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:56:44.382058+00:00",
      "read_cold_ms": 2088,
      "read_warm_ms": 531,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3119_identity_cdc_hwm_tracking",
      "num": 3119,
      "name": "identity_cdc_hwm_tracking",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3119_identity_cdc_hwm_tracking.sql",
      "read_script": "generator/spark-reads-df/verify_3119_identity_cdc_hwm_tracking.py",
      "description": "Identity column with CDC enabled. INSERT 30, DELETE 10, INSERT 20 more. Verifies that CDF captures all operations and new inserts continue from HWM.",
      "status": "pass",
      "duration_ms": 4452,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:56:48.835389+00:00",
      "read_cold_ms": 2266,
      "read_warm_ms": 1049,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 219,
      "write_warm_ms": 383,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/311_zorder_with_deletion_vectors",
      "num": 311,
      "name": "zorder_with_deletion_vectors",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/311_zorder_with_deletion_vectors.sql",
      "read_script": "generator/spark-reads-df/verify_311_zorder_with_deletion_vectors.py",
      "description": "Z-ORDER on table with deletion vectors",
      "status": "pass",
      "duration_ms": 3305,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:45.222268+00:00",
      "read_cold_ms": 2400,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 43,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3120_identity_colmap_physical_name",
      "num": 3120,
      "name": "identity_colmap_physical_name",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3120_identity_colmap_physical_name.sql",
      "read_script": "generator/spark-reads-df/verify_3120_identity_colmap_physical_name.py",
      "description": "Identity column combined with column mapping (mode=name). Logical names must be readable through Spark even though Delta uses physical names internally.",
      "status": "pass",
      "duration_ms": 3055,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:56:51.891385+00:00",
      "read_cold_ms": 1747,
      "read_warm_ms": 682,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3121_identity_partition_global_unique",
      "num": 3121,
      "name": "identity_partition_global_unique",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3121_identity_partition_global_unique.sql",
      "read_script": "generator/spark-reads-df/verify_3121_identity_partition_global_unique.py",
      "description": "Identity column on a partitioned table. Three separate INSERT operations each insert into a different partition. Identity must assign globally unique ids across all partitions (not per-partition sequences).",
      "status": "pass",
      "duration_ms": 2933,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:56:54.824929+00:00",
      "read_cold_ms": 1933,
      "read_warm_ms": 616,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 340,
      "write_warm_ms": 544,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3122_identity_optimize_hwm_stable",
      "num": 3122,
      "name": "identity_optimize_hwm_stable",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3122_identity_optimize_hwm_stable.sql",
      "read_script": "generator/spark-reads-df/verify_3122_identity_optimize_hwm_stable.py",
      "description": "OPTIMIZE between two identity inserts. HWM must survive the compaction and the second batch must receive ids 51-100 (not restart from 1).",
      "status": "pass",
      "duration_ms": 3222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:56:58.047685+00:00",
      "read_cold_ms": 2151,
      "read_warm_ms": 475,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 126,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3123_default_merge_not_matched",
      "num": 3123,
      "name": "default_merge_not_matched",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3123_default_merge_not_matched.sql",
      "read_script": "generator/spark-reads-df/verify_3123_default_merge_not_matched.py",
      "description": "MERGE NOT MATCHED inserts rows omitting the DEFAULT column (status). Pre-existing rows have status='active'; merged rows should receive 'pending' (default).",
      "status": "pass",
      "duration_ms": 2941,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:00.989735+00:00",
      "read_cold_ms": 1737,
      "read_warm_ms": 662,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 265,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3124_default_evolve_change_default",
      "num": 3124,
      "name": "default_evolve_change_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3124_default_evolve_change_default.sql",
      "read_script": "generator/spark-reads-df/verify_3124_default_evolve_change_default.py",
      "description": "ALTER TABLE ... ALTER COLUMN ... SET DEFAULT changes the default mid-stream. First 30 rows get priority=0 (old default); last 20 rows get priority=99 (new default).",
      "status": "pass",
      "duration_ms": 3133,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:04.124011+00:00",
      "read_cold_ms": 1979,
      "read_warm_ms": 609,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 288,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3125_default_cdc_pre_post_image",
      "num": 3125,
      "name": "default_cdc_pre_post_image",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3125_default_cdc_pre_post_image.sql",
      "read_script": "generator/spark-reads-df/verify_3125_default_cdc_pre_post_image.py",
      "description": "CDC captures pre/post images for rows with DEFAULT columns. INSERT omits status (gets default 'new'), then UPDATE changes it to 'processed'. CDF must contain pre-images with 'new' and post-images with 'processed'.",
      "status": "pass",
      "duration_ms": 4270,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:08.395655+00:00",
      "read_cold_ms": 2431,
      "read_warm_ms": 759,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 156,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3126_default_null_vs_default",
      "num": 3126,
      "name": "default_null_vs_default",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3126_default_null_vs_default.sql",
      "read_script": "generator/spark-reads-df/verify_3126_default_null_vs_default.py",
      "description": "Distinguishes explicit NULL from omitted column (DEFAULT). id=1 has x=NULL (explicitly inserted); ids 2-10 omit x and get x=42 (DEFAULT).",
      "status": "pass",
      "duration_ms": 2955,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:11.352583+00:00",
      "read_cold_ms": 1785,
      "read_warm_ms": 659,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 219,
      "write_warm_ms": 278,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3127_default_expression_current",
      "num": 3127,
      "name": "default_expression_current",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3127_default_expression_current.sql",
      "read_script": "generator/spark-reads-df/verify_3127_default_expression_current.py",
      "description": "Numeric DEFAULT 0 vs explicit total. First 30 rows provide total=price*qty; last 20 rows omit total and get DEFAULT 0.",
      "status": "pass",
      "duration_ms": 2972,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:14.326548+00:00",
      "read_cold_ms": 1824,
      "read_warm_ms": 572,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 207,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3128_gencol_update_base_recompute",
      "num": 3128,
      "name": "gencol_update_base_recompute",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3128_gencol_update_base_recompute.sql",
      "read_script": "generator/spark-reads-df/verify_3128_gencol_update_base_recompute.py",
      "description": "UPDATE a base column. The generated column (sum_ab = a + b) must auto-recompute for all updated rows. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 3851,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:18.179153+00:00",
      "read_cold_ms": 2280,
      "read_warm_ms": 761,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 103,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3129_gencol_cdc_computed_in_cdf",
      "num": 3129,
      "name": "gencol_cdc_computed_in_cdf",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3129_gencol_cdc_computed_in_cdf.sql",
      "read_script": "generator/spark-reads-df/verify_3129_gencol_cdc_computed_in_cdf.py",
      "description": "CDC with a generated column (total = price * qty). UPDATE changes price for ids 1-15. CDF post-images must contain the recomputed total. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 4242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:22.422384+00:00",
      "read_cold_ms": 2146,
      "read_warm_ms": 1050,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/312_zorder_high_cardinality",
      "num": 312,
      "name": "zorder_high_cardinality",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/312_zorder_high_cardinality.sql",
      "read_script": "generator/spark-reads-df/verify_312_zorder_high_cardinality.py",
      "description": "Z-ORDER on high cardinality columns",
      "status": "pass",
      "duration_ms": 3840,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:50:49.063661+00:00",
      "read_cold_ms": 2216,
      "read_warm_ms": 625,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1505,
      "write_warm_ms": 2045,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3130_gencol_partition_by_gencol",
      "num": 3130,
      "name": "gencol_partition_by_gencol",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3130_gencol_partition_by_gencol.sql",
      "read_script": "generator/spark-reads-df/verify_3130_gencol_partition_by_gencol.py",
      "description": "Generated column on a partitioned table (partitioned by a regular column, not the generated column itself). The doubled column (price * 2) must be correct for all rows across all partitions. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 2842,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:25.265824+00:00",
      "read_cold_ms": 1664,
      "read_warm_ms": 554,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 375,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3131_gencol_identity_plus_gencol",
      "num": 3131,
      "name": "gencol_identity_plus_gencol",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3131_gencol_identity_plus_gencol.sql",
      "read_script": "generator/spark-reads-df/verify_3131_gencol_identity_plus_gencol.py",
      "description": "Table combines GENERATED BY DEFAULT AS IDENTITY with a GENERATED ALWAYS AS column. Requires minWriterVersion=4 for the generated expression column.",
      "status": "pass",
      "duration_ms": 2834,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:28.100749+00:00",
      "read_cold_ms": 1475,
      "read_warm_ms": 787,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 176,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3132_gencol_three_gen_cols",
      "num": 3132,
      "name": "gencol_three_gen_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3132_gencol_three_gen_cols.sql",
      "read_script": "generator/spark-reads-df/verify_3132_gencol_three_gen_cols.py",
      "description": "Table with three independent generated columns (gen1=a+b, gen2=b*c, gen3=a+c). All three must be computed correctly for every row. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 2235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T16:57:30.336324+00:00",
      "read_cold_ms": 1508,
      "read_warm_ms": 319,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3133_gencol_string_concat_gen",
      "num": 3133,
      "name": "gencol_string_concat_gen",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3133_gencol_string_concat_gen.sql",
      "read_script": "generator/spark-reads-df/verify_3133_gencol_string_concat_gen.py",
      "description": "Generated column using string CONCAT (full_name = CONCAT(first_name, ' ', last_name)). Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 2348,
      "error": null,
      "notes": null,
      "extras": {},
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    },
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    {
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        "format:delta"
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      "manual_tags": [],
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    },
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      "reader": "spark",
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      "manual_tags": [],
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    },
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      "writer": "delta-forge",
      "reader": "spark",
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        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "extras": {},
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        "delta:optimize",
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        "format:delta"
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    },
    {
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      "format": "delta",
      "writer": "delta-forge",
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        "dml:insert",
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        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
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    },
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      "reader": "spark",
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        "dml:insert",
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        "format:delta"
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    },
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    {
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      "type": "df-writes",
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      "reader": "spark",
      "write_script": "generator/df-sql/3185_colmap_evolve_rename.sql",
      "read_script": "generator/spark-reads-df/verify_3185_colmap_evolve_rename.py",
      "description": "column mapping (name) + schema evolution (ADD COLUMN).",
      "status": "pass",
      "duration_ms": 2601,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:17.428244+00:00",
      "read_cold_ms": 1467,
      "read_warm_ms": 551,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3186_dv_checkpoint_survive",
      "num": 3186,
      "name": "dv_checkpoint_survive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3186_dv_checkpoint_survive.sql",
      "read_script": "generator/spark-reads-df/verify_3186_dv_checkpoint_survive.py",
      "description": "DV + checkpoint survival. DELETE with DVs, then 10 single-row",
      "status": "pass",
      "duration_ms": 5802,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:23.232263+00:00",
      "read_cold_ms": 3201,
      "read_warm_ms": 1179,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 490,
      "write_warm_ms": 617,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3187_dv_constraint_check_after_delete",
      "num": 3187,
      "name": "dv_constraint_check_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3187_dv_constraint_check_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3187_dv_constraint_check_after_delete.py",
      "description": "DV + CHECK constraint. Adds constraint then deletes rows,",
      "status": "pass",
      "duration_ms": 5416,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:28.649510+00:00",
      "read_cold_ms": 3656,
      "read_warm_ms": 895,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 301,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3188_dv_default_after_delete",
      "num": 3188,
      "name": "dv_default_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3188_dv_default_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3188_dv_default_after_delete.py",
      "description": "DV + DEFAULT column. INSERT with explicit status, DELETE some,",
      "status": "pass",
      "duration_ms": 5441,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:34.092254+00:00",
      "read_cold_ms": 3292,
      "read_warm_ms": 985,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 305,
      "write_warm_ms": 114,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3189_dv_evolve_add_col",
      "num": 3189,
      "name": "dv_evolve_add_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3189_dv_evolve_add_col.sql",
      "read_script": "generator/spark-reads-df/verify_3189_dv_evolve_add_col.py",
      "description": "DV + schema evolution (ADD COLUMN). DELETE with DVs, then",
      "status": "pass",
      "duration_ms": 5649,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:39.743238+00:00",
      "read_cold_ms": 3286,
      "read_warm_ms": 851,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 167,
      "write_warm_ms": 224,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/318_zorder_hilbert_curve",
      "num": 318,
      "name": "zorder_hilbert_curve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/318_zorder_hilbert_curve.sql",
      "read_script": "generator/spark-reads-df/verify_318_zorder_hilbert_curve.py",
      "description": "Hilbert curve vs Z-curve comparison",
      "status": "pass",
      "duration_ms": 3936,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:13.046339+00:00",
      "read_cold_ms": 2007,
      "read_warm_ms": 623,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 200,
      "write_warm_ms": 86,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3190_dv_ict_interaction",
      "num": 3190,
      "name": "dv_ict_interaction",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3190_dv_ict_interaction.sql",
      "read_script": "generator/spark-reads-df/verify_3190_dv_ict_interaction.py",
      "description": "DV + ICT together. INSERT, DELETE with DVs, INSERT more.",
      "status": "pass",
      "duration_ms": 6062,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:45.806789+00:00",
      "read_cold_ms": 3105,
      "read_warm_ms": 1562,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3191_dv_optimize_compact",
      "num": 3191,
      "name": "dv_optimize_compact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3191_dv_optimize_compact.sql",
      "read_script": "generator/spark-reads-df/verify_3191_dv_optimize_compact.py",
      "description": "DV + OPTIMIZE. DELETE with DVs then compact files.",
      "status": "pass",
      "duration_ms": 5591,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:51.399655+00:00",
      "read_cold_ms": 3031,
      "read_warm_ms": 1326,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 268,
      "write_warm_ms": 56,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3192_dv_partition_cross",
      "num": 3192,
      "name": "dv_partition_cross",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3192_dv_partition_cross.sql",
      "read_script": "generator/spark-reads-df/verify_3192_dv_partition_cross.py",
      "description": "DV + partitioning. DELETE only from specific partitions,",
      "status": "pass",
      "duration_ms": 5601,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:01:57.002484+00:00",
      "read_cold_ms": 3373,
      "read_warm_ms": 960,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 164,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3193_dv_restore_deleted_rows",
      "num": 3193,
      "name": "dv_restore_deleted_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3193_dv_restore_deleted_rows.sql",
      "read_script": "generator/spark-reads-df/verify_3193_dv_restore_deleted_rows.py",
      "description": "DV + RESTORE. DELETE with DVs then RESTORE to undo.",
      "status": "pass",
      "duration_ms": 4446,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:01.450242+00:00",
      "read_cold_ms": 3025,
      "read_warm_ms": 772,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 112,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3194_dv_time_travel_prior",
      "num": 3194,
      "name": "dv_time_travel_prior",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3194_dv_time_travel_prior.sql",
      "read_script": "generator/spark-reads-df/verify_3194_dv_time_travel_prior.py",
      "description": "DV + time travel. INSERT then DELETE, read prior version",
      "status": "pass",
      "duration_ms": 8556,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:10.007722+00:00",
      "read_cold_ms": 3580,
      "read_warm_ms": 1200,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3195_dv_vacuum_cleanup",
      "num": 3195,
      "name": "dv_vacuum_cleanup",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3195_dv_vacuum_cleanup.sql",
      "read_script": "generator/spark-reads-df/verify_3195_dv_vacuum_cleanup.py",
      "description": "DV + VACUUM. DELETE, OPTIMIZE, then VACUUM to clean up.",
      "status": "pass",
      "duration_ms": 5726,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:15.735495+00:00",
      "read_cold_ms": 3264,
      "read_warm_ms": 976,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3196_dv_widen_after_delete",
      "num": 3196,
      "name": "dv_widen_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3196_dv_widen_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3196_dv_widen_after_delete.py",
      "description": "DV + type widening. DELETE with DVs, widen INT->BIGINT,",
      "status": "pass",
      "duration_ms": 5126,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:20.863688+00:00",
      "read_cold_ms": 3278,
      "read_warm_ms": 759,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3197_ict_checkpoint_ordering",
      "num": 3197,
      "name": "ict_checkpoint_ordering",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3197_ict_checkpoint_ordering.sql",
      "read_script": "generator/spark-reads-df/verify_3197_ict_checkpoint_ordering.py",
      "description": "ICT + checkpoint. 12 single-row inserts to force checkpoint.",
      "status": "pass",
      "duration_ms": 4942,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:25.808361+00:00",
      "read_cold_ms": 2625,
      "read_warm_ms": 1317,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 693,
      "write_warm_ms": 679,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3198_ict_constraint_check",
      "num": 3198,
      "name": "ict_constraint_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3198_ict_constraint_check.sql",
      "read_script": "generator/spark-reads-df/verify_3198_ict_constraint_check.py",
      "description": "ICT + CHECK constraint. Verifies both features coexist.",
      "status": "pass",
      "duration_ms": 3970,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:29.780495+00:00",
      "read_cold_ms": 2257,
      "read_warm_ms": 921,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3199_ict_default_values",
      "num": 3199,
      "name": "ict_default_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3199_ict_default_values.sql",
      "read_script": "generator/spark-reads-df/verify_3199_ict_default_values.py",
      "description": "ICT + DEFAULT column values. INSERT omitting default column.",
      "status": "pass",
      "duration_ms": 3480,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:33.261990+00:00",
      "read_cold_ms": 2065,
      "read_warm_ms": 743,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/319_merge_comprehensive_basic",
      "num": 319,
      "name": "merge_comprehensive_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/319_merge_comprehensive_basic.sql",
      "read_script": "generator/spark-reads-df/verify_319_merge_comprehensive_basic.py",
      "description": "Basic MERGE UPSERT (WHEN MATCHED UPDATE + WHEN NOT MATCHED INSERT)",
      "status": "pass",
      "duration_ms": 3148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:16.196542+00:00",
      "read_cold_ms": 2414,
      "read_warm_ms": 362,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 23,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/31_dv_descriptor_schema_validation",
      "num": 31,
      "name": "dv_descriptor_schema_validation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/31_dv_descriptor_schema_validation.sql",
      "read_script": "generator/spark-reads-df/verify_31_dv_descriptor_schema_validation.py",
      "description": "Demonstrates deletion vector descriptor schema validation. Schema (18 columns): post_id (string), user_id (string), content_type (string), platform (string), post_timestamp (string), content_length_chars (bigint), likes_count (bigint), shares_count (bigint), comments_count...",
      "status": "pass",
      "duration_ms": 4435,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:20.632834+00:00",
      "read_cold_ms": 1941,
      "read_warm_ms": 926,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 353,
      "write_warm_ms": 339,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3200_ict_evolve_schema",
      "num": 3200,
      "name": "ict_evolve_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3200_ict_evolve_schema.sql",
      "read_script": "generator/spark-reads-df/verify_3200_ict_evolve_schema.py",
      "description": "ICT + schema evolution. INSERT, ADD COLUMN, INSERT more.",
      "status": "pass",
      "duration_ms": 4092,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:02:37.355991+00:00",
      "read_cold_ms": 2205,
      "read_warm_ms": 914,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 328,
      "write_warm_ms": 76,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3201_ict_optimize_timestamps",
      "num": 3201,
      "name": "ict_optimize_timestamps",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3201_ict_optimize_timestamps.sql",
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      "notes": null,
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        "type:string",
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        "delta:deletion-vectors",
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        "format:delta"
      ],
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      "concern_note": null
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      "name": "ict_partition_ordering",
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
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      "error": null,
      "notes": null,
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      "verified_at": "2026-05-19T17:02:45.767300+00:00",
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        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3203_ict_restore_timestamps",
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      "reader": "spark",
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      "error": null,
      "notes": null,
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      "concern_note": null
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      "error": null,
      "notes": null,
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        "dml:insert",
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        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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      "error": null,
      "notes": null,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "error": null,
      "notes": null,
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        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
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      "read_cold_ms": 3048,
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      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3208_widen_constraint_coerce",
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      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:15.892935+00:00",
      "read_cold_ms": 2904,
      "read_warm_ms": 633,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3209_widen_default_type",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:19.815127+00:00",
      "read_cold_ms": 2254,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/320_merge_matched_delete.sql",
      "read_script": "generator/spark-reads-df/verify_320_merge_matched_delete.py",
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      "status": "pass",
      "duration_ms": 2565,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-20T00:51:23.198254+00:00",
      "read_cold_ms": 1741,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
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      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3210_widen_evolve_add_then_widen",
      "num": 3210,
      "name": "widen_evolve_add_then_widen",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3210_widen_evolve_add_then_widen.sql",
      "read_script": "generator/spark-reads-df/verify_3210_widen_evolve_add_then_widen.py",
      "description": "Type widening + schema evolution (ADD COLUMN then widen).",
      "status": "pass",
      "duration_ms": 3886,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:23.702778+00:00",
      "read_cold_ms": 2019,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3211_widen_optimize_post",
      "num": 3211,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3211_widen_optimize_post.py",
      "description": "Type widening + OPTIMIZE. Widen INT->BIGINT, 5x INSERT 10, OPTIMIZE.",
      "status": "pass",
      "duration_ms": 4388,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:28.091619+00:00",
      "read_cold_ms": 2718,
      "read_warm_ms": 688,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 349,
      "write_warm_ms": 433,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "num": 3212,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3212_widen_partition_typed.sql",
      "read_script": "generator/spark-reads-df/verify_3212_widen_partition_typed.py",
      "description": "Type widening + partitioning. INSERT 80 rows, widen INT->BIGINT.",
      "status": "pass",
      "duration_ms": 2854,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:30.946173+00:00",
      "read_cold_ms": 1873,
      "read_warm_ms": 518,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 225,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3213_widen_restore_pre_widen",
      "num": 3213,
      "name": "widen_restore_pre_widen",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3213_widen_restore_pre_widen.sql",
      "read_script": "generator/spark-reads-df/verify_3213_widen_restore_pre_widen.py",
      "description": "Type widening + RESTORE to pre-widen state.",
      "status": "pass",
      "duration_ms": 4292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:35.239587+00:00",
      "read_cold_ms": 2849,
      "read_warm_ms": 766,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 153,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3214_widen_time_travel_type",
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      "name": "widen_time_travel_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3214_widen_time_travel_type.sql",
      "read_script": "generator/spark-reads-df/verify_3214_widen_time_travel_type.py",
      "description": "Type widening + time travel.",
      "status": "pass",
      "duration_ms": 7076,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:42.316191+00:00",
      "read_cold_ms": 2829,
      "read_warm_ms": 755,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 151,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3215_widen_vacuum_metadata",
      "num": 3215,
      "name": "widen_vacuum_metadata",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3215_widen_vacuum_metadata.sql",
      "read_script": "generator/spark-reads-df/verify_3215_widen_vacuum_metadata.py",
      "description": "Type widening + VACUUM. Widen INT->BIGINT, DELETE, VACUUM.",
      "status": "pass",
      "duration_ms": 6161,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:48.478739+00:00",
      "read_cold_ms": 3712,
      "read_warm_ms": 1271,
      "skip_feature": null,
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      "write_cold_ms": 72,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3216_checkpoint_evolve_schema",
      "num": 3216,
      "name": "checkpoint_evolve_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3216_checkpoint_evolve_schema.sql",
      "read_script": "generator/spark-reads-df/verify_3216_checkpoint_evolve_schema.py",
      "description": "Checkpoint + schema evolution. CREATE with id/val, INSERT 50 rows,",
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      "duration_ms": 4942,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:53.423497+00:00",
      "read_cold_ms": 3211,
      "read_warm_ms": 779,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 635,
      "write_warm_ms": 550,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/3217_checkpoint_partition_multi",
      "num": 3217,
      "name": "checkpoint_partition_multi",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3217_checkpoint_partition_multi.sql",
      "read_script": "generator/spark-reads-df/verify_3217_checkpoint_partition_multi.py",
      "description": "Checkpoint + partitioning. 12x INSERT 5 rows each across 4 partitions.",
      "status": "pass",
      "duration_ms": 4606,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:03:58.030887+00:00",
      "read_cold_ms": 2586,
      "read_warm_ms": 1080,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 891,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3218_checkpoint_merge_trigger",
      "num": 3218,
      "name": "checkpoint_merge_trigger",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3218_checkpoint_merge_trigger.sql",
      "read_script": "generator/spark-reads-df/verify_3218_checkpoint_merge_trigger.py",
      "description": "Checkpoint + MERGE. INSERT 50 rows, then 11x MERGE each adding 1 new row",
      "status": "pass",
      "duration_ms": 4660,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:02.692978+00:00",
      "read_cold_ms": 2931,
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      "skip_feature": null,
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      "write_cold_ms": 1311,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3219_checkpoint_vacuum_log",
      "num": 3219,
      "name": "checkpoint_vacuum_log",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3219_checkpoint_vacuum_log.sql",
      "read_script": "generator/spark-reads-df/verify_3219_checkpoint_vacuum_log.py",
      "description": "Checkpoint + VACUUM. 15x INSERT 5 rows each to trigger checkpoint, then VACUUM.",
      "status": "pass",
      "duration_ms": 4847,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:07.542738+00:00",
      "read_cold_ms": 3381,
      "read_warm_ms": 769,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 740,
      "write_warm_ms": 1228,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/321_merge_not_matched_source_delete",
      "num": 321,
      "name": "merge_not_matched_source_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/321_merge_not_matched_source_delete.sql",
      "read_script": "generator/spark-reads-df/verify_321_merge_not_matched_source_delete.py",
      "description": "MERGE WHEN NOT MATCHED BY SOURCE DELETE pattern (full sync)",
      "status": "pass",
      "duration_ms": 2494,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:25.693025+00:00",
      "read_cold_ms": 1656,
      "read_warm_ms": 309,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 33,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3220_checkpoint_restore_pre",
      "num": 3220,
      "name": "checkpoint_restore_pre",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3220_checkpoint_restore_pre.sql",
      "read_script": "generator/spark-reads-df/verify_3220_checkpoint_restore_pre.py",
      "description": "Checkpoint + RESTORE. 15x INSERT 1 row each to trigger checkpoint, then RESTORE TO VERSION 5.",
      "status": "pass",
      "duration_ms": 4598,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:12.141876+00:00",
      "read_cold_ms": 2935,
      "read_warm_ms": 851,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1421,
      "write_warm_ms": 1163,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3221_restore_colmap_mapping",
      "num": 3221,
      "name": "restore_colmap_mapping",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3221_restore_colmap_mapping.sql",
      "read_script": "generator/spark-reads-df/verify_3221_restore_colmap_mapping.py",
      "description": "RESTORE + column mapping. INSERT 50, UPDATE first 20 names, RESTORE to version 1.",
      "status": "pass",
      "duration_ms": 4420,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:16.564539+00:00",
      "read_cold_ms": 2805,
      "read_warm_ms": 766,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 442,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3222_restore_dv_undelete",
      "num": 3222,
      "name": "restore_dv_undelete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3222_restore_dv_undelete.sql",
      "read_script": "generator/spark-reads-df/verify_3222_restore_dv_undelete.py",
      "description": "RESTORE + deletion vectors. INSERT 50, DELETE first 20 (via DV), RESTORE to version 1.",
      "status": "pass",
      "duration_ms": 4310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:20.877174+00:00",
      "read_cold_ms": 2320,
      "read_warm_ms": 997,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 284,
      "write_warm_ms": 114,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3223_restore_evolve_rollback",
      "num": 3223,
      "name": "restore_evolve_rollback",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3223_restore_evolve_rollback.sql",
      "read_script": "generator/spark-reads-df/verify_3223_restore_evolve_rollback.py",
      "description": "RESTORE + schema evolution rollback. INSERT 50, ADD COLUMN tag, INSERT 50 more,",
      "status": "pass",
      "duration_ms": 3882,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:24.761141+00:00",
      "read_cold_ms": 2662,
      "read_warm_ms": 640,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 256,
      "write_warm_ms": 349,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3224_restore_partition_subset",
      "num": 3224,
      "name": "restore_partition_subset",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3224_restore_partition_subset.sql",
      "read_script": "generator/spark-reads-df/verify_3224_restore_partition_subset.py",
      "description": "RESTORE + partition. INSERT 80 rows across 4 partitions,",
      "status": "pass",
      "duration_ms": 4441,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:29.204156+00:00",
      "read_cold_ms": 3003,
      "read_warm_ms": 740,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 339,
      "write_warm_ms": 248,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3225_restore_widen_rollback",
      "num": 3225,
      "name": "restore_widen_rollback",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3225_restore_widen_rollback.sql",
      "read_script": "generator/spark-reads-df/verify_3225_restore_widen_rollback.py",
      "description": "RESTORE + type widening rollback. INSERT 50, ALTER val INT->BIGINT, RESTORE to version 1.",
      "status": "pass",
      "duration_ms": 4479,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:33.684441+00:00",
      "read_cold_ms": 2846,
      "read_warm_ms": 694,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 242,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3226_vacuum_colmap_safe",
      "num": 3226,
      "name": "vacuum_colmap_safe",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3226_vacuum_colmap_safe.sql",
      "read_script": "generator/spark-reads-df/verify_3226_vacuum_colmap_safe.py",
      "description": "VACUUM + column mapping. INSERT 50, DELETE first 20, VACUUM.",
      "status": "pass",
      "duration_ms": 5640,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:39.326703+00:00",
      "read_cold_ms": 3632,
      "read_warm_ms": 892,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 116,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3227_vacuum_dv_resolved",
      "num": 3227,
      "name": "vacuum_dv_resolved",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3227_vacuum_dv_resolved.sql",
      "read_script": "generator/spark-reads-df/verify_3227_vacuum_dv_resolved.py",
      "description": "VACUUM + DV. INSERT 100, DELETE first 30 (via DV), OPTIMIZE, VACUUM.",
      "status": "pass",
      "duration_ms": 5583,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:44.910989+00:00",
      "read_cold_ms": 3436,
      "read_warm_ms": 1106,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 303,
      "write_warm_ms": 151,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3228_vacuum_evolve_schema",
      "num": 3228,
      "name": "vacuum_evolve_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3228_vacuum_evolve_schema.sql",
      "read_script": "generator/spark-reads-df/verify_3228_vacuum_evolve_schema.py",
      "description": "VACUUM + schema evolution. INSERT 50, ADD COLUMN tag, INSERT 50 more, VACUUM.",
      "status": "pass",
      "duration_ms": 4500,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:49.412250+00:00",
      "read_cold_ms": 3081,
      "read_warm_ms": 778,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3229_vacuum_partition_selective",
      "num": 3229,
      "name": "vacuum_partition_selective",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3229_vacuum_partition_selective.sql",
      "read_script": "generator/spark-reads-df/verify_3229_vacuum_partition_selective.py",
      "description": "VACUUM + partition. INSERT 80 rows across 4 partitions, DELETE US partition, VACUUM.",
      "status": "pass",
      "duration_ms": 4211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:53.625006+00:00",
      "read_cold_ms": 2634,
      "read_warm_ms": 894,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 478,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/322_merge_not_matched_source_update",
      "num": 322,
      "name": "merge_not_matched_source_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/322_merge_not_matched_source_update.sql",
      "read_script": "generator/spark-reads-df/verify_322_merge_not_matched_source_update.py",
      "description": "MERGE WHEN NOT MATCHED BY SOURCE UPDATE pattern (soft delete)",
      "status": "pass",
      "duration_ms": 3677,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:29.370674+00:00",
      "read_cold_ms": 2246,
      "read_warm_ms": 789,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 124,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3230_vacuum_widen_metadata",
      "num": 3230,
      "name": "vacuum_widen_metadata",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3230_vacuum_widen_metadata.sql",
      "read_script": "generator/spark-reads-df/verify_3230_vacuum_widen_metadata.py",
      "description": "VACUUM + type widening. INSERT 50, widen val INT->BIGINT, DELETE first 20, VACUUM.",
      "status": "pass",
      "duration_ms": 5762,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:04:59.388995+00:00",
      "read_cold_ms": 3234,
      "read_warm_ms": 834,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 182,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3231_vacuum_identity_hwm",
      "num": 3231,
      "name": "vacuum_identity_hwm",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3231_vacuum_identity_hwm.sql",
      "read_script": "generator/spark-reads-df/verify_3231_vacuum_identity_hwm.py",
      "description": "VACUUM + IDENTITY. INSERT 50, DELETE first 40, VACUUM, INSERT 10 more.",
      "status": "pass",
      "duration_ms": 5674,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:05:05.064690+00:00",
      "read_cold_ms": 3372,
      "read_warm_ms": 1135,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 253,
      "write_warm_ms": 381,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3232_stats_colmap_pushdown",
      "num": 3232,
      "name": "stats_colmap_pushdown",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3232_stats_colmap_pushdown.sql",
      "read_script": "generator/spark-reads-df/verify_3232_stats_colmap_pushdown.py",
      "description": "Stats + column mapping. INSERT 1000 rows for stats collection with colmap=name.",
      "status": "pass",
      "duration_ms": 5766,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:05:10.831909+00:00",
      "read_cold_ms": 2945,
      "read_warm_ms": 666,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3233_stats_dv_after_delete",
      "num": 3233,
      "name": "stats_dv_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3233_stats_dv_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3233_stats_dv_after_delete.py",
      "description": "Stats + DV after DELETE. INSERT 100, DELETE 20. Stats reflect visible rows.",
      "status": "pass",
      "duration_ms": 5608,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:05:16.441809+00:00",
      "read_cold_ms": 3484,
      "read_warm_ms": 943,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3234_stats_evolve_new_col",
      "num": 3234,
      "name": "stats_evolve_new_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3234_stats_evolve_new_col.sql",
      "read_script": "generator/spark-reads-df/verify_3234_stats_evolve_new_col.py",
      "description": "Stats after schema evolution (ADD COLUMN). INSERT 50, ADD COLUMN, INSERT 50 more.",
      "status": "pass",
      "duration_ms": 4748,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:05:21.191507+00:00",
      "read_cold_ms": 3132,
      "read_warm_ms": 672,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 224,
      "write_warm_ms": 165,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3235_stats_identity_minmax",
      "num": 3235,
      "name": "stats_identity_minmax",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3235_stats_identity_minmax.sql",
      "read_script": "generator/spark-reads-df/verify_3235_stats_identity_minmax.py",
      "description": "Stats min/max on IDENTITY column. INSERT 100 rows.",
      "status": "pass",
      "duration_ms": 4084,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:05:25.280044+00:00",
      "read_cold_ms": 2493,
      "read_warm_ms": 961,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 184,
      "write_warm_ms": 187,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3236_stats_partition_per_file",
      "num": 3236,
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      "manual_tags": [],
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      "manual_tags": [],
      "concern_note": null
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      "manual_tags": [],
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      "manual_tags": [],
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      "error": null,
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      "manual_tags": [],
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      "error": null,
      "notes": null,
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      "manual_tags": [],
      "concern_note": null
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/324_merge_partitioned.sql",
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      "duration_ms": 6149,
      "error": null,
      "notes": null,
      "extras": {},
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      "manual_tags": [],
      "concern_note": null
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      "error": null,
      "notes": null,
      "extras": {},
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      "manual_tags": [],
      "concern_note": null
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      "extras": {},
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      "manual_tags": [],
      "concern_note": null
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3253_rowtrack_cdc_tracking",
      "num": 3253,
      "name": "rowtrack_cdc_tracking",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3253_rowtrack_cdc_tracking.sql",
      "read_script": "generator/spark-reads-df/verify_3253_rowtrack_cdc_tracking.py",
      "description": "Row tracking + CDC. INSERT 50, UPDATE 20, DELETE 10. Final: 20 rows.",
      "status": "pass",
      "duration_ms": 6763,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:06:49.544746+00:00",
      "read_cold_ms": 3560,
      "read_warm_ms": 1263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 446,
      "write_warm_ms": 297,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3254_rowtrack_dv_stable",
      "num": 3254,
      "name": "rowtrack_dv_stable",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3254_rowtrack_dv_stable.sql",
      "read_script": "generator/spark-reads-df/verify_3254_rowtrack_dv_stable.py",
      "description": "Row tracking + DV stability. INSERT 50, DELETE 20, INSERT 30. Final: 60 rows.",
      "status": "pass",
      "duration_ms": 6062,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:06:55.608126+00:00",
      "read_cold_ms": 3691,
      "read_warm_ms": 927,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 210,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3255_rowtrack_optimize_persist",
      "num": 3255,
      "name": "rowtrack_optimize_persist",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3255_rowtrack_optimize_persist.sql",
      "read_script": "generator/spark-reads-df/verify_3255_rowtrack_optimize_persist.py",
      "description": "Row tracking persists through OPTIMIZE. 5x INSERT 20, OPTIMIZE. Final: 100 rows.",
      "status": "pass",
      "duration_ms": 5630,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:01.239520+00:00",
      "read_cold_ms": 2982,
      "read_warm_ms": 898,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 368,
      "write_warm_ms": 415,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3256_rowtrack_merge_tracking",
      "num": 3256,
      "name": "rowtrack_merge_tracking",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3256_rowtrack_merge_tracking.sql",
      "read_script": "generator/spark-reads-df/verify_3256_rowtrack_merge_tracking.py",
      "description": "Row tracking + MERGE. INSERT 50, MERGE update 20 + insert 30. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 7262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:08.503280+00:00",
      "read_cold_ms": 3894,
      "read_warm_ms": 1840,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3257_rowtrack_evolve_schema",
      "num": 3257,
      "name": "rowtrack_evolve_schema",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3257_rowtrack_evolve_schema.sql",
      "read_script": "generator/spark-reads-df/verify_3257_rowtrack_evolve_schema.py",
      "description": "Row tracking + schema evolution. INSERT 50, ADD COLUMN, INSERT 50. Final: 100 rows.",
      "status": "pass",
      "duration_ms": 4928,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:13.432367+00:00",
      "read_cold_ms": 3177,
      "read_warm_ms": 714,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 267,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3258_rowtrack_partition_dist",
      "num": 3258,
      "name": "rowtrack_partition_dist",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3258_rowtrack_partition_dist.sql",
      "read_script": "generator/spark-reads-df/verify_3258_rowtrack_partition_dist.py",
      "description": "Row tracking + partitioned table. INSERT 80 across 4 partitions. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 5041,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:18.477025+00:00",
      "read_cold_ms": 2933,
      "read_warm_ms": 908,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 207,
      "write_warm_ms": 56,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3259_colmap_dv_optimize_triple",
      "num": 3259,
      "name": "colmap_dv_optimize_triple",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3259_colmap_dv_optimize_triple.sql",
      "read_script": "generator/spark-reads-df/verify_3259_colmap_dv_optimize_triple.py",
      "description": "Triple: colmap + DV + OPTIMIZE. INSERT 100, DELETE 20, OPTIMIZE. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 6798,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:25.279757+00:00",
      "read_cold_ms": 3628,
      "read_warm_ms": 1366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 94,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/325_merge_with_dv",
      "num": 325,
      "name": "merge_with_dv",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/325_merge_with_dv.sql",
      "read_script": "generator/spark-reads-df/verify_325_merge_with_dv.py",
      "description": "MERGE operations with deletion vectors enabled",
      "status": "pass",
      "duration_ms": 3240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:41.609978+00:00",
      "read_cold_ms": 2230,
      "read_warm_ms": 559,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 21,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3260_ict_dv_cdc_triple",
      "num": 3260,
      "name": "ict_dv_cdc_triple",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3260_ict_dv_cdc_triple.sql",
      "read_script": "generator/spark-reads-df/verify_3260_ict_dv_cdc_triple.py",
      "description": "Triple: ICT + DV + CDC. INSERT 50, DELETE 20, UPDATE 15. Final: 15 rows.",
      "status": "pass",
      "duration_ms": 6622,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:31.903890+00:00",
      "read_cold_ms": 3613,
      "read_warm_ms": 1115,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 166,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3261_merge_ten_versions_accum",
      "num": 3261,
      "name": "merge_ten_versions_accum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3261_merge_ten_versions_accum.sql",
      "read_script": "generator/spark-reads-df/verify_3261_merge_ten_versions_accum.py",
      "description": "10 consecutive MERGEs accumulating data with updates and inserts",
      "status": "pass",
      "duration_ms": 8716,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:40.621340+00:00",
      "read_cold_ms": 5503,
      "read_warm_ms": 1490,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1142,
      "write_warm_ms": 1176,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3262_update_delete_update_cycle",
      "num": 3262,
      "name": "update_delete_update_cycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3262_update_delete_update_cycle.sql",
      "read_script": "generator/spark-reads-df/verify_3262_update_delete_update_cycle.py",
      "description": "UPDATE then DELETE then UPDATE cycle",
      "status": "pass",
      "duration_ms": 6153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:46.775621+00:00",
      "read_cold_ms": 3652,
      "read_warm_ms": 1355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 219,
      "write_warm_ms": 190,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3263_delete_all_reinsert_full",
      "num": 3263,
      "name": "delete_all_reinsert_full",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3263_delete_all_reinsert_full.sql",
      "read_script": "generator/spark-reads-df/verify_3263_delete_all_reinsert_full.py",
      "description": "DELETE all rows then re-INSERT fresh data",
      "status": "pass",
      "duration_ms": 5484,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:52.261120+00:00",
      "read_cold_ms": 3218,
      "read_warm_ms": 1104,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 186,
      "write_warm_ms": 152,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3264_merge_self_join_dedup",
      "num": 3264,
      "name": "merge_self_join_dedup",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3264_merge_self_join_dedup.sql",
      "read_script": "generator/spark-reads-df/verify_3264_merge_self_join_dedup.py",
      "description": "MERGE for deduplication using self-join to keep latest version",
      "status": "pass",
      "duration_ms": 5939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:07:58.201604+00:00",
      "read_cold_ms": 3549,
      "read_warm_ms": 1089,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3265_insert_hundred_single_rows",
      "num": 3265,
      "name": "insert_hundred_single_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3265_insert_hundred_single_rows.sql",
      "read_script": "generator/spark-reads-df/verify_3265_insert_hundred_single_rows.py",
      "description": "20 small batch INSERTs creating 100 rows total (multi-version table)",
      "status": "pass",
      "duration_ms": 6247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:04.449974+00:00",
      "read_cold_ms": 3645,
      "read_warm_ms": 880,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1672,
      "write_warm_ms": 1295,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3266_update_with_case_expr",
      "num": 3266,
      "name": "update_with_case_expr",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3266_update_with_case_expr.sql",
      "read_script": "generator/spark-reads-df/verify_3266_update_with_case_expr.py",
      "description": "UPDATE with CASE expression mapping categories to results",
      "status": "pass",
      "duration_ms": 6564,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:11.017423+00:00",
      "read_cold_ms": 4011,
      "read_warm_ms": 1369,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3267_delete_predicate_compound",
      "num": 3267,
      "name": "delete_predicate_compound",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3267_delete_predicate_compound.sql",
      "read_script": "generator/spark-reads-df/verify_3267_delete_predicate_compound.py",
      "description": "DELETE with compound predicate (AND/OR)",
      "status": "pass",
      "duration_ms": 5900,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:16.919612+00:00",
      "read_cold_ms": 3540,
      "read_warm_ms": 1161,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3268_merge_update_delete_insert_all",
      "num": 3268,
      "name": "merge_update_delete_insert_all",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3268_merge_update_delete_insert_all.sql",
      "read_script": "generator/spark-reads-df/verify_3268_merge_update_delete_insert_all.py",
      "description": "MERGE with all 3 clauses (UPDATE, DELETE, INSERT)",
      "status": "pass",
      "duration_ms": 5950,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:22.872191+00:00",
      "read_cold_ms": 3479,
      "read_warm_ms": 1089,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3269_insert_overwrite_partition",
      "num": 3269,
      "name": "insert_overwrite_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3269_insert_overwrite_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3269_insert_overwrite_partition.py",
      "description": "INSERT OVERWRITE replacing a single partition",
      "status": "pass",
      "duration_ms": 4350,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:27.224946+00:00",
      "read_cold_ms": 2886,
      "read_warm_ms": 627,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 161,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/326_merge_identity",
      "num": 326,
      "name": "merge_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/326_merge_identity.sql",
      "read_script": "generator/spark-reads-df/verify_326_merge_identity.py",
      "description": "Tables with IDENTITY columns (auto-generated values)",
      "status": "pass",
      "duration_ms": 2764,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:44.375489+00:00",
      "read_cold_ms": 1981,
      "read_warm_ms": 291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 20,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3270_update_all_columns",
      "num": 3270,
      "name": "update_all_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3270_update_all_columns.py",
      "description": "UPDATE every non-key column in one statement",
      "status": "pass",
      "duration_ms": 6538,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-19T17:08:33.763935+00:00",
      "read_cold_ms": 3862,
      "read_warm_ms": 1485,
      "skip_feature": null,
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      "write_warm_ms": 187,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3271_delete_then_insert_same_ids",
      "num": 3271,
      "name": "delete_then_insert_same_ids",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3271_delete_then_insert_same_ids.sql",
      "read_script": "generator/spark-reads-df/verify_3271_delete_then_insert_same_ids.py",
      "description": "DELETE specific IDs then INSERT same IDs with new data",
      "status": "pass",
      "duration_ms": 5695,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:39.460863+00:00",
      "read_cold_ms": 3180,
      "read_warm_ms": 1205,
      "skip_feature": null,
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      "write_cold_ms": 56,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3272_merge_cdc_five_rounds",
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      "name": "merge_cdc_five_rounds",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3272_merge_cdc_five_rounds.sql",
      "read_script": "generator/spark-reads-df/verify_3272_merge_cdc_five_rounds.py",
      "description": "5 rounds of MERGE on a CDC-enabled table",
      "status": "pass",
      "duration_ms": 6301,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:45.764329+00:00",
      "read_cold_ms": 3504,
      "read_warm_ms": 1094,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3273_update_zero_rows_match",
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      "format": "delta",
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      "reader": "spark",
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      "description": "UPDATE matching no rows leaves table unchanged",
      "status": "pass",
      "duration_ms": 4448,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:50.213977+00:00",
      "read_cold_ms": 2928,
      "read_warm_ms": 833,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
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        "dml:update",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3274_delete_all_then_vacuum",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3274_delete_all_then_vacuum.py",
      "description": "DELETE all rows + VACUUM producing empty readable table",
      "status": "pass",
      "duration_ms": 6690,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:08:56.905798+00:00",
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      "read_warm_ms": 1217,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 60,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3275_merge_with_computed_source",
      "num": 3275,
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3275_merge_with_computed_source.py",
      "description": "MERGE with computed source extending and updating table",
      "status": "pass",
      "duration_ms": 6078,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:02.985842+00:00",
      "read_cold_ms": 3679,
      "read_warm_ms": 1215,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 167,
      "write_warm_ms": 360,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
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        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3276_insert_duplicate_key_append",
      "num": 3276,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3276_insert_duplicate_key_append.sql",
      "read_script": "generator/spark-reads-df/verify_3276_insert_duplicate_key_append.py",
      "description": "Multiple INSERTs with overlapping IDs (append mode)",
      "status": "pass",
      "duration_ms": 4428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:07.417759+00:00",
      "read_cold_ms": 3104,
      "read_warm_ms": 700,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3277_update_partition_key",
      "num": 3277,
      "name": "update_partition_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3277_update_partition_key.py",
      "description": "UPDATE partition key column",
      "status": "pass",
      "duration_ms": 6421,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:13.839795+00:00",
      "read_cold_ms": 3948,
      "read_warm_ms": 1357,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3278_delete_reinsert_optimize",
      "num": 3278,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3278_delete_reinsert_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_3278_delete_reinsert_optimize.py",
      "description": "DELETE + re-INSERT + OPTIMIZE for compaction",
      "status": "pass",
      "duration_ms": 4988,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:18.829459+00:00",
      "read_cold_ms": 2576,
      "read_warm_ms": 1086,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 225,
      "write_warm_ms": 227,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3279_merge_idempotent_rerun",
      "num": 3279,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3279_merge_idempotent_rerun.py",
      "description": "Same MERGE executed twice produces idempotent result",
      "status": "pass",
      "duration_ms": 5953,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:24.784688+00:00",
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      "read_warm_ms": 1599,
      "skip_feature": null,
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      "write_cold_ms": 105,
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        "type:integer",
        "type:string",
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        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/327_merge_generated_columns",
      "num": 327,
      "name": "merge_generated_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/327_merge_generated_columns.sql",
      "read_script": "generator/spark-reads-df/verify_327_merge_generated_columns.py",
      "description": "Tables with GENERATED columns (computed from other columns)",
      "status": "pass",
      "duration_ms": 3266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:51:47.642774+00:00",
      "read_cold_ms": 2079,
      "read_warm_ms": 537,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 24,
      "write_warm_ms": 18,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3280_update_decimal_precision",
      "num": 3280,
      "name": "update_decimal_precision",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3280_update_decimal_precision.py",
      "description": "UPDATE with DECIMAL arithmetic preserving precision",
      "status": "pass",
      "duration_ms": 5786,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:30.573162+00:00",
      "read_cold_ms": 3360,
      "read_warm_ms": 1382,
      "skip_feature": null,
      "skip_cause": null,
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      "write_warm_ms": 118,
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        "type:decimal",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3281_empty_table_all_features",
      "num": 3281,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3281_empty_table_all_features.py",
      "description": "Table with CDC, colmap, CHECK, DEFAULT, IDENTITY, partitioned -- but zero rows",
      "status": "pass",
      "duration_ms": 4472,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:35.047841+00:00",
      "read_cold_ms": 2557,
      "read_warm_ms": 865,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 10,
      "write_warm_ms": 13,
      "tags": [
        "type:integer",
        "type:string",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3282_single_row_all_ops",
      "num": 3282,
      "name": "single_row_all_ops",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3282_single_row_all_ops.sql",
      "read_script": "generator/spark-reads-df/verify_3282_single_row_all_ops.py",
      "description": "Single row through full DML cycle: INSERT, UPDATE, DELETE, re-INSERT",
      "status": "pass",
      "duration_ms": 6188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:41.238936+00:00",
      "read_cold_ms": 3514,
      "read_warm_ms": 1293,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 173,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3283_null_all_nullable_cols",
      "num": 3283,
      "name": "null_all_nullable_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3283_null_all_nullable_cols.sql",
      "read_script": "generator/spark-reads-df/verify_3283_null_all_nullable_cols.py",
      "description": "All nullable columns set to NULL",
      "status": "pass",
      "duration_ms": 4619,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:45.858992+00:00",
      "read_cold_ms": 2929,
      "read_warm_ms": 739,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
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      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3284_unicode_partition_key",
      "num": 3284,
      "name": "unicode_partition_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3284_unicode_partition_key.sql",
      "read_script": "generator/spark-reads-df/verify_3284_unicode_partition_key.py",
      "description": "Partition keys with city names (ASCII-safe partition dirs)",
      "status": "pass",
      "duration_ms": 4762,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:50.624523+00:00",
      "read_cold_ms": 3362,
      "read_warm_ms": 688,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 62,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/delta/3285_max_int_boundary",
      "num": 3285,
      "name": "max_int_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3285_max_int_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_3285_max_int_boundary.py",
      "description": "BIGINT boundary values including MIN, MAX, zero, +/-1",
      "status": "pass",
      "duration_ms": 4404,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:55.029835+00:00",
      "read_cold_ms": 2785,
      "read_warm_ms": 910,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 28,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3286_empty_string_values",
      "num": 3286,
      "name": "empty_string_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3286_empty_string_values.sql",
      "read_script": "generator/spark-reads-df/verify_3286_empty_string_values.py",
      "description": "Empty strings vs NULL strings -- empty strings must be preserved (not coerced to NULL)",
      "status": "pass",
      "duration_ms": 4447,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:09:59.478155+00:00",
      "read_cold_ms": 2870,
      "read_warm_ms": 583,
      "skip_feature": null,
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3287_decimal_max_precision",
      "num": 3287,
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3287_decimal_max_precision.py",
      "description": "DECIMAL(38,18) -- max precision decimal values",
      "status": "pass",
      "duration_ms": 4263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:10:03.743349+00:00",
      "read_cold_ms": 2635,
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      "skip_feature": null,
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        "scale:large-dataset",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3288_boolean_three_state",
      "num": 3288,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3288_boolean_three_state.sql",
      "read_script": "generator/spark-reads-df/verify_3288_boolean_three_state.py",
      "description": "BOOLEAN with TRUE, FALSE, and NULL values",
      "status": "pass",
      "duration_ms": 4499,
      "error": null,
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      "name": "dv_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
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      "write_script": "generator/df-sql/338_dv_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_338_dv_partitioned.py",
      "description": "DVs work within partitions",
      "status": "pass",
      "duration_ms": 3444,
      "error": null,
      "notes": null,
      "extras": {},
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3390_merge_large_source",
      "num": 3390,
      "name": "merge_large_source",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3390_merge_large_source.py",
      "description": "MERGE with source larger than target.",
      "status": "pass",
      "duration_ms": 5307,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:19:59.908204+00:00",
      "read_cold_ms": 3188,
      "read_warm_ms": 1104,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 30,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3391_merge_empty_source",
      "num": 3391,
      "name": "merge_empty_source",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3391_merge_empty_source.sql",
      "read_script": "generator/spark-reads-df/verify_3391_merge_empty_source.py",
      "description": "MERGE with 0-row source (no-op).",
      "status": "pass",
      "duration_ms": 4481,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:04.390857+00:00",
      "read_cold_ms": 2678,
      "read_warm_ms": 993,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 15,
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        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3392_merge_same_key_twice",
      "num": 3392,
      "name": "merge_same_key_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3392_merge_same_key_twice.sql",
      "read_script": "generator/spark-reads-df/verify_3392_merge_same_key_twice.py",
      "description": "MERGE with duplicate keys in source.",
      "status": "pass",
      "duration_ms": 5377,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:09.769920+00:00",
      "read_cold_ms": 3210,
      "read_warm_ms": 1072,
      "skip_feature": null,
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      "write_cold_ms": 41,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3393_merge_with_default_col",
      "num": 3393,
      "name": "merge_with_default_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3393_merge_with_default_col.sql",
      "read_script": "generator/spark-reads-df/verify_3393_merge_with_default_col.py",
      "description": "MERGE INSERT uses DEFAULT column value.",
      "status": "pass",
      "duration_ms": 4396,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:14.168509+00:00",
      "read_cold_ms": 2877,
      "read_warm_ms": 739,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
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        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3394_merge_evolve_after",
      "num": 3394,
      "name": "merge_evolve_after",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3394_merge_evolve_after.sql",
      "read_script": "generator/spark-reads-df/verify_3394_merge_evolve_after.py",
      "description": "MERGE after schema evolution (ADD COLUMN).",
      "status": "pass",
      "duration_ms": 6416,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:20.585408+00:00",
      "read_cold_ms": 3786,
      "read_warm_ms": 1097,
      "skip_feature": null,
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      "write_cold_ms": 38,
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        "type:string",
        "dml:insert",
        "dml:merge",
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        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3395_merge_constraint_respected",
      "num": 3395,
      "name": "merge_constraint_respected",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3395_merge_constraint_respected.sql",
      "read_script": "generator/spark-reads-df/verify_3395_merge_constraint_respected.py",
      "description": "MERGE respects CHECK constraint.",
      "status": "pass",
      "duration_ms": 5991,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:26.578872+00:00",
      "read_cold_ms": 3802,
      "read_warm_ms": 1025,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
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        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3396_merge_five_rounds_accum",
      "num": 3396,
      "name": "merge_five_rounds_accum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3396_merge_five_rounds_accum.sql",
      "read_script": "generator/spark-reads-df/verify_3396_merge_five_rounds_accum.py",
      "description": "5 rounds of MERGE accumulating rows.",
      "status": "pass",
      "duration_ms": 6152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:32.732940+00:00",
      "read_cold_ms": 3415,
      "read_warm_ms": 1455,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 184,
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      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3397_merge_widen_then_merge",
      "num": 3397,
      "name": "merge_widen_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3397_merge_widen_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3397_merge_widen_then_merge.py",
      "description": "MERGE after column type widening (INT -> BIGINT).",
      "status": "pass",
      "duration_ms": 12532,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:20:45.268136+00:00",
      "read_cold_ms": 3629,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3398_merge_struct_update",
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      "name": "merge_struct_update",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3398_merge_struct_update.sql",
      "read_script": "generator/spark-reads-df/verify_3398_merge_struct_update.py",
      "description": "MERGE updating struct column.",
      "status": "pass",
      "duration_ms": 8489,
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      "notes": null,
      "extras": {},
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      "read_cold_ms": 4302,
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      "write_cold_ms": 31,
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      "tags": [
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        "type:string",
        "type:struct",
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        "dml:merge",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/3399_merge_boolean_predicate",
      "num": 3399,
      "name": "merge_boolean_predicate",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3399_merge_boolean_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_3399_merge_boolean_predicate.py",
      "description": "MERGE with boolean in join/predicate.",
      "status": "pass",
      "duration_ms": 6739,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:00.499949+00:00",
      "read_cold_ms": 3739,
      "read_warm_ms": 1635,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
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        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/339_dv_read_compat",
      "num": 339,
      "name": "dv_read_compat",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/339_dv_read_compat.sql",
      "read_script": "generator/spark-reads-df/verify_339_dv_read_compat.py",
      "description": "DeltaForge reads DBX-created DVs correctly",
      "status": "pass",
      "duration_ms": 3114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:52:41.741354+00:00",
      "read_cold_ms": 1306,
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      "write_cold_ms": 24,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
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        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/33_dv_storage_relative_path_prefixed",
      "num": 33,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/33_dv_storage_relative_path_prefixed.sql",
      "read_script": "generator/spark-reads-df/verify_33_dv_storage_relative_path_prefixed.py",
      "description": "Demonstrates deletion vectors stored with relative path (storageType: \"p\"). The random prefix distributes DVs across subdirectories for better performance.",
      "status": "pass",
      "duration_ms": 4352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:52:46.094458+00:00",
      "read_cold_ms": 2914,
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      "write_cold_ms": 239,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/3400_merge_null_safe_join",
      "num": 3400,
      "name": "merge_null_safe_join",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3400_merge_null_safe_join.sql",
      "read_script": "generator/spark-reads-df/verify_3400_merge_null_safe_join.py",
      "description": "MERGE with NULLs in join key.",
      "status": "pass",
      "duration_ms": 5727,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:21:06.230738+00:00",
      "read_cold_ms": 3181,
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3401_partition_null_key_value",
      "num": 3401,
      "name": "partition_null_key_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3401_partition_null_key_value.sql",
      "read_script": "generator/spark-reads-df/verify_3401_partition_null_key_value.py",
      "description": "NULL partition key value -- HIVE default partition handling",
      "status": "pass",
      "duration_ms": 4287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:10.521800+00:00",
      "read_cold_ms": 2746,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/3402_partition_multi_col",
      "num": 3402,
      "name": "partition_multi_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3402_partition_multi_col.sql",
      "read_script": "generator/spark-reads-df/verify_3402_partition_multi_col.py",
      "description": "Multi-column partitioning (region + year)",
      "status": "pass",
      "duration_ms": 4500,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:15.023530+00:00",
      "read_cold_ms": 3067,
      "read_warm_ms": 761,
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      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3403_partition_boolean_key",
      "num": 3403,
      "name": "partition_boolean_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3403_partition_boolean_key.sql",
      "read_script": "generator/spark-reads-df/verify_3403_partition_boolean_key.py",
      "description": "BOOLEAN partition key with true/false/NULL values",
      "status": "pass",
      "duration_ms": 4339,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:19.363354+00:00",
      "read_cold_ms": 2486,
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      "skip_feature": null,
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      "write_cold_ms": 30,
      "write_warm_ms": 38,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3404_partition_int_key",
      "num": 3404,
      "name": "partition_int_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3404_partition_int_key.sql",
      "read_script": "generator/spark-reads-df/verify_3404_partition_int_key.py",
      "description": "INT partition key with 10 buckets",
      "status": "pass",
      "duration_ms": 4321,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:23.687252+00:00",
      "read_cold_ms": 2741,
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      "tags": [
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        "dml:insert",
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        "delta:partitioning",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3405_partition_date_key",
      "num": 3405,
      "name": "partition_date_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3405_partition_date_key.sql",
      "read_script": "generator/spark-reads-df/verify_3405_partition_date_key.py",
      "description": "DATE partition key with 5 distinct dates",
      "status": "pass",
      "duration_ms": 3989,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:27.678763+00:00",
      "read_cold_ms": 2411,
      "read_warm_ms": 765,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 105,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3406_partition_delete_one",
      "num": 3406,
      "name": "partition_delete_one",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3406_partition_delete_one.sql",
      "read_script": "generator/spark-reads-df/verify_3406_partition_delete_one.py",
      "description": "DELETE entire partition",
      "status": "pass",
      "duration_ms": 4364,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:32.044644+00:00",
      "read_cold_ms": 2758,
      "read_warm_ms": 715,
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      "write_cold_ms": 70,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3407_partition_update_within",
      "num": 3407,
      "name": "partition_update_within",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3407_partition_update_within.sql",
      "read_script": "generator/spark-reads-df/verify_3407_partition_update_within.py",
      "description": "UPDATE within a single partition",
      "status": "pass",
      "duration_ms": 5536,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:37.582471+00:00",
      "read_cold_ms": 3276,
      "read_warm_ms": 1098,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 66,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3408_partition_merge_cross",
      "num": 3408,
      "name": "partition_merge_cross",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3408_partition_merge_cross.sql",
      "read_script": "generator/spark-reads-df/verify_3408_partition_merge_cross.py",
      "description": "MERGE across partitions",
      "status": "pass",
      "duration_ms": 5045,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:42.629015+00:00",
      "read_cold_ms": 3040,
      "read_warm_ms": 1106,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 92,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3409_partition_optimize_per",
      "num": 3409,
      "name": "partition_optimize_per",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3409_partition_optimize_per.sql",
      "read_script": "generator/spark-reads-df/verify_3409_partition_optimize_per.py",
      "description": "OPTIMIZE on partitioned table after multiple inserts",
      "status": "pass",
      "duration_ms": 2824,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:45.453983+00:00",
      "read_cold_ms": 1958,
      "read_warm_ms": 351,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 206,
      "write_warm_ms": 363,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/340_dv_write_compat",
      "num": 340,
      "name": "dv_write_compat",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/340_dv_write_compat.sql",
      "read_script": "generator/spark-reads-df/verify_340_dv_write_compat.py",
      "description": "DBX reads DeltaForge-created DVs",
      "status": "pass",
      "duration_ms": 3421,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:52:49.516494+00:00",
      "read_cold_ms": 2328,
      "read_warm_ms": 549,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 47,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3410_partition_vacuum_selective",
      "num": 3410,
      "name": "partition_vacuum_selective",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3410_partition_vacuum_selective.sql",
      "read_script": "generator/spark-reads-df/verify_3410_partition_vacuum_selective.py",
      "description": "VACUUM after partition delete",
      "status": "pass",
      "duration_ms": 1269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:46.724064+00:00",
      "read_cold_ms": 751,
      "read_warm_ms": 248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3411_partition_restore_full",
      "num": 3411,
      "name": "partition_restore_full",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3411_partition_restore_full.sql",
      "read_script": "generator/spark-reads-df/verify_3411_partition_restore_full.py",
      "description": "RESTORE partitioned table to prior version",
      "status": "pass",
      "duration_ms": 1077,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:47.801429+00:00",
      "read_cold_ms": 653,
      "read_warm_ms": 180,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 67,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3412_partition_time_travel",
      "num": 3412,
      "name": "partition_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3412_partition_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_3412_partition_time_travel.py",
      "description": "Time travel on partitioned table",
      "status": "pass",
      "duration_ms": 1669,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:49.471068+00:00",
      "read_cold_ms": 735,
      "read_warm_ms": 150,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3413_partition_stats_pushdown",
      "num": 3413,
      "name": "partition_stats_pushdown",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3413_partition_stats_pushdown.sql",
      "read_script": "generator/spark-reads-df/verify_3413_partition_stats_pushdown.py",
      "description": "Stats on partitioned table (400 rows, 100 per region)",
      "status": "pass",
      "duration_ms": 1316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:50.787491+00:00",
      "read_cold_ms": 693,
      "read_warm_ms": 166,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 33,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3414_partition_cdc_per_partition",
      "num": 3414,
      "name": "partition_cdc_per_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3414_partition_cdc_per_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3414_partition_cdc_per_partition.py",
      "description": "CDC per partition -- CDF has update records for US only",
      "status": "pass",
      "duration_ms": 1483,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:52.271504+00:00",
      "read_cold_ms": 810,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3415_partition_identity_unique",
      "num": 3415,
      "name": "partition_identity_unique",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3415_partition_identity_unique.sql",
      "read_script": "generator/spark-reads-df/verify_3415_partition_identity_unique.py",
      "description": "IDENTITY column across partitions",
      "status": "pass",
      "duration_ms": 947,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:53.219043+00:00",
      "read_cold_ms": 606,
      "read_warm_ms": 176,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3416_partition_colmap_names",
      "num": 3416,
      "name": "partition_colmap_names",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3416_partition_colmap_names.sql",
      "read_script": "generator/spark-reads-df/verify_3416_partition_colmap_names.py",
      "description": "Column mapping (name mode) on partitioned table",
      "status": "pass",
      "duration_ms": 1125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:54.344497+00:00",
      "read_cold_ms": 726,
      "read_warm_ms": 224,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 41,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3417_partition_constraint_per",
      "num": 3417,
      "name": "partition_constraint_per",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3417_partition_constraint_per.sql",
      "read_script": "generator/spark-reads-df/verify_3417_partition_constraint_per.py",
      "description": "CHECK constraint on partitioned table",
      "status": "pass",
      "duration_ms": 1083,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:55.429469+00:00",
      "read_cold_ms": 675,
      "read_warm_ms": 188,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3418_partition_widen_col",
      "num": 3418,
      "name": "partition_widen_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3418_partition_widen_col.sql",
      "read_script": "generator/spark-reads-df/verify_3418_partition_widen_col.py",
      "description": "Widen column type on partitioned table (INT -> BIGINT)",
      "status": "pass",
      "duration_ms": 1223,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:56.653631+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 196,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 35,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3419_partition_dv_per_partition",
      "num": 3419,
      "name": "partition_dv_per_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3419_partition_dv_per_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3419_partition_dv_per_partition.py",
      "description": "Deletion vectors per partition -- DELETE only in US partition",
      "status": "pass",
      "duration_ms": 1391,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:58.045067+00:00",
      "read_cold_ms": 844,
      "read_warm_ms": 320,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/341_dv_roaring_format",
      "num": 341,
      "name": "dv_roaring_format",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/341_dv_roaring_format.sql",
      "read_script": "generator/spark-reads-df/verify_341_dv_roaring_format.py",
      "description": "Verify DV uses correct roaring bitmap format",
      "status": "pass",
      "duration_ms": 3313,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:52:52.830083+00:00",
      "read_cold_ms": 1915,
      "read_warm_ms": 753,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 33,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3420_partition_evolve_add",
      "num": 3420,
      "name": "partition_evolve_add",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3420_partition_evolve_add.sql",
      "read_script": "generator/spark-reads-df/verify_3420_partition_evolve_add.py",
      "description": "ADD COLUMN on partitioned table",
      "status": "pass",
      "duration_ms": 1305,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:21:59.350736+00:00",
      "read_cold_ms": 933,
      "read_warm_ms": 188,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 70,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3421_time_travel_ten_versions",
      "num": 3421,
      "name": "time_travel_ten_versions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3421_time_travel_ten_versions.sql",
      "read_script": "generator/spark-reads-df/verify_3421_time_travel_ten_versions.py",
      "description": "Time travel across 10 INSERT versions (cumulative 10,20,...,100 rows)",
      "status": "pass",
      "duration_ms": 2670,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:02.021883+00:00",
      "read_cold_ms": 899,
      "read_warm_ms": 204,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 332,
      "write_warm_ms": 253,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3422_time_travel_after_update",
      "num": 3422,
      "name": "time_travel_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3422_time_travel_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_3422_time_travel_after_update.py",
      "description": "Time travel before UPDATE. V1=original values, current=doubled.",
      "status": "pass",
      "duration_ms": 2163,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:04.189102+00:00",
      "read_cold_ms": 807,
      "read_warm_ms": 350,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3423_time_travel_after_delete",
      "num": 3423,
      "name": "time_travel_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3423_time_travel_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3423_time_travel_after_delete.py",
      "description": "Time travel before DELETE. V1=50 rows, current=30 rows.",
      "status": "pass",
      "duration_ms": 2439,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:06.628909+00:00",
      "read_cold_ms": 936,
      "read_warm_ms": 263,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3424_time_travel_after_merge",
      "num": 3424,
      "name": "time_travel_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3424_time_travel_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3424_time_travel_after_merge.py",
      "description": "Time travel before MERGE. V1=50 rows, current=70 rows.",
      "status": "pass",
      "duration_ms": 2797,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:09.426329+00:00",
      "read_cold_ms": 879,
      "read_warm_ms": 471,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3425_time_travel_after_schema_evolve",
      "num": 3425,
      "name": "time_travel_after_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3425_time_travel_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_3425_time_travel_after_schema_evolve.py",
      "description": "Time travel across schema evolution. V1=50 rows no tag, current=100 rows with tag.",
      "status": "pass",
      "duration_ms": 1892,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:11.319296+00:00",
      "read_cold_ms": 763,
      "read_warm_ms": 328,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3426_time_travel_cdc_version",
      "num": 3426,
      "name": "time_travel_cdc_version",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3426_time_travel_cdc_version.sql",
      "read_script": "generator/spark-reads-df/verify_3426_time_travel_cdc_version.py",
      "description": "Time travel on CDC-enabled table. V1=50 rows, current=20 rows.",
      "status": "pass",
      "duration_ms": 3386,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:14.706152+00:00",
      "read_cold_ms": 1513,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 34,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3427_time_travel_partition",
      "num": 3427,
      "name": "time_travel_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3427_time_travel_partition.sql",
      "read_script": "generator/spark-reads-df/verify_3427_time_travel_partition.py",
      "description": "Time travel on partitioned table. V1=80 rows 4 regions, current=60 rows after US delete.",
      "status": "pass",
      "duration_ms": 2092,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:16.798789+00:00",
      "read_cold_ms": 727,
      "read_warm_ms": 184,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 62,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3428_restore_by_version",
      "num": 3428,
      "name": "restore_by_version",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3428_restore_by_version.sql",
      "read_script": "generator/spark-reads-df/verify_3428_restore_by_version.py",
      "description": "RESTORE to version 1 (after first INSERT). Rolls back UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 1153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:17.952889+00:00",
      "read_cold_ms": 749,
      "read_warm_ms": 187,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 61,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3429_restore_then_dml",
      "num": 3429,
      "name": "restore_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3429_restore_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_3429_restore_then_dml.py",
      "description": "DML after RESTORE. Restore to v1, then INSERT 30 more rows.",
      "status": "pass",
      "duration_ms": 1118,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:19.072027+00:00",
      "read_cold_ms": 739,
      "read_warm_ms": 161,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/342_dv_zorder",
      "num": 342,
      "name": "dv_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/342_dv_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_342_dv_zorder.py",
      "description": "Z-ORDER materializes DVs during rewrite",
      "status": "pass",
      "duration_ms": 4455,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:52:57.285995+00:00",
      "read_cold_ms": 2755,
      "read_warm_ms": 884,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 142,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3430_restore_then_merge",
      "num": 3430,
      "name": "restore_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3430_restore_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3430_restore_then_merge.py",
      "description": "MERGE after RESTORE. Restore to v1, then MERGE 30 new rows.",
      "status": "pass",
      "duration_ms": 1187,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:20.260021+00:00",
      "read_cold_ms": 802,
      "read_warm_ms": 197,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3431_restore_cdc_table",
      "num": 3431,
      "name": "restore_cdc_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3431_restore_cdc_table.sql",
      "read_script": "generator/spark-reads-df/verify_3431_restore_cdc_table.py",
      "description": "RESTORE on CDC-enabled table. Restore to v1 (original 50 rows).",
      "status": "pass",
      "duration_ms": 1089,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:21.350818+00:00",
      "read_cold_ms": 652,
      "read_warm_ms": 195,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
      "write_warm_ms": 55,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3432_restore_after_optimize",
      "num": 3432,
      "name": "restore_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3432_restore_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_3432_restore_after_optimize.py",
      "description": "RESTORE to pre-OPTIMIZE version. 10 inserts of 10 rows each, OPTIMIZE, restore to v5.",
      "status": "pass",
      "duration_ms": 1416,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:22.767704+00:00",
      "read_cold_ms": 967,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 406,
      "write_warm_ms": 289,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3433_restore_twice",
      "num": 3433,
      "name": "restore_twice",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3433_restore_twice.sql",
      "read_script": "generator/spark-reads-df/verify_3433_restore_twice.py",
      "description": "Two sequential RESTOREs. v1=50 rows, v2=100, v3=150. Restore to v2, then v1.",
      "status": "pass",
      "duration_ms": 1051,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:23.819705+00:00",
      "read_cold_ms": 644,
      "read_warm_ms": 204,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3434_time_travel_version_zero",
      "num": 3434,
      "name": "time_travel_version_zero",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3434_time_travel_version_zero.sql",
      "read_script": "generator/spark-reads-df/verify_3434_time_travel_version_zero.py",
      "description": "Reading version 0 (schema only, 0 rows).",
      "status": "pass",
      "duration_ms": 4401,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:28.221507+00:00",
      "read_cold_ms": 1299,
      "read_warm_ms": 886,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3435_restore_identity_hwm",
      "num": 3435,
      "name": "restore_identity_hwm",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3435_restore_identity_hwm.sql",
      "read_script": "generator/spark-reads-df/verify_3435_restore_identity_hwm.py",
      "description": "IDENTITY high-water mark after RESTORE. Restore to v1, insert 20 more rows.",
      "status": "pass",
      "duration_ms": 3106,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:31.329230+00:00",
      "read_cold_ms": 2008,
      "read_warm_ms": 539,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 158,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3436_restore_colmap_table",
      "num": 3436,
      "name": "restore_colmap_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3436_restore_colmap_table.sql",
      "read_script": "generator/spark-reads-df/verify_3436_restore_colmap_table.py",
      "description": "RESTORE on column-mapped table. Restore to v1 (original values).",
      "status": "pass",
      "duration_ms": 2147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:33.476733+00:00",
      "read_cold_ms": 1688,
      "read_warm_ms": 262,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3437_restore_dv_table",
      "num": 3437,
      "name": "restore_dv_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3437_restore_dv_table.sql",
      "read_script": "generator/spark-reads-df/verify_3437_restore_dv_table.py",
      "description": "RESTORE on DV-enabled table. Restore to v1 (all 50 rows).",
      "status": "pass",
      "duration_ms": 1689,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:35.166677+00:00",
      "read_cold_ms": 1078,
      "read_warm_ms": 246,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 23,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3438_time_travel_colmap",
      "num": 3438,
      "name": "time_travel_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3438_time_travel_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_3438_time_travel_colmap.py",
      "description": "Time travel on column-mapped table. V1=names 'tt_*', current='up_*'.",
      "status": "pass",
      "duration_ms": 5152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:40.319738+00:00",
      "read_cold_ms": 1970,
      "read_warm_ms": 616,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3439_time_travel_identity",
      "num": 3439,
      "name": "time_travel_identity",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3439_time_travel_identity.sql",
      "read_script": "generator/spark-reads-df/verify_3439_time_travel_identity.py",
      "description": "Time travel on IDENTITY table. V1=50 rows, current=100 rows.",
      "status": "pass",
      "duration_ms": 5407,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:45.728320+00:00",
      "read_cold_ms": 1921,
      "read_warm_ms": 628,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/343_dv_merge",
      "num": 343,
      "name": "dv_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/343_dv_merge.sql",
      "read_script": "generator/spark-reads-df/verify_343_dv_merge.py",
      "description": "MERGE UPDATE clause creates DVs",
      "status": "pass",
      "duration_ms": 3611,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:53:00.898236+00:00",
      "read_cold_ms": 1976,
      "read_warm_ms": 663,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 24,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3440_time_travel_widen",
      "num": 3440,
      "name": "time_travel_widen",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3440_time_travel_widen.sql",
      "read_script": "generator/spark-reads-df/verify_3440_time_travel_widen.py",
      "description": "Time travel across type widening. V1=50 rows INT val, current=100 rows BIGINT val.",
      "status": "pass",
      "duration_ms": 4202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:22:49.933046+00:00",
      "read_cold_ms": 1648,
      "read_warm_ms": 658,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3441_constraint_multi_col_check",
      "num": 3441,
      "name": "constraint_multi_col_check",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3441_constraint_multi_col_check.sql",
      "read_script": "generator/spark-reads-df/verify_3441_constraint_multi_col_check.py",
      "description": "Multi-column CHECK constraint (end_val > start_val).",
      "status": "pass",
      "duration_ms": 2921,
      "error": null,
      "notes": null,
      "extras": {},
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      "manual_tags": [],
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      "manual_tags": [],
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    {
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      "duration_ms": 3336,
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      "extras": {},
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      "manual_tags": [],
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    {
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      "duration_ms": 3059,
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      "manual_tags": [],
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    {
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      "description": "ADD COLUMN with DEFAULT, then insert with explicit values.",
      "status": "pass",
      "duration_ms": 2767,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3457_constraint_cdc_violation_log",
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      "write_script": "generator/df-sql/3457_constraint_cdc_violation_log.sql",
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      "manual_tags": [],
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      "description": "CHECK constraint on partitioned table.",
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      "duration_ms": 2361,
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      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/3459_default_cdc_record",
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      "description": "DEFAULT + CDC. status='new' via default, CDF insert records.",
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      "duration_ms": 2493,
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/345_dv_inline_vs_file",
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      "reader": "spark",
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      "description": "Small DV stored inline, large DV in file",
      "status": "pass",
      "duration_ms": 4505,
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        "type:integer",
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        "dml:insert",
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        "type:integer",
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        "format:delta"
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        "type:integer",
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        "type:boolean",
        "type:floating",
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      "manual_tags": [],
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        "direction:df-writes",
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      "description": "ICT across 5 INSERTs + OPTIMIZE + 1 INSERT.",
      "status": "pass",
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        "dml:insert",
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        "delta:optimize",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
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      "name": "ict_three_features",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3509_ict_three_features.py",
      "description": "ICT + CDC + rowTracking + INSERT 100 + UPDATE 30.",
      "status": "pass",
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      "error": null,
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      "extras": {},
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        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:row-tracking",
        "direction:df-writes",
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      "manual_tags": [],
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/350_time_travel_timestamp.sql",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "name": "ict_with_restore",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3511_ict_with_restore.py",
      "description": "ICT + RESTORE preserves commit timestamps.",
      "status": "pass",
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        "dml:insert",
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        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3512_ict_with_vacuum",
      "num": 3512,
      "name": "ict_with_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
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        "type:integer",
        "dml:delete",
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        "format:delta"
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      "id": "df-writes/delta/3513_ict_with_evolve",
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
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    },
    {
      "id": "df-writes/delta/3514_ict_with_widen",
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
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    },
    {
      "id": "df-writes/delta/3515_ict_with_colmap",
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3516_ict_with_identity",
      "num": 3516,
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        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/3517_ict_with_constraint",
      "num": 3517,
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        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/3518_ict_with_default",
      "num": 3518,
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      "format": "delta",
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      "reader": "spark",
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        "type:integer",
        "dml:insert",
        "delta:default-values",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/3519_ict_with_partition",
      "num": 3519,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "skip_feature": null,
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        "type:integer",
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        "dml:insert",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/351_time_travel_first",
      "num": 351,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/3520_ict_with_dv_cdc",
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        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
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        "delta:change-data-feed",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3521_ict_many_columns",
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      "read_cold_ms": 1934,
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      "skip_feature": null,
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        "type:boolean",
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      "manual_tags": [],
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      "id": "df-writes/delta/3522_ict_string_key",
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        "type:date",
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      "id": "df-writes/delta/3525_generated_day_from_date",
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      "id": "df-writes/delta/3526_generated_expr_plus",
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      "id": "df-writes/delta/3527_generated_expr_mult",
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      "id": "df-writes/delta/3551_identity_colmap_cdc_partition_constraint",
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      "reader": "spark",
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    {
      "id": "df-writes/delta/3614_delete_where_not_in",
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      "id": "df-writes/delta/3650_update_col_from_another_col",
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      "description": "Scale stress -- partitioned table with 2000 rows across 4 regions.",
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      "description": "Scale stress -- INSERT 1000, DELETE 500, OPTIMIZE.",
      "status": "pass",
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      "error": null,
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    },
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      ],
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    {
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      "description": "Scale stress -- 30 sequential inserts of 10 rows each (triggers checkpoints).",
      "status": "pass",
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    {
      "id": "df-writes/delta/3672_scale_concurrent_dml_simulated",
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      "description": "Scale stress -- 10 alternating INSERT / UPDATE / DELETE operations.",
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      "duration_ms": 5027,
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      "description": "Scale stress -- skewed distribution: 950 rows with val=1, 50 rows with val=i.",
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      "read_cold_ms": 2894,
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      "reader": "spark",
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      "description": "Scale stress -- 2000 rows, every tag distinct.",
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      "read_cold_ms": 2237,
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    {
      "id": "df-writes/delta/3676_scale_date_range",
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      "reader": "spark",
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      "description": "Scale stress -- 500 rows with dates spanning 5 years (approx. one every 3-4 days).",
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      "read_cold_ms": 2473,
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      "tags": [
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      "id": "df-writes/delta/3677_scale_mixed_types",
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      "reader": "spark",
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      "read_cold_ms": 3046,
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        "type:boolean",
        "type:date",
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        "format:delta"
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      "id": "df-writes/delta/3678_scale_partition_null_heavy",
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      "description": "Scale stress -- partitioned by region with 500 rows; 100 have NULL region.",
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      "read_cold_ms": 2142,
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        "delta:deletion-vectors",
        "delta:partitioning",
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        "format:delta"
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      "manual_tags": [],
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    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Scale stress -- INSERT 1000, then UPDATE SET val=val+1000 WHERE id%10=0.",
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      "duration_ms": 4592,
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      "verified_at": "2026-05-19T17:38:59.397075+00:00",
      "read_cold_ms": 3127,
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      "write_cold_ms": 84,
      "write_warm_ms": 73,
      "tags": [
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      "manual_tags": [],
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      "id": "df-writes/delta/367_restore_after_update",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_367_restore_after_update.py",
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      "status": "pass",
      "duration_ms": 4249,
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      "extras": {},
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      "tags": [
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3680_scale_delete_every_7",
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      "name": "scale_delete_every_7",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3680_scale_delete_every_7.py",
      "description": "Scale stress -- INSERT 700, then DELETE WHERE id%7=0.",
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      "duration_ms": 5291,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 2910,
      "read_warm_ms": 1245,
      "skip_feature": null,
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      "tags": [
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        "dml:delete",
        "dml:insert",
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        "format:delta"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/delta/3681_scale_merge_partition",
      "num": 3681,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3681_scale_merge_partition.py",
      "description": "Scale stress -- partitioned INSERT 800, MERGE (200 update + 100 insert).",
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      "duration_ms": 5524,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:10.214382+00:00",
      "read_cold_ms": 3653,
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      "skip_feature": null,
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      "write_cold_ms": 179,
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      "tags": [
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        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3682_scale_multi_version_reads",
      "num": 3682,
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3682_scale_multi_version_reads.py",
      "description": "Scale stress -- 10 sequential inserts of 100 rows each; each version readable.",
      "status": "pass",
      "duration_ms": 19131,
      "error": null,
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      "verified_at": "2026-05-19T17:39:29.348343+00:00",
      "read_cold_ms": 1998,
      "read_warm_ms": 680,
      "skip_feature": null,
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      "tags": [
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      "manual_tags": [],
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      "id": "df-writes/delta/3683_stats_int_minmax",
      "num": 3683,
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3683_stats_int_minmax.py",
      "description": "Stats -- INT min/max on 500 rows (val=i).",
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      "duration_ms": 3213,
      "error": null,
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      "read_cold_ms": 2040,
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      "tags": [
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3684_stats_bigint_minmax",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Stats -- BIGINT min/max across large range (500 rows, step of 1 billion).",
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      "extras": {},
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      "read_cold_ms": 2216,
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      "tags": [
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3685_stats_double_minmax",
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      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3685_stats_double_minmax.py",
      "description": "Stats -- DOUBLE min/max on 500 rows (val = i * 1.5).",
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      "error": null,
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      "extras": {},
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      "read_cold_ms": 2205,
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      "skip_feature": null,
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      "write_cold_ms": 40,
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      "tags": [
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        "dml:insert",
        "delta:deletion-vectors",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3686_stats_decimal_minmax",
      "num": 3686,
      "name": "stats_decimal_minmax",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3686_stats_decimal_minmax.sql",
      "read_script": "generator/spark-reads-df/verify_3686_stats_decimal_minmax.py",
      "description": "Stats -- DECIMAL(18,4) min/max on 500 rows (amount = i * 0.25).",
      "status": "pass",
      "duration_ms": 3360,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:39:42.860883+00:00",
      "read_cold_ms": 1785,
      "read_warm_ms": 917,
      "skip_feature": null,
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      "write_cold_ms": 32,
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      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3687_stats_string_minmax",
      "num": 3687,
      "name": "stats_string_minmax",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3687_stats_string_minmax.sql",
      "read_script": "generator/spark-reads-df/verify_3687_stats_string_minmax.py",
      "description": "Stats -- STRING lexicographic min/max (zero-padded ids).",
      "status": "pass",
      "duration_ms": 4031,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:46.894121+00:00",
      "read_cold_ms": 2289,
      "read_warm_ms": 704,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 27,
      "write_warm_ms": 30,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3688_stats_date_minmax",
      "num": 3688,
      "name": "stats_date_minmax",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3688_stats_date_minmax.sql",
      "read_script": "generator/spark-reads-df/verify_3688_stats_date_minmax.py",
      "description": "Stats -- DATE min/max (500 rows, one per day starting 2023-01-01).",
      "status": "pass",
      "duration_ms": 4388,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:51.284487+00:00",
      "read_cold_ms": 2818,
      "read_warm_ms": 933,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 46,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3689_stats_timestamp_minmax",
      "num": 3689,
      "name": "stats_timestamp_minmax",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3689_stats_timestamp_minmax.sql",
      "read_script": "generator/spark-reads-df/verify_3689_stats_timestamp_minmax.py",
      "description": "Stats -- TIMESTAMP min/max (500 rows, one per hour starting 2024-01-01 00:00:00).",
      "status": "pass",
      "duration_ms": 4079,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:55.364652+00:00",
      "read_cold_ms": 2739,
      "read_warm_ms": 671,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/368_restore_schema_change",
      "num": 368,
      "name": "restore_schema_change",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/368_restore_schema_change.sql",
      "read_script": "generator/spark-reads-df/verify_368_restore_schema_change.py",
      "description": "Schema changes during restore/time travel scenarios",
      "status": "pass",
      "duration_ms": 3635,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:54:37.584547+00:00",
      "read_cold_ms": 2322,
      "read_warm_ms": 626,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 55,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:time-travel",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3690_stats_null_column",
      "num": 3690,
      "name": "stats_null_column",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3690_stats_null_column.sql",
      "read_script": "generator/spark-reads-df/verify_3690_stats_null_column.py",
      "description": "Stats -- column that is entirely NULL (null_count should equal row_count).",
      "status": "pass",
      "duration_ms": 4369,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:39:59.735027+00:00",
      "read_cold_ms": 2870,
      "read_warm_ms": 714,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 31,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3691_stats_after_update",
      "num": 3691,
      "name": "stats_after_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3691_stats_after_update.sql",
      "read_script": "generator/spark-reads-df/verify_3691_stats_after_update.py",
      "description": "Stats -- INSERT 500 then UPDATE SET val=val+10000 WHERE id<=50.",
      "status": "pass",
      "duration_ms": 6118,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:05.854057+00:00",
      "read_cold_ms": 3755,
      "read_warm_ms": 1137,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3692_stats_after_delete",
      "num": 3692,
      "name": "stats_after_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3692_stats_after_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3692_stats_after_delete.py",
      "description": "Stats -- INSERT 500 then DELETE WHERE val<=10.",
      "status": "pass",
      "duration_ms": 6940,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:12.795029+00:00",
      "read_cold_ms": 3959,
      "read_warm_ms": 1466,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3693_stats_after_merge",
      "num": 3693,
      "name": "stats_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3693_stats_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_3693_stats_after_merge.py",
      "description": "Stats -- INSERT 500, MERGE (update last 100 + insert 100 new).",
      "status": "pass",
      "duration_ms": 5064,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:17.859950+00:00",
      "read_cold_ms": 2924,
      "read_warm_ms": 1136,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3694_stats_after_optimize",
      "num": 3694,
      "name": "stats_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3694_stats_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_3694_stats_after_optimize.py",
      "description": "Stats -- INSERT 500 then OPTIMIZE (compaction).",
      "status": "pass",
      "duration_ms": 3944,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:21.805608+00:00",
      "read_cold_ms": 2085,
      "read_warm_ms": 941,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3695_stats_boolean_col",
      "num": 3695,
      "name": "stats_boolean_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3695_stats_boolean_col.sql",
      "read_script": "generator/spark-reads-df/verify_3695_stats_boolean_col.py",
      "description": "Stats -- BOOLEAN column with mix of true/false/null (200 rows).",
      "status": "pass",
      "duration_ms": 4214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:26.021122+00:00",
      "read_cold_ms": 2871,
      "read_warm_ms": 584,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 41,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3696_stats_binary_col",
      "num": 3696,
      "name": "stats_binary_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3696_stats_binary_col.sql",
      "read_script": "generator/spark-reads-df/verify_3696_stats_binary_col.py",
      "description": "Stats -- BINARY column with 100 distinct binary values.",
      "status": "pass",
      "duration_ms": 4951,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:30.973198+00:00",
      "read_cold_ms": 3352,
      "read_warm_ms": 856,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 39,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3697_stats_partition_stats",
      "num": 3697,
      "name": "stats_partition_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3697_stats_partition_stats.sql",
      "read_script": "generator/spark-reads-df/verify_3697_stats_partition_stats.py",
      "description": "Stats -- PARTITIONED BY(region) with 400 rows, per-partition stats.",
      "status": "pass",
      "duration_ms": 4269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:35.243265+00:00",
      "read_cold_ms": 2679,
      "read_warm_ms": 757,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3698_stats_zorder_stats",
      "num": 3698,
      "name": "stats_zorder_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3698_stats_zorder_stats.sql",
      "read_script": "generator/spark-reads-df/verify_3698_stats_zorder_stats.py",
      "description": "Stats -- OPTIMIZE ZORDER BY(val) on 500 rows.",
      "status": "pass",
      "duration_ms": 3250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:38.496608+00:00",
      "read_cold_ms": 1949,
      "read_warm_ms": 618,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3699_stats_wide_column_stats",
      "num": 3699,
      "name": "stats_wide_column_stats",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3699_stats_wide_column_stats.sql",
      "read_script": "generator/spark-reads-df/verify_3699_stats_wide_column_stats.py",
      "description": "Stats -- 20-column table with stats collected per column. INSERT 200.",
      "status": "pass",
      "duration_ms": 4455,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:42.955048+00:00",
      "read_cold_ms": 2509,
      "read_warm_ms": 1074,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/369_restore_partitioned",
      "num": 369,
      "name": "restore_partitioned",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/369_restore_partitioned.sql",
      "read_script": "generator/spark-reads-df/verify_369_restore_partitioned.py",
      "description": "Partitioned table operations with DELETE and restore scenarios",
      "status": "pass",
      "duration_ms": 3114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:54:40.700227+00:00",
      "read_cold_ms": 1789,
      "read_warm_ms": 684,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 190,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/36_dv_binary_format_roaring",
      "num": 36,
      "name": "dv_binary_format_roaring",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/36_dv_binary_format_roaring.sql",
      "read_script": "generator/spark-reads-df/verify_36_dv_binary_format_roaring.py",
      "description": "Demonstrates deletion vector binary format using RoaringBitmap. DVs use portable RoaringBitmap serialization format (Croaring library). Stores row indices efficiently with run-length encoding.",
      "status": "pass",
      "duration_ms": 6270,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:54:46.971916+00:00",
      "read_cold_ms": 3176,
      "read_warm_ms": 564,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 134,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3700_stats_long_string_truncate",
      "num": 3700,
      "name": "stats_long_string_truncate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3700_stats_long_string_truncate.sql",
      "read_script": "generator/spark-reads-df/verify_3700_stats_long_string_truncate.py",
      "description": "Stats -- long strings (>1KB) should be truncated in stats min/max.",
      "status": "pass",
      "duration_ms": 4714,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:47.671411+00:00",
      "read_cold_ms": 3107,
      "read_warm_ms": 701,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 36,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3701_stats_special_double",
      "num": 3701,
      "name": "stats_special_double",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3701_stats_special_double.sql",
      "read_script": "generator/spark-reads-df/verify_3701_stats_special_double.py",
      "description": "Stats -- DOUBLE with NaN and Infinity mixed (50 rows).",
      "status": "pass",
      "duration_ms": 4185,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:51.857294+00:00",
      "read_cold_ms": 2765,
      "read_warm_ms": 743,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 45,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3702_stats_negative_range",
      "num": 3702,
      "name": "stats_negative_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3702_stats_negative_range.sql",
      "read_script": "generator/spark-reads-df/verify_3702_stats_negative_range.py",
      "description": "Stats -- INT column covering negative to positive range.",
      "status": "pass",
      "duration_ms": 4457,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:40:56.315923+00:00",
      "read_cold_ms": 2968,
      "read_warm_ms": 848,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 31,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3703_zorder_single_int",
      "num": 3703,
      "name": "zorder_single_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3703_zorder_single_int.sql",
      "read_script": "generator/spark-reads-df/verify_3703_zorder_single_int.py",
      "description": "Z-ORDER on a single INT column. INSERT 500 rows then OPTIMIZE ZORDER BY (val).",
      "status": "pass",
      "duration_ms": 4232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:00.550303+00:00",
      "read_cold_ms": 2031,
      "read_warm_ms": 841,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
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        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3704_zorder_single_bigint",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Z-ORDER on BIGINT column. INSERT 500 + OPTIMIZE ZORDER BY (val).",
      "status": "pass",
      "duration_ms": 4684,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:05.235344+00:00",
      "read_cold_ms": 2157,
      "read_warm_ms": 935,
      "skip_feature": null,
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      "write_cold_ms": 56,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
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        "scale:large-dataset",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3705_zorder_single_string",
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      "status": "pass",
      "duration_ms": 4846,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:10.084173+00:00",
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        "type:integer",
        "type:string",
        "dml:insert",
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        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "reader": "spark",
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      "description": "Z-ORDER on DOUBLE column. INSERT 500 + OPTIMIZE ZORDER BY (val).",
      "status": "pass",
      "duration_ms": 5279,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:15.368832+00:00",
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      "skip_feature": null,
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        "type:floating",
        "type:integer",
        "dml:insert",
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      ],
      "manual_tags": [],
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    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3772,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:19.142533+00:00",
      "read_cold_ms": 2443,
      "read_warm_ms": 578,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
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        "type:decimal",
        "type:integer",
        "dml:insert",
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        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3708_zorder_single_date",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_3708_zorder_single_date.py",
      "description": "Z-ORDER on DATE column. INSERT 300 + OPTIMIZE ZORDER BY (d).",
      "status": "pass",
      "duration_ms": 4496,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:23.640697+00:00",
      "read_cold_ms": 2702,
      "read_warm_ms": 696,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 42,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 3709,
      "name": "zorder_single_timestamp",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Z-ORDER on TIMESTAMP column. INSERT 300 + OPTIMIZE ZORDER BY (ts).",
      "status": "pass",
      "duration_ms": 4089,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:27.732454+00:00",
      "read_cold_ms": 2614,
      "read_warm_ms": 512,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
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      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/370_restore_after_merge",
      "num": 370,
      "name": "restore_after_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/370_restore_after_merge.sql",
      "read_script": "generator/spark-reads-df/verify_370_restore_after_merge.py",
      "description": "Testing RESTORE operation after MERGE operations.",
      "status": "pass",
      "duration_ms": 3161,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:54:50.133265+00:00",
      "read_cold_ms": 1675,
      "read_warm_ms": 578,
      "skip_feature": null,
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      "write_cold_ms": 83,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3710_zorder_two_cols",
      "num": 3710,
      "name": "zorder_two_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3710_zorder_two_cols.sql",
      "read_script": "generator/spark-reads-df/verify_3710_zorder_two_cols.py",
      "description": "Z-ORDER on 2 INT columns. INSERT 500 + OPTIMIZE ZORDER BY (val, bucket).",
      "status": "pass",
      "duration_ms": 4553,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:32.287504+00:00",
      "read_cold_ms": 2123,
      "read_warm_ms": 940,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3711_zorder_three_cols",
      "num": 3711,
      "name": "zorder_three_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3711_zorder_three_cols.sql",
      "read_script": "generator/spark-reads-df/verify_3711_zorder_three_cols.py",
      "description": "Z-ORDER on 3 columns. INSERT 500 + OPTIMIZE ZORDER BY (a, b, c).",
      "status": "pass",
      "duration_ms": 5256,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:37.545033+00:00",
      "read_cold_ms": 2754,
      "read_warm_ms": 1008,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3712_zorder_high_cardinality",
      "num": 3712,
      "name": "zorder_high_cardinality",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3712_zorder_high_cardinality.sql",
      "read_script": "generator/spark-reads-df/verify_3712_zorder_high_cardinality.py",
      "description": "Z-ORDER on high-cardinality INT. INSERT 2000 unique + OPTIMIZE ZORDER BY (val).",
      "status": "pass",
      "duration_ms": 3905,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:41.450597+00:00",
      "read_cold_ms": 2236,
      "read_warm_ms": 515,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3713_zorder_low_cardinality",
      "num": 3713,
      "name": "zorder_low_cardinality",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3713_zorder_low_cardinality.sql",
      "read_script": "generator/spark-reads-df/verify_3713_zorder_low_cardinality.py",
      "description": "Z-ORDER on low-cardinality INT (val IN 1..10). INSERT 500 + OPTIMIZE ZORDER BY (val).",
      "status": "pass",
      "duration_ms": 4119,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:45.571676+00:00",
      "read_cold_ms": 2101,
      "read_warm_ms": 800,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 51,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3714_zorder_after_insert_update_delete",
      "num": 3714,
      "name": "zorder_after_insert_update_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3714_zorder_after_insert_update_delete.sql",
      "read_script": "generator/spark-reads-df/verify_3714_zorder_after_insert_update_delete.py",
      "description": "Z-ORDER after INSERT/UPDATE/DELETE chain.",
      "status": "pass",
      "duration_ms": 4129,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:49.703422+00:00",
      "read_cold_ms": 2450,
      "read_warm_ms": 620,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3715_zorder_with_cdc_metadata",
      "num": 3715,
      "name": "zorder_with_cdc_metadata",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3715_zorder_with_cdc_metadata.sql",
      "read_script": "generator/spark-reads-df/verify_3715_zorder_with_cdc_metadata.py",
      "description": "Z-ORDER on table with CDC. INSERT 500 + OPTIMIZE ZORDER BY (val). CDF readable.",
      "status": "pass",
      "duration_ms": 4899,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:54.603737+00:00",
      "read_cold_ms": 2223,
      "read_warm_ms": 835,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3716_zorder_after_schema_evolve",
      "num": 3716,
      "name": "zorder_after_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3716_zorder_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_3716_zorder_after_schema_evolve.py",
      "description": "Z-ORDER after schema evolution.",
      "status": "pass",
      "duration_ms": 3513,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:41:58.118979+00:00",
      "read_cold_ms": 2296,
      "read_warm_ms": 567,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 131,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3717_zorder_null_heavy",
      "num": 3717,
      "name": "zorder_null_heavy",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3717_zorder_null_heavy.sql",
      "read_script": "generator/spark-reads-df/verify_3717_zorder_null_heavy.py",
      "description": "Z-ORDER on NULL-heavy column. INSERT 500 where id <= 200 have NULL val + ZORDER.",
      "status": "pass",
      "duration_ms": 4738,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:42:02.859399+00:00",
      "read_cold_ms": 2498,
      "read_warm_ms": 993,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3718_zorder_negative_range",
      "num": 3718,
      "name": "zorder_negative_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3718_zorder_negative_range.sql",
      "read_script": "generator/spark-reads-df/verify_3718_zorder_negative_range.py",
      "description": "Z-ORDER on negative INT range. INSERT 200 with val = -100..100.",
      "status": "pass",
      "duration_ms": 5075,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:42:07.935954+00:00",
      "read_cold_ms": 2900,
      "read_warm_ms": 845,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3719_zorder_boolean_col",
      "num": 3719,
      "name": "zorder_boolean_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3719_zorder_boolean_col.sql",
      "read_script": "generator/spark-reads-df/verify_3719_zorder_boolean_col.py",
      "description": "Z-ORDER on BOOLEAN column. INSERT 300 + OPTIMIZE ZORDER BY (flag).",
      "status": "pass",
      "duration_ms": 4996,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-19T17:42:12.933918+00:00",
      "read_cold_ms": 2893,
      "read_warm_ms": 744,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 70,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/371_restore_after_optimize",
      "num": 371,
      "name": "restore_after_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/371_restore_after_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_371_restore_after_optimize.py",
      "description": "Testing RESTORE operation after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 3312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:54:53.446582+00:00",
      "read_cold_ms": 2038,
      "read_warm_ms": 448,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2161,
      "write_warm_ms": 2331,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:time-travel",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/3720_zorder_after_widen",
      "num": 3720,
      "name": "zorder_after_widen",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/3720_zorder_after_widen.sql",
      "read_script": "generator/spark-reads-df/verify_3720_zorder_after_widen.py",
      "description": "Z-ORDER after type widening. INSERT 300 (INT) + ALTER widen val to BIGINT + ZORDER.",
      "status": "pass",
      "duration_ms": 4159,
      "error": null,
      "notes": null,
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        "type:integer",
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        "delta:deletion-vectors",
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        "delta:z-order",
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        "scale:wide-schema",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/3721_zorder_after_restore",
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        "type:integer",
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        "dml:insert",
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      ],
      "manual_tags": [],
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    },
    {
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      "duration_ms": 4424,
      "error": null,
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        "dml:insert",
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      "manual_tags": [],
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    },
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      "format": "delta",
      "writer": "delta-forge",
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      "duration_ms": 3845,
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      "description": "STRUCT with 5 mixed-type fields. INSERT 100.",
      "status": "pass",
      "duration_ms": 3644,
      "error": null,
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        "type:boolean",
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      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/delta/3725_struct_with_null",
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      "reader": "spark",
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      "status": "pass",
      "duration_ms": 3791,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-19T17:42:36.822130+00:00",
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/delta/3726_struct_with_decimal",
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      "read_script": "generator/spark-reads-df/verify_3726_struct_with_decimal.py",
      "description": "STRUCT<price:DECIMAL(18,2), qty:INT>. INSERT 100.",
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      "duration_ms": 4885,
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    {
      "id": "df-writes/delta/3727_struct_with_boolean",
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      "reader": "spark",
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      "description": "STRUCT<active:BOOLEAN, val:INT>. INSERT 100.",
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      "duration_ms": 4650,
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    {
      "id": "df-writes/delta/3728_struct_with_date",
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      "duration_ms": 4223,
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      ],
      "manual_tags": [],
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    {
      "id": "df-writes/delta/3729_struct_nested_two",
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      "duration_ms": 4837,
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      "writer": "delta-forge",
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      "write_script": "generator/df-sql/372_restore_creates_version.sql",
      "read_script": "generator/spark-reads-df/verify_372_restore_creates_version.py",
      "description": "Version tracking during restore operations",
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      "duration_ms": 2947,
      "error": null,
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      "verified_at": "2026-05-20T00:54:56.394723+00:00",
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      "manual_tags": [],
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    {
      "id": "df-writes/delta/3730_struct_nested_three",
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      "name": "struct_nested_three",
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      "format": "delta",
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      "reader": "spark",
      "write_script": "generator/df-sql/3730_struct_nested_three.sql",
      "read_script": "generator/spark-reads-df/verify_3730_struct_nested_three.py",
      "description": "3-level nested STRUCT. INSERT 30.",
      "status": "pass",
      "duration_ms": 3708,
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      "reader": "spark",
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      "description": "UPDATE entire STRUCT value. INSERT 50 + UPDATE SET data=named_struct(...).",
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      "duration_ms": 6537,
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    {
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      "reader": "spark",
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      "description": "ARRAY<STRING> with 3 elements per row. INSERT 50.",
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      "duration_ms": 4446,
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    {
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      "type": "df-writes",
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      "reader": "spark",
      "write_script": "generator/df-sql/3734_array_of_arrays.sql",
      "read_script": "generator/spark-reads-df/verify_3734_array_of_arrays.py",
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    {
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      "read_script": "generator/spark-reads-df/verify_3735_array_empty_mixed.py",
      "description": "Mix of empty and non-empty ARRAY<INT>. INSERT 50 (even ids: empty).",
      "status": "pass",
      "duration_ms": 4273,
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      "num": 3736,
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    {
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    },
    {
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    },
    {
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    },
    {
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:57:45.493044+00:00",
      "read_cold_ms": 2332,
      "read_warm_ms": 433,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 6774,
      "write_warm_ms": 5544,
      "tags": [
        "type:array",
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/387_concurrent_optimize",
      "num": 387,
      "name": "concurrent_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/387_concurrent_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_387_concurrent_optimize.py",
      "description": "Demonstrates OPTIMIZE while both engines write: 1. Generator creates fragmented table 2. DeltaForge INSERT runs concurrently 3. DeltaForge OPTIMIZE runs 4. DBX verifies no conflicts, both succeed",
      "status": "pass",
      "duration_ms": 9349,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:57:54.843685+00:00",
      "read_cold_ms": 3186,
      "read_warm_ms": 669,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 17,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/388_concurrent_vacuum",
      "num": 388,
      "name": "concurrent_vacuum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/388_concurrent_vacuum.sql",
      "read_script": "generator/spark-reads-df/verify_388_concurrent_vacuum.py",
      "description": "Demonstrates VACUUM while both engines write: 1. Generator creates table with history 2. Active writes from both engines 3. DeltaForge VACUUM runs 4. All operations should succeed",
      "status": "pass",
      "duration_ms": 6718,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:58:01.563487+00:00",
      "read_cold_ms": 2476,
      "read_warm_ms": 592,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 17,
      "write_warm_ms": 15,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:vacuum",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/389_concurrent_zorder",
      "num": 389,
      "name": "concurrent_zorder",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/389_concurrent_zorder.sql",
      "read_script": "generator/spark-reads-df/verify_389_concurrent_zorder.py",
      "description": "Demonstrates Z-ORDER while both engines write: 1. Generator creates table with data for Z-ORDER 2. Table being actively written to 3. Z-ORDER operation from DeltaForge 4. Both should complete successfully",
      "status": "pass",
      "duration_ms": 7274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:58:08.840566+00:00",
      "read_cold_ms": 1697,
      "read_warm_ms": 589,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 22,
      "write_warm_ms": 29,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:z-order",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/38_iceberg_compat_v1_uniform",
      "num": 38,
      "name": "iceberg_compat_v1_uniform",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/38_iceberg_compat_v1_uniform.sql",
      "read_script": "generator/spark-reads-df/verify_38_iceberg_compat_v1_uniform.py",
      "description": "Demonstrates Iceberg compatibility V1 enabling reading Delta tables as Iceberg tables. Writes Iceberg metadata alongside Delta metadata for universal data lake access.",
      "status": "pass",
      "duration_ms": 4747,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:58:13.591104+00:00",
      "read_cold_ms": 1965,
      "read_warm_ms": 308,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
      "write_warm_ms": 110,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/390_interleaved_maintenance",
      "num": 390,
      "name": "interleaved_maintenance",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/390_interleaved_maintenance.sql",
      "read_script": "generator/spark-reads-df/verify_390_interleaved_maintenance.py",
      "description": "Demonstrates interleaved maintenance operations between engines: 1. DBX creates table and performs OPTIMIZE 2. DeltaForge INSERT 3. DeltaForge VACUUM 4. DBX INSERT 5. All history preserved correctly",
      "status": "pass",
      "duration_ms": 5708,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:58:19.299733+00:00",
      "read_cold_ms": 2097,
      "read_warm_ms": 425,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 19,
      "write_warm_ms": 15,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:optimize",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/391_coordinated_comprehensive",
      "num": 391,
      "name": "coordinated_comprehensive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/391_coordinated_comprehensive.sql",
      "read_script": "generator/spark-reads-df/verify_391_coordinated_comprehensive.py",
      "description": "Demonstrates full coordinated commits scenario: 1. Both engines actively writing 2. Maintenance operations from both 3. Conflict resolution validation 4. Full history and CDC verification",
      "status": "pass",
      "duration_ms": 19861,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:58:39.161517+00:00",
      "read_cold_ms": 2915,
      "read_warm_ms": 874,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 262,
      "write_warm_ms": 110,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/392_dv_cdc_delete",
      "num": 392,
      "name": "dv_cdc_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/392_dv_cdc_delete.sql",
      "read_script": "generator/spark-reads-df/verify_392_dv_cdc_delete.py",
      "description": "Demonstrates deletion vectors + change data feed + DELETE operation: 1. Insert 200 rows with deterministic data 2. DELETE WHERE id % 5 = 0 (removes 40 rows) 3. Final state: 160 rows",
      "status": "pass",
      "duration_ms": 5989,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:58:45.151673+00:00",
      "read_cold_ms": 3773,
      "read_warm_ms": 1245,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 55,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/393_dv_cdc_update",
      "num": 393,
      "name": "dv_cdc_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/393_dv_cdc_update.sql",
      "read_script": "generator/spark-reads-df/verify_393_dv_cdc_update.py",
      "description": "Demonstrates deletion vectors + change data feed + UPDATE operation: 1. Insert 200 rows with deterministic data (all status = 'open') 2. UPDATE SET status = 'closed' WHERE id <= 50 3. Final state: 200 rows (50 closed, 150 open)",
      "status": "pass",
      "duration_ms": 1462,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:45.599774+00:00",
      "read_cold_ms": 998,
      "read_warm_ms": 224,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 79,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/394_dv_cdc_merge",
      "num": 394,
      "name": "dv_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/394_dv_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_394_dv_cdc_merge.py",
      "description": "Demonstrates deletion vectors + change data feed + MERGE operation: 1. Insert 100 rows (id 1..100) 2. MERGE from a CTE source of 120 rows (id 51..170) - MATCHED (id 51..100): UPDATE SET score = source.score + 1000 - NOT MATCHED (id 101..170): INSERT all source columns 3. Final...",
      "status": "pass",
      "duration_ms": 1173,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:46.773444+00:00",
      "read_cold_ms": 708,
      "read_warm_ms": 218,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 292,
      "write_warm_ms": 200,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/395_dv_cdc_delete_update",
      "num": 395,
      "name": "dv_cdc_delete_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/395_dv_cdc_delete_update.sql",
      "read_script": "generator/spark-reads-df/verify_395_dv_cdc_delete_update.py",
      "description": "Demonstrates deletion vectors + change data feed + DELETE then UPDATE: 1. Insert 300 rows (id 1..300) 2. DELETE WHERE id % 10 = 0 (removes 30 rows) 3. UPDATE SET value = value * 2 WHERE id % 3 = 0 (among remaining rows) 4. Final state: 270 rows",
      "status": "pass",
      "duration_ms": 5925,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:03.713018+00:00",
      "read_cold_ms": 2960,
      "read_warm_ms": 657,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 45,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/396_evolve_then_update",
      "num": 396,
      "name": "evolve_then_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/396_evolve_then_update.sql",
      "read_script": "generator/spark-reads-df/verify_396_evolve_then_update.py",
      "description": "Demonstrates schema evolution + UPDATE: 1. Insert 100 rows (id 1..100) with 3 columns 2. ALTER TABLE ADD COLUMN priority INT 3. UPDATE SET priority = id % 5 WHERE id <= 50 4. INSERT 20 new rows (id 101..120) with priority populated 5. Final state: 120 rows",
      "status": "pass",
      "duration_ms": 4723,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:08.436760+00:00",
      "read_cold_ms": 2623,
      "read_warm_ms": 1146,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 146,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/397_evolve_then_delete",
      "num": 397,
      "name": "evolve_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/397_evolve_then_delete.sql",
      "read_script": "generator/spark-reads-df/verify_397_evolve_then_delete.py",
      "description": "Schema evolution (ADD COLUMN) followed by DELETE. 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN extra STRING 3. INSERT 50 rows (id 101-150) with extra populated 4. DELETE WHERE id <= 30",
      "status": "pass",
      "duration_ms": 10329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:18.766610+00:00",
      "read_cold_ms": 2400,
      "read_warm_ms": 529,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 38,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/398_evolve_then_merge",
      "num": 398,
      "name": "evolve_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/398_evolve_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_398_evolve_then_merge.py",
      "description": "Schema evolution (ADD COLUMN) followed by MERGE. 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN tag STRING 3. MERGE from 120-row CTE (id 1-120): - MATCHED: UPDATE SET tag='merged', value=source.value - NOT MATCHED: INSERT all columns",
      "status": "pass",
      "duration_ms": 8105,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:26.873705+00:00",
      "read_cold_ms": 2626,
      "read_warm_ms": 933,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 66,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/399_evolve_add_then_drop",
      "num": 399,
      "name": "evolve_add_then_drop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/399_evolve_add_then_drop.sql",
      "read_script": "generator/spark-reads-df/verify_399_evolve_add_then_drop.py",
      "description": "ADD COLUMN then DROP COLUMN with column mapping enabled. 1. INSERT 50 rows (id 1-50) 2. ALTER TABLE ADD COLUMN temp STRING 3. INSERT 20 rows (id 51-70) with temp populated 4. ALTER TABLE DROP COLUMN temp 5. INSERT 10 rows (id 71-80) with 3 columns",
      "status": "pass",
      "duration_ms": 5142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:32.017534+00:00",
      "read_cold_ms": 2238,
      "read_warm_ms": 691,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/39_iceberg_compat_v2_advanced",
      "num": 39,
      "name": "iceberg_compat_v2_advanced",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/39_iceberg_compat_v2_advanced.sql",
      "read_script": "generator/spark-reads-df/verify_39_iceberg_compat_v2_advanced.py",
      "description": "Demonstrates Iceberg compatibility V2 with column mapping for row-level operations. V2 adds support for row-level operations metadata compatibility.",
      "status": "pass",
      "duration_ms": 1834,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:33.853476+00:00",
      "read_cold_ms": 1350,
      "read_warm_ms": 151,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 305,
      "write_warm_ms": 211,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/400_colmap_update_delete",
      "num": 400,
      "name": "colmap_update_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/400_colmap_update_delete.sql",
      "read_script": "generator/spark-reads-df/verify_400_colmap_update_delete.py",
      "description": "Column mapping with UPDATE and DELETE operations. 1. INSERT 200 rows (id 1-200) 2. UPDATE SET status='closed' WHERE score < 20 3. DELETE WHERE category='D' (removes 50 rows where i%4=3)",
      "status": "pass",
      "duration_ms": 15290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:49.145436+00:00",
      "read_cold_ms": 2473,
      "read_warm_ms": 1178,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 183,
      "write_warm_ms": 122,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/401_colmap_merge",
      "num": 401,
      "name": "colmap_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/401_colmap_merge.sql",
      "read_script": "generator/spark-reads-df/verify_401_colmap_merge.py",
      "description": "Column mapping with MERGE operation. 1. INSERT 100 rows (id 1-100) 2. MERGE from 130-row CTE (id 1-130): - MATCHED: UPDATE SET price=source.price - NOT MATCHED: INSERT all columns",
      "status": "pass",
      "duration_ms": 9183,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T00:59:58.329681+00:00",
      "read_cold_ms": 2765,
      "read_warm_ms": 1291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 152,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/402_colmap_evolve_dml",
      "num": 402,
      "name": "colmap_evolve_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/402_colmap_evolve_dml.sql",
      "read_script": "generator/spark-reads-df/verify_402_colmap_evolve_dml.py",
      "description": "Column mapping (name mode) + schema evolution + DML: 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN rating INT 3. UPDATE SET rating = score % 10 WHERE id <= 50 4. INSERT 30 rows (id 101-130) with rating populated",
      "status": "pass",
      "duration_ms": 3988,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:00:02.318860+00:00",
      "read_cold_ms": 2451,
      "read_warm_ms": 913,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 172,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/403_cdc_evolve_dml",
      "num": 403,
      "name": "cdc_evolve_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/403_cdc_evolve_dml.sql",
      "read_script": "generator/spark-reads-df/verify_403_cdc_evolve_dml.py",
      "description": "CDC (Change Data Feed) + schema evolution + DML: 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN tag STRING 3. INSERT 30 rows (id 101-130) with tag populated 4. UPDATE SET tag = 'migrated' WHERE id <= 50 5. DELETE WHERE id > 90 AND id <= 100",
      "status": "pass",
      "duration_ms": 4872,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:00:07.191703+00:00",
      "read_cold_ms": 2781,
      "read_warm_ms": 1005,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 242,
      "write_warm_ms": 163,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/404_dv_evolve_delete",
      "num": 404,
      "name": "dv_evolve_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/404_dv_evolve_delete.sql",
      "read_script": "generator/spark-reads-df/verify_404_dv_evolve_delete.py",
      "description": "Deletion vectors + schema evolution after DV-creating DELETE: 1. INSERT 200 rows (id 1-200) with 3 columns 2. DELETE WHERE id % 4 = 0 (removes 50 rows, creates DVs) 3. ALTER TABLE ADD COLUMN extra STRING 4. INSERT 50 rows (id 201-250) with extra populated",
      "status": "pass",
      "duration_ms": 6503,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:00:13.696178+00:00",
      "read_cold_ms": 4270,
      "read_warm_ms": 1167,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 129,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/405_dv_evolve_update",
      "num": 405,
      "name": "dv_evolve_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/405_dv_evolve_update.sql",
      "read_script": "generator/spark-reads-df/verify_405_dv_evolve_update.py",
      "description": "Deletion vectors + schema evolution + UPDATE after evolution: 1. INSERT 200 rows (id 1-200) with 3 columns 2. DELETE WHERE id <= 20 (removes 20 rows) 3. ALTER TABLE ADD COLUMN flag BOOLEAN 4. UPDATE SET flag = true WHERE id > 180",
      "status": "pass",
      "duration_ms": 7074,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:00:20.771573+00:00",
      "read_cold_ms": 3377,
      "read_warm_ms": 1008,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 60,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/406_cdc_partition_dml",
      "num": 406,
      "name": "cdc_partition_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/406_cdc_partition_dml.sql",
      "read_script": "generator/spark-reads-df/verify_406_cdc_partition_dml.py",
      "description": "CDC (Change Data Feed) + partitioning + DML: 1. INSERT 120 rows across 3 partitions (US/EU/AP, 40 each) 2. UPDATE SET status = 'closed' WHERE region = 'US' 3. DELETE WHERE region = 'EU' AND id % 5 = 0",
      "status": "pass",
      "duration_ms": 5303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:00:26.076391+00:00",
      "read_cold_ms": 3316,
      "read_warm_ms": 1154,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 89,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/407_constraint_update",
      "num": 407,
      "name": "constraint_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/407_constraint_update.sql",
      "read_script": "generator/spark-reads-df/verify_407_constraint_update.py",
      "description": "CHECK constraint + UPDATE interaction. 1. INSERT 100 rows with scores 0-99 2. ALTER TABLE ADD CONSTRAINT score_check CHECK (score >= 0 AND score <= 100) 3. UPDATE SET score = score - 10 WHERE score >= 50",
      "status": "pass",
      "duration_ms": 1149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:47.922775+00:00",
      "read_cold_ms": 671,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 184,
      "write_warm_ms": 191,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/408_constraint_delete",
      "num": 408,
      "name": "constraint_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/408_constraint_delete.sql",
      "read_script": "generator/spark-reads-df/verify_408_constraint_delete.py",
      "description": "CHECK constraint + DELETE interaction. 1. INSERT 100 rows with non-empty names 2. ALTER TABLE ADD CONSTRAINT name_len CHECK (LENGTH(name) > 0) 3. DELETE WHERE id % 3 = 0",
      "status": "pass",
      "duration_ms": 1115,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:49.038450+00:00",
      "read_cold_ms": 643,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 193,
      "write_warm_ms": 97,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/409_constraint_merge",
      "num": 409,
      "name": "constraint_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/409_constraint_merge.sql",
      "read_script": "generator/spark-reads-df/verify_409_constraint_merge.py",
      "description": "CHECK constraint + MERGE interaction. 1. INSERT 80 rows with positive values 2. ALTER TABLE ADD CONSTRAINT val_positive CHECK (value > 0) 3. MERGE from 100-row CTE (80 updates + 20 inserts)",
      "status": "pass",
      "duration_ms": 1120,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:50.159161+00:00",
      "read_cold_ms": 649,
      "read_warm_ms": 221,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 221,
      "write_warm_ms": 261,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/40_timestamp_ntz_without_timezone",
      "num": 40,
      "name": "timestamp_ntz_without_timezone",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/40_timestamp_ntz_without_timezone.sql",
      "read_script": "generator/spark-reads-df/verify_40_timestamp_ntz_without_timezone.py",
      "description": "Demonstrates timestamps stored without timezone information as INT64 microseconds. Requires readerVersion >= 3, writerVersion >= 7, timestampNtz feature.",
      "status": "pass",
      "duration_ms": 7599,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:00:51.142807+00:00",
      "read_cold_ms": 2704,
      "read_warm_ms": 989,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 439,
      "write_warm_ms": 350,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/410_dv_partition_update",
      "num": 410,
      "name": "dv_partition_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/410_dv_partition_update.sql",
      "read_script": "generator/spark-reads-df/verify_410_dv_partition_update.py",
      "description": "Deletion vectors + partitioning + UPDATE + DELETE. 1. INSERT 150 rows across 3 partitions (US, EU, AP) 2. UPDATE amount = amount * 1.1 WHERE region = 'US' 3. DELETE WHERE region = 'EU' AND id % 5 = 0",
      "status": "pass",
      "duration_ms": 1159,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:51.318461+00:00",
      "read_cold_ms": 684,
      "read_warm_ms": 221,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 106,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/411_dv_partition_merge",
      "num": 411,
      "name": "dv_partition_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/411_dv_partition_merge.sql",
      "read_script": "generator/spark-reads-df/verify_411_dv_partition_merge.py",
      "description": "Deletion vectors + partitioning + MERGE. 1. INSERT 150 rows across 3 partitions (US, EU, AP) 2. MERGE from 180-row CTE (150 updates + 30 inserts)",
      "status": "pass",
      "duration_ms": 1142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:52.461304+00:00",
      "read_cold_ms": 664,
      "read_warm_ms": 223,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 138,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/412_delete_reinsert_keys",
      "num": 412,
      "name": "delete_reinsert_keys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/412_delete_reinsert_keys.sql",
      "read_script": "generator/spark-reads-df/verify_412_delete_reinsert_keys.py",
      "description": "DELETE then re-INSERT of the same key values. Tests that deletion vectors correctly handle re-inserted keys that were previously deleted. 1. INSERT 100 rows (id 1-100, version_tag=1) 2. DELETE WHERE id <= 30 3. INSERT 30 rows (id 1-30 again, version_tag=2, different name)",
      "status": "pass",
      "duration_ms": 1138,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:53.600095+00:00",
      "read_cold_ms": 667,
      "read_warm_ms": 236,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 150,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/413_delete_all_reinsert",
      "num": 413,
      "name": "delete_all_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/413_delete_all_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_413_delete_all_reinsert.py",
      "description": "DELETE all rows then INSERT new data. Tests empty-table-via-DV intermediate state where every row in the table has a deletion vector. 1. INSERT 50 rows (id 1-50) 2. DELETE all rows (WHERE true) 3. INSERT 50 new rows (id 101-150)",
      "status": "pass",
      "duration_ms": 6502,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:38:52.119410+00:00",
      "read_cold_ms": 5609,
      "read_warm_ms": 375,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/414_update_chain",
      "num": 414,
      "name": "update_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/414_update_chain.sql",
      "read_script": "generator/spark-reads-df/verify_414_update_chain.py",
      "description": "Multiple sequential UPDATEs to the same rows. Tests DV accumulation across chained updates where rows are updated more than once. 1. INSERT 100 rows (all status='new', priority=0) 2. UPDATE SET status='processing' WHERE id <= 50 3. UPDATE SET status='complete' WHERE id <= 25 4...",
      "status": "pass",
      "duration_ms": 2272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:38:54.391822+00:00",
      "read_cold_ms": 1513,
      "read_warm_ms": 374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 214,
      "write_warm_ms": 125,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/415_update_all_rows",
      "num": 415,
      "name": "update_all_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/415_update_all_rows.sql",
      "read_script": "generator/spark-reads-df/verify_415_update_all_rows.py",
      "description": "UPDATE all rows (no WHERE clause). Tests DV behavior when every single row in the table gets a deletion vector. Two consecutive full-table updates stress-test DV file management. 1. INSERT 200 rows 2. UPDATE value = value * 2 (all 200 rows) 3. UPDATE name = CONCAT('v2_', name)...",
      "status": "pass",
      "duration_ms": 1115,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:56.714188+00:00",
      "read_cold_ms": 652,
      "read_warm_ms": 224,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 189,
      "write_warm_ms": 108,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/416_merge_delete_clause",
      "num": 416,
      "name": "merge_delete_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/416_merge_delete_clause.sql",
      "read_script": "generator/spark-reads-df/verify_416_merge_delete_clause.py",
      "description": "MERGE with WHEN MATCHED THEN DELETE clause. Tests that MERGE can selectively delete rows based on a condition while updating others. 1. INSERT 200 rows (all status='active') 2. MERGE from 50-row source (id 1-50): - WHEN MATCHED AND target.score < 10 THEN DELETE - WHEN MATCHED...",
      "status": "pass",
      "duration_ms": 1080,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:57.795068+00:00",
      "read_cold_ms": 629,
      "read_warm_ms": 214,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/417_merge_insert_only",
      "num": 417,
      "name": "merge_insert_only",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/417_merge_insert_only.sql",
      "read_script": "generator/spark-reads-df/verify_417_merge_insert_only.py",
      "description": "MERGE with only WHEN NOT MATCHED THEN INSERT (no MATCHED clause). 1. INSERT 100 rows (id 1-100) 2. MERGE from CTE of 50 rows (id 101-150) with WHEN NOT MATCHED THEN INSERT",
      "status": "pass",
      "duration_ms": 3822,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:01:28.795978+00:00",
      "read_cold_ms": 1962,
      "read_warm_ms": 843,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 142,
      "write_warm_ms": 105,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/418_merge_update_only",
      "num": 418,
      "name": "merge_update_only",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/418_merge_update_only.sql",
      "read_script": "generator/spark-reads-df/verify_418_merge_update_only.py",
      "description": "MERGE with only WHEN MATCHED THEN UPDATE (no NOT MATCHED clause). 1. INSERT 100 rows (id 1-100) with tag='original' 2. MERGE from CTE of 100 rows (id 1-100) with WHEN MATCHED THEN UPDATE",
      "status": "pass",
      "duration_ms": 1113,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:34:58.909151+00:00",
      "read_cold_ms": 607,
      "read_warm_ms": 239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 71,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/419_optimize_then_delete",
      "num": 419,
      "name": "optimize_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/419_optimize_then_delete.sql",
      "read_script": "generator/spark-reads-df/verify_419_optimize_then_delete.py",
      "description": "OPTIMIZE followed by DELETE. 1. INSERT 500 rows in 5 batches of 100 2. OPTIMIZE to compact data files 3. DELETE WHERE id % 4 = 0 (removes 125 rows)",
      "status": "pass",
      "duration_ms": 4508,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:01:38.852298+00:00",
      "read_cold_ms": 3067,
      "read_warm_ms": 780,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 288,
      "write_warm_ms": 262,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/41_v2_checkpoint_feature_enabled",
      "num": 41,
      "name": "v2_checkpoint_feature_enabled",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/41_v2_checkpoint_feature_enabled.sql",
      "read_script": "generator/spark-reads-df/verify_41_v2_checkpoint_feature_enabled.py",
      "description": "Demonstrates V2 checkpoint with writeStatsAsStruct enabled. V2 checkpoints store statistics as structured data for faster metadata access.",
      "status": "pass",
      "duration_ms": 5633,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:01:44.486232+00:00",
      "read_cold_ms": 2034,
      "read_warm_ms": 2924,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 212,
      "write_warm_ms": 180,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/420_optimize_then_update",
      "num": 420,
      "name": "optimize_then_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/420_optimize_then_update.sql",
      "read_script": "generator/spark-reads-df/verify_420_optimize_then_update.py",
      "description": "OPTIMIZE followed by UPDATE. 1. INSERT 500 rows in 5 batches of 100 2. OPTIMIZE to compact data files 3. UPDATE SET status='refreshed' WHERE id <= 100 (updates 100 rows)",
      "status": "pass",
      "duration_ms": 1182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:00.092219+00:00",
      "read_cold_ms": 699,
      "read_warm_ms": 238,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 315,
      "write_warm_ms": 393,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/421_optimize_then_merge",
      "num": 421,
      "name": "optimize_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/421_optimize_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_421_optimize_then_merge.py",
      "description": "OPTIMIZE followed by MERGE. 1. INSERT 300 rows in 3 batches of 100 2. OPTIMIZE to compact data files 3. MERGE from 350-row CTE (300 updates + 50 inserts)",
      "status": "pass",
      "duration_ms": 5224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:01:54.346330+00:00",
      "read_cold_ms": 3686,
      "read_warm_ms": 663,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 220,
      "write_warm_ms": 308,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/422_dv_cdc_evolve",
      "num": 422,
      "name": "dv_cdc_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/422_dv_cdc_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_422_dv_cdc_evolve.py",
      "description": "Deletion vectors + CDC + schema evolution (three-way interaction): 1. INSERT 100 rows (id 1-100) 2. DELETE WHERE id % 10 = 0 (removes 10 rows) 3. ALTER TABLE ADD COLUMN tag STRING 4. INSERT 30 rows (id 101-130) with tag populated 5. UPDATE SET tag = 'backfill' WHERE id <= 20",
      "status": "pass",
      "duration_ms": 1092,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:01.184601+00:00",
      "read_cold_ms": 626,
      "read_warm_ms": 241,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 107,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/423_dv_cdc_partition_merge",
      "num": 423,
      "name": "dv_cdc_partition_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/423_dv_cdc_partition_merge.sql",
      "read_script": "generator/spark-reads-df/verify_423_dv_cdc_partition_merge.py",
      "description": "Deletion vectors + CDC + partitioning + MERGE (four-way interaction): 1. INSERT 150 rows across 3 partitions (US, EU, AP) 2. MERGE from 180-row CTE (150 updates + 30 inserts)",
      "status": "pass",
      "duration_ms": 1200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:02.385033+00:00",
      "read_cold_ms": 715,
      "read_warm_ms": 227,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 615,
      "write_warm_ms": 636,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/424_null_heavy_dml",
      "num": 424,
      "name": "null_heavy_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/424_null_heavy_dml.sql",
      "read_script": "generator/spark-reads-df/verify_424_null_heavy_dml.py",
      "description": "NULL-heavy table with DML operations. All nullable columns are NULL throughout the test to verify correct handling of all-NULL columns during UPDATE and DELETE operations with deletion vectors. 1. INSERT 100 rows (nullable_a and nullable_b always NULL) 2. UPDATE SET name =...",
      "status": "pass",
      "duration_ms": 1087,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:03.473082+00:00",
      "read_cold_ms": 610,
      "read_warm_ms": 229,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
      "write_warm_ms": 55,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/425_null_partition_delete",
      "num": 425,
      "name": "null_partition_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/425_null_partition_delete.sql",
      "read_script": "generator/spark-reads-df/verify_425_null_partition_delete.py",
      "description": "NULL partition values + DELETE targeting the NULL partition. Verifies correct handling of IS NULL predicates on partition columns with deletion vectors enabled. 1. INSERT 90 rows across 3 partitions (A, B, NULL) 2. DELETE WHERE category IS NULL",
      "status": "pass",
      "duration_ms": 776,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:04.249988+00:00",
      "read_cold_ms": 484,
      "read_warm_ms": 144,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 62,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/426_null_in_merge_key",
      "num": 426,
      "name": "null_in_merge_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/426_null_in_merge_key.sql",
      "read_script": "generator/spark-reads-df/verify_426_null_in_merge_key.py",
      "description": "MERGE with NULL join keys. Verifies that NULL != NULL semantics are respected in the MERGE ON condition: NULL keys in the source never match NULL keys in the target, resulting in INSERT instead of UPDATE. 1. INSERT 50 rows (10 with rec_id = NULL, 40 with non-NULL rec_id) 2...",
      "status": "pass",
      "duration_ms": 782,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:05.033048+00:00",
      "read_cold_ms": 481,
      "read_warm_ms": 151,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/427_bigint_boundaries",
      "num": 427,
      "name": "bigint_boundaries",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/427_bigint_boundaries.sql",
      "read_script": "generator/spark-reads-df/verify_427_bigint_boundaries.py",
      "description": "BIGINT boundary values combined with DML operations: 1. INSERT 100 rows (id 1-100) with deterministic formulas 2. INSERT 5 boundary rows using VALUES (min+1, -1, 0, max-1, max) 3. DELETE WHERE id = 0 (removes 1 row) 4. UPDATE SET name='extreme' WHERE id = -9223372036854775807",
      "status": "pass",
      "duration_ms": 1062,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:06.095311+00:00",
      "read_cold_ms": 625,
      "read_warm_ms": 211,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 55,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/428_empty_string_values",
      "num": 428,
      "name": "empty_string_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/428_empty_string_values.sql",
      "read_script": "generator/spark-reads-df/verify_428_empty_string_values.py",
      "description": "Empty string values vs NULL string values with DML: 1. INSERT 100 rows with a mix of empty strings, NULLs, and normal names 2. UPDATE SET status='blank' WHERE name = '' (updates 20 rows) 3. DELETE WHERE name IS NULL (removes 20 rows)",
      "status": "pass",
      "duration_ms": 1066,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:07.161856+00:00",
      "read_cold_ms": 593,
      "read_warm_ms": 212,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 240,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/429_decimal_full_precision_dml",
      "num": 429,
      "name": "decimal_full_precision_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/429_decimal_full_precision_dml.sql",
      "read_script": "generator/spark-reads-df/verify_429_decimal_full_precision_dml.py",
      "description": "DECIMAL(10,4) precision preserved through UPDATE and DELETE operations: 1. INSERT 100 rows with DECIMAL(10,4) amounts 2. UPDATE amount = amount + 0.0001 WHERE id <= 50 3. DELETE WHERE amount < 1.0",
      "status": "pass",
      "duration_ms": 1296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:08.458205+00:00",
      "read_cold_ms": 780,
      "read_warm_ms": 262,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 144,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/42_row_tracking_stable_row_ids",
      "num": 42,
      "name": "row_tracking_stable_row_ids",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/42_row_tracking_stable_row_ids.sql",
      "read_script": "generator/spark-reads-df/verify_42_row_tracking_stable_row_ids.py",
      "description": "Demonstrates row tracking with stable row IDs for each row across versions. Each row maintains a unique, stable identifier across updates and compactions.",
      "status": "pass",
      "duration_ms": 5700,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:02:42.618880+00:00",
      "read_cold_ms": 2420,
      "read_warm_ms": 2311,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 427,
      "write_warm_ms": 382,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/430_single_row_dml_cycle",
      "num": 430,
      "name": "single_row_dml_cycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/430_single_row_dml_cycle.sql",
      "read_script": "generator/spark-reads-df/verify_430_single_row_dml_cycle.py",
      "description": "Full DML cycle on a single-row table: 1. INSERT 1 row (id=1) 2. UPDATE the row (value and status) 3. DELETE the row 4. INSERT a new row (id=2)",
      "status": "pass",
      "duration_ms": 4023,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:02:46.643180+00:00",
      "read_cold_ms": 2412,
      "read_warm_ms": 751,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 51,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/431_multi_version_insert_update",
      "num": 431,
      "name": "multi_version_insert_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/431_multi_version_insert_update.sql",
      "read_script": "generator/spark-reads-df/verify_431_multi_version_insert_update.py",
      "description": "15 versions alternating INSERT and UPDATE to test multi-version correctness: - 8 INSERT batches (50 rows each, ids 1-400) - 7 UPDATE operations (set status per batch range) - Total: 15 versions (version 0-14)",
      "status": "pass",
      "duration_ms": 1541,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:10.000012+00:00",
      "read_cold_ms": 944,
      "read_warm_ms": 324,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 352,
      "write_warm_ms": 577,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/432_multi_version_mixed_dml",
      "num": 432,
      "name": "multi_version_mixed_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/432_multi_version_mixed_dml.sql",
      "read_script": "generator/spark-reads-df/verify_432_multi_version_mixed_dml.py",
      "description": "12 versions of mixed DML (INSERT, UPDATE, DELETE, MERGE) to test long DML chains with interleaved operations.",
      "status": "pass",
      "duration_ms": 1370,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:11.370473+00:00",
      "read_cold_ms": 796,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 381,
      "write_warm_ms": 280,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/433_constraint_then_evolve",
      "num": 433,
      "name": "constraint_then_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/433_constraint_then_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_433_constraint_then_evolve.py",
      "description": "CHECK constraint + schema evolution + DML: 1. INSERT 80 rows (id 1-80) 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0) 3. ALTER TABLE ADD COLUMN priority INT 4. INSERT 20 rows (id 81-100) with priority populated 5. UPDATE SET priority = 1 WHERE id <= 40 AND score > 50",
      "status": "pass",
      "duration_ms": 1206,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:12.577164+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 222,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 138,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/434_dv_constraint_dml",
      "num": 434,
      "name": "dv_constraint_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/434_dv_constraint_dml.sql",
      "read_script": "generator/spark-reads-df/verify_434_dv_constraint_dml.py",
      "description": "Deletion vectors + CHECK constraint + DML: 1. INSERT 100 rows (id 1-100) 2. ALTER TABLE ADD CONSTRAINT val_positive CHECK (value > 0) 3. DELETE WHERE id % 4 = 0 (25 rows removed) 4. UPDATE SET tag = 'v2', value = value + 1.0 WHERE id % 3 = 0",
      "status": "pass",
      "duration_ms": 1128,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:13.705794+00:00",
      "read_cold_ms": 673,
      "read_warm_ms": 244,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 37,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/435_cdc_multi_version_dml",
      "num": 435,
      "name": "cdc_multi_version_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/435_cdc_multi_version_dml.sql",
      "read_script": "generator/spark-reads-df/verify_435_cdc_multi_version_dml.py",
      "description": "CDC (Change Data Feed) + 10 versions of mixed DML to test long CDF version history with INSERT, UPDATE, DELETE, and MERGE operations.",
      "status": "pass",
      "duration_ms": 1248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:14.954896+00:00",
      "read_cold_ms": 755,
      "read_warm_ms": 262,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 367,
      "write_warm_ms": 466,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/436_wide_table_dml",
      "num": 436,
      "name": "wide_table_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/436_wide_table_dml.sql",
      "read_script": "generator/spark-reads-df/verify_436_wide_table_dml.py",
      "description": "Wide table (15 columns) + DML operations to test correctness with many columns across UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 1410,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:16.365646+00:00",
      "read_cold_ms": 854,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 64,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/437_partition_optimize_dml",
      "num": 437,
      "name": "partition_optimize_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/437_partition_optimize_dml.sql",
      "read_script": "generator/spark-reads-df/verify_437_partition_optimize_dml.py",
      "description": "Partition + OPTIMIZE + DML: - INSERT 300 rows in 5 batches across 3 partitions (US, EU, AP) - OPTIMIZE to compact files - DELETE rows where id%5=0 (60 rows removed) - UPDATE rows in US partition to status='post_opt",
      "status": "pass",
      "duration_ms": 1290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:17.656027+00:00",
      "read_cold_ms": 729,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 411,
      "write_warm_ms": 492,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/438_dv_cdc_optimize",
      "num": 438,
      "name": "dv_cdc_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/438_dv_cdc_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_438_dv_cdc_optimize.py",
      "description": "DV + CDC + OPTIMIZE interaction: - INSERT 200 rows in 4 batches of 50 each (with CDF enabled) - DELETE rows where id%10=0 (creates deletion vectors) - OPTIMIZE to compact files (should NOT emit CDC rows) - INSERT 20 more rows after OPTIMIZE",
      "status": "pass",
      "duration_ms": 1476,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:38:55.868422+00:00",
      "read_cold_ms": 1023,
      "read_warm_ms": 227,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 301,
      "write_warm_ms": 419,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/439_insert_overwrite_then_dml",
      "num": 439,
      "name": "insert_overwrite_then_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/439_insert_overwrite_then_dml.sql",
      "read_script": "generator/spark-reads-df/verify_439_insert_overwrite_then_dml.py",
      "description": "INSERT OVERWRITE followed by DML operations: - INSERT OVERWRITE seeds the table with 200 rows - UPDATE first 50 rows to status='processed' - DELETE rows where id>180 (removes 20 rows)",
      "status": "pass",
      "duration_ms": 1211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:19.803939+00:00",
      "read_cold_ms": 714,
      "read_warm_ms": 236,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 95,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/43_row_tracking_commit_versions",
      "num": 43,
      "name": "row_tracking_commit_versions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/43_row_tracking_commit_versions.sql",
      "read_script": "generator/spark-reads-df/verify_43_row_tracking_commit_versions.py",
      "description": "Demonstrates row tracking with commit versions showing when each row was last modified. Tracks the Delta version number when each row was created or last updated.",
      "status": "pass",
      "duration_ms": 7311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:03:39.020220+00:00",
      "read_cold_ms": 4844,
      "read_warm_ms": 1006,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 5512,
      "write_warm_ms": 5736,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/440_interleaved_evolve_dml",
      "num": 440,
      "name": "interleaved_evolve_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/440_interleaved_evolve_dml.sql",
      "read_script": "generator/spark-reads-df/verify_440_interleaved_evolve_dml.py",
      "description": "Interleaved ALTER TABLE ADD COLUMN + DML operations: - INSERT 100 rows with 3 columns - ALTER ADD COLUMN cat STRING - UPDATE cat for first 50 rows - ALTER ADD COLUMN priority INT - UPDATE priority for all surviving rows - DELETE rows where id%10=0 - INSERT 20 new rows with all 5...",
      "status": "pass",
      "duration_ms": 1665,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:38:57.534289+00:00",
      "read_cold_ms": 1066,
      "read_warm_ms": 287,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 226,
      "write_warm_ms": 298,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/441_partition_constraint_dml",
      "num": 441,
      "name": "partition_constraint_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/441_partition_constraint_dml.sql",
      "read_script": "generator/spark-reads-df/verify_441_partition_constraint_dml.py",
      "description": "Partition + CHECK constraint + DML: - INSERT 120 rows across 3 partitions (US, EU, AP) - ALTER TABLE ADD CONSTRAINT score_range CHECK (score >= 0 AND score <= 100) - UPDATE US rows (score = score - 5 WHERE score >= 5) - DELETE EU rows where score < 20",
      "status": "pass",
      "duration_ms": 1314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:22.332985+00:00",
      "read_cold_ms": 753,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 79,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/442_cdc_colmap_merge",
      "num": 442,
      "name": "cdc_colmap_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/442_cdc_colmap_merge.sql",
      "read_script": "generator/spark-reads-df/verify_442_cdc_colmap_merge.py",
      "description": "CDC + column mapping (name mode) + MERGE. Three-way combo never tested. - INSERT 100 rows - MERGE from 120-row CTE (id 1-120): - MATCHED (id 1-100): UPDATE SET price=source.price, status='merged' - NOT MATCHED (id 101-120): INSERT all columns",
      "status": "pass",
      "duration_ms": 1759,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:24.093042+00:00",
      "read_cold_ms": 663,
      "read_warm_ms": 249,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 255,
      "write_warm_ms": 203,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/443_cdc_constraint_dml",
      "num": 443,
      "name": "cdc_constraint_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/443_cdc_constraint_dml.sql",
      "read_script": "generator/spark-reads-df/verify_443_cdc_constraint_dml.py",
      "description": "CDC + CHECK constraint + DML. Never combined before. - INSERT 100 rows - ALTER TABLE ADD CONSTRAINT score_valid CHECK (score >= 0 AND score <= 100) - UPDATE SET score = score - 5 WHERE score >= 50 - DELETE WHERE score < 5",
      "status": "pass",
      "duration_ms": 1971,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:38:59.506435+00:00",
      "read_cold_ms": 926,
      "read_warm_ms": 327,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/444_dv_cdc_constraint",
      "num": 444,
      "name": "dv_cdc_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/444_dv_cdc_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_444_dv_cdc_constraint.py",
      "description": "DV + CDC + CHECK constraint. Three-way combo never tested. - INSERT 100 rows - ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) - DELETE WHERE id % 4 = 0 (25 rows removed) - UPDATE SET tag = 'checked' WHERE id % 3 = 0 (of surviving rows)",
      "status": "pass",
      "duration_ms": 1267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:26.581154+00:00",
      "read_cold_ms": 712,
      "read_warm_ms": 203,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 146,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/445_colmap_partition_dml",
      "num": 445,
      "name": "colmap_partition_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/445_colmap_partition_dml.sql",
      "read_script": "generator/spark-reads-df/verify_445_colmap_partition_dml.py",
      "description": "Column mapping (name mode) + partitioning + DML. Never combined. - INSERT 150 rows across 3 partitions (US, EU, AP) - UPDATE amount * 1.5 for US partition - DELETE EU rows where id % 5 = 0",
      "status": "pass",
      "duration_ms": 1602,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:28.183807+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 221,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 90,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/446_optimize_schema_evolve",
      "num": 446,
      "name": "optimize_schema_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/446_optimize_schema_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_446_optimize_schema_evolve.py",
      "description": "OPTIMIZE + schema evolution (ALTER TABLE ADD COLUMN). Never combined in SQL. - INSERT 300 rows in 3 batches of 100 - OPTIMIZE (compacts the 3 data files) - ALTER TABLE ADD COLUMN extra STRING - INSERT 50 rows (id 301-350) with extra column - UPDATE SET extra = 'backfill' WHERE...",
      "status": "pass",
      "duration_ms": 1228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:29.412415+00:00",
      "read_cold_ms": 711,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 218,
      "write_warm_ms": 196,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/447_optimize_cdc",
      "num": 447,
      "name": "optimize_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/447_optimize_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_447_optimize_cdc.py",
      "description": "OPTIMIZE + CDC in SQL. Tests that OPTIMIZE produces no CDF rows. - INSERT 300 rows in 6 batches of 50 each (creates 6 small files) - UPDATE first 50 rows - OPTIMIZE (should NOT emit CDF rows) - INSERT 50 more rows",
      "status": "pass",
      "duration_ms": 1164,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:30.576763+00:00",
      "read_cold_ms": 683,
      "read_warm_ms": 139,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 505,
      "write_warm_ms": 365,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/448_merge_large_scale",
      "num": 448,
      "name": "merge_large_scale",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/448_merge_large_scale.sql",
      "read_script": "generator/spark-reads-df/verify_448_merge_large_scale.py",
      "description": "Large-scale MERGE (1000 rows + 1200-row MERGE). Tests MERGE at scale in SQL. - INSERT 1000 rows - MERGE from 1200-row CTE (id 1-1200): - MATCHED (id 1-1000): UPDATE SET score = score + 1000, status = 'updated' - NOT MATCHED (id 1001-1200): INSERT all columns",
      "status": "pass",
      "duration_ms": 1238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:31.815804+00:00",
      "read_cold_ms": 736,
      "read_warm_ms": 217,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 70,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/449_delete_compound_predicate",
      "num": 449,
      "name": "delete_compound_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/449_delete_compound_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_449_delete_compound_predicate.py",
      "description": "DELETE with compound AND/OR predicates. Tests complex predicate pushdown. - INSERT 500 rows - DELETE WHERE (category='A' AND score<20) OR (category='B' AND NOT active) OR (id>480)",
      "status": "pass",
      "duration_ms": 1252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:33.068063+00:00",
      "read_cold_ms": 710,
      "read_warm_ms": 257,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 29,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/44_vacuum_protocol_check_enabled",
      "num": 44,
      "name": "vacuum_protocol_check_enabled",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/44_vacuum_protocol_check_enabled.sql",
      "read_script": "generator/spark-reads-df/verify_44_vacuum_protocol_check_enabled.py",
      "description": "Demonstrates table with deletion vectors that creates multiple obsolete files. This tests vacuum behavior and file retention for readers using older protocol versions.",
      "status": "pass",
      "duration_ms": 6720,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:04:39.213458+00:00",
      "read_cold_ms": 2920,
      "read_warm_ms": 1039,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 443,
      "write_warm_ms": 645,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/450_update_case_expression",
      "num": 450,
      "name": "update_case_expression",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/450_update_case_expression.sql",
      "read_script": "generator/spark-reads-df/verify_450_update_case_expression.py",
      "description": "UPDATE with CASE expression in SET clause. Tests complex UPDATE expressions. - INSERT 200 rows - UPDATE SET grade = CASE WHEN score>=90 THEN 'A' ... END (all rows) - UPDATE SET status = CASE WHEN grade IN ('A','B') THEN 'honor' ... END (all rows)",
      "status": "pass",
      "duration_ms": 1184,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:34.252348+00:00",
      "read_cold_ms": 706,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/451_merge_self_dedup",
      "num": 451,
      "name": "merge_self_dedup",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/451_merge_self_dedup.sql",
      "read_script": "generator/spark-reads-df/verify_451_merge_self_dedup.py",
      "description": "MERGE where source has recomputed values, updating only rows where the source value exceeds the target value. Tests conditional MERGE with a WHEN MATCHED AND <condition> clause. - INSERT 100 rows with target formula values - MERGE from 100-row CTE with source formula values: -...",
      "status": "pass",
      "duration_ms": 1279,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:35.532173+00:00",
      "read_cold_ms": 769,
      "read_warm_ms": 217,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 69,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/452_timestamp_dml",
      "num": 452,
      "name": "timestamp_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/452_timestamp_dml.sql",
      "read_script": "generator/spark-reads-df/verify_452_timestamp_dml.py",
      "description": "TIMESTAMP columns with DML operations: - INSERT 100 rows with TIMESTAMP column using arrow_cast microsecond encoding - UPDATE rows with timestamp predicate (event_ts < threshold) - DELETE rows with timestamp predicate (event_ts > threshold)",
      "status": "pass",
      "duration_ms": 1200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:36.733222+00:00",
      "read_cold_ms": 739,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/453_boolean_dml",
      "num": 453,
      "name": "boolean_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/453_boolean_dml.sql",
      "read_script": "generator/spark-reads-df/verify_453_boolean_dml.py",
      "description": "BOOLEAN columns as DML predicates and update values: - INSERT 200 rows with two boolean columns (active, verified) - UPDATE verified=true WHERE active=true AND score>50 - DELETE WHERE active=false AND verified=false AND score<20",
      "status": "pass",
      "duration_ms": 6054,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:05:06.773419+00:00",
      "read_cold_ms": 3457,
      "read_warm_ms": 1436,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 42,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/454_struct_dml",
      "num": 454,
      "name": "struct_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/454_struct_dml.sql",
      "read_script": "generator/spark-reads-df/verify_454_struct_dml.py",
      "description": "Nested STRUCT columns with DML operations: - INSERT 100 rows with a STRUCT<age: INT, city: STRING> column - UPDATE non-struct column (value) for id<=30 - DELETE rows where id%7=0 - Verifies struct values are preserved through UPDATE/DELETE operations",
      "status": "pass",
      "duration_ms": 1807,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:39:01.314240+00:00",
      "read_cold_ms": 1004,
      "read_warm_ms": 291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 90,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/455_array_dml",
      "num": 455,
      "name": "array_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/455_array_dml.sql",
      "read_script": "generator/spark-reads-df/verify_455_array_dml.py",
      "description": "String tags column (simulating array-like data) with DML operations: - INSERT 100 rows with a tags STRING column containing comma-separated values - UPDATE tags='gold' WHERE score>80 - DELETE WHERE tags='green' AND score<20 - Tests tag-based filtering and update through DML",
      "status": "pass",
      "duration_ms": 1321,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:39.385782+00:00",
      "read_cold_ms": 778,
      "read_warm_ms": 217,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
      "write_warm_ms": 152,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/456_multi_table_merge",
      "num": 456,
      "name": "multi_table_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/456_multi_table_merge.sql",
      "read_script": "generator/spark-reads-df/verify_456_multi_table_merge.py",
      "description": "MERGE with conditional update logic: - INSERT 200 rows with category column - MERGE from a 250-row CTE source - WHEN MATCHED AND target.category != 'D' THEN UPDATE (skip category D rows) - WHEN NOT MATCHED THEN INSERT - Tests selective MERGE with predicate-guarded updates",
      "status": "pass",
      "duration_ms": 1177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:40.563656+00:00",
      "read_cold_ms": 699,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 125,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/457_dv_no_dv_comparison",
      "num": 457,
      "name": "dv_no_dv_comparison",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/457_dv_no_dv_comparison.sql",
      "read_script": "generator/spark-reads-df/verify_457_dv_no_dv_comparison.py",
      "description": "Table WITHOUT deletion vectors (full file rewrites for DML): - INSERT 100 rows - UPDATE first 30 rows (full rewrite, no DVs) - DELETE last 20 rows (full rewrite, no DVs) - Verifies non-DV writes produce correct results - All other tests in this series use...",
      "status": "pass",
      "duration_ms": 844,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:41.407928+00:00",
      "read_cold_ms": 649,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 97,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/458_partition_multi_col",
      "num": 458,
      "name": "partition_multi_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/458_partition_multi_col.sql",
      "read_script": "generator/spark-reads-df/verify_458_partition_multi_col.py",
      "description": "Multi-column partitioning with DML operations: - PARTITIONED BY (region, year) -- two partition columns - INSERT 120 rows across 6 partition combinations (3 regions x 2 years) - UPDATE rows in one specific partition (region='US', year=2024) - DELETE rows in another partition...",
      "status": "pass",
      "duration_ms": 6266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:05:39.119854+00:00",
      "read_cold_ms": 3022,
      "read_warm_ms": 2363,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
      "write_warm_ms": 271,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/459_insert_overwrite_partition",
      "num": 459,
      "name": "insert_overwrite_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/459_insert_overwrite_partition.sql",
      "read_script": "generator/spark-reads-df/verify_459_insert_overwrite_partition.py",
      "description": "INSERT OVERWRITE replacing entire table contents, followed by DML: - INSERT 90 rows across 3 categories - INSERT OVERWRITE with 50 new rows (replaces entire table) - UPDATE first 20 of the new rows - Verifies INSERT OVERWRITE + subsequent DML correctness",
      "status": "pass",
      "duration_ms": 1464,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:39:02.779157+00:00",
      "read_cold_ms": 884,
      "read_warm_ms": 272,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 240,
      "write_warm_ms": 252,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/45_variant_data_type_basic",
      "num": 45,
      "name": "variant_data_type_basic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/45_variant_data_type_basic.sql",
      "read_script": "generator/spark-reads-df/verify_45_variant_data_type_basic.py",
      "description": "Demonstrates variant data type for semi-structured JSON data. Efficiently stores and queries nested JSON without fixed schema.",
      "status": "pass",
      "duration_ms": 10310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:05:57.196485+00:00",
      "read_cold_ms": 2482,
      "read_warm_ms": 911,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 177,
      "write_warm_ms": 188,
      "tags": [
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/460_delete_then_optimize",
      "num": 460,
      "name": "delete_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/460_delete_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_460_delete_then_optimize.py",
      "description": "DELETE creating deletion vectors, then OPTIMIZE to materialize them: - INSERT 1000 rows in 10 batches of 100 (creates 10 data files) - DELETE rows where id%3=0 (creates DVs on each data file) - OPTIMIZE compacts files and materializes DVs into clean data files - Verifies...",
      "status": "pass",
      "duration_ms": 1026,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:43.786462+00:00",
      "read_cold_ms": 675,
      "read_warm_ms": 154,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 750,
      "write_warm_ms": 603,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/461_update_then_optimize",
      "num": 461,
      "name": "update_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/461_update_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_461_update_then_optimize.py",
      "description": "UPDATE creating deletion vectors, then OPTIMIZE to compact: - INSERT 500 rows in 5 batches of 100 (creates 5 data files) - UPDATE first 200 rows (creates DVs + new data files for updated rows) - OPTIMIZE compacts all files into fewer, clean files - Verifies correct values after...",
      "status": "pass",
      "duration_ms": 992,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:44.779140+00:00",
      "read_cold_ms": 551,
      "read_warm_ms": 246,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 365,
      "write_warm_ms": 401,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/462_constraint_drop_insert",
      "num": 462,
      "name": "constraint_drop_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/462_constraint_drop_insert.sql",
      "read_script": "generator/spark-reads-df/verify_462_constraint_drop_insert.py",
      "description": "Constraint lifecycle: ADD CONSTRAINT then DROP CONSTRAINT then insert violating data. 1. INSERT 50 rows with score = (i*53)%100 (range [0, 99]) 2. ALTER TABLE ADD CONSTRAINT score_bound CHECK (score >= 0 AND score <= 100) 3. INSERT 30 more valid rows (id 51-80) 4. ALTER TABLE...",
      "status": "pass",
      "duration_ms": 1009,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:39:03.789000+00:00",
      "read_cold_ms": 625,
      "read_warm_ms": 199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 213,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/463_evolve_rename_dml",
      "num": 463,
      "name": "evolve_rename_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/463_evolve_rename_dml.sql",
      "read_script": "generator/spark-reads-df/verify_463_evolve_rename_dml.py",
      "description": "RENAME COLUMN then DML using the new column name. Tests column mapping after rename. 1. INSERT 100 rows with old_name column 2. ALTER TABLE RENAME COLUMN old_name TO display_name 3. UPDATE SET display_name = CONCAT('renamed_', CAST(id AS STRING)) WHERE id <= 50 4. DELETE WHERE...",
      "status": "pass",
      "duration_ms": 1379,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:39:05.168324+00:00",
      "read_cold_ms": 810,
      "read_warm_ms": 287,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 124,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/464_evolve_multi_add",
      "num": 464,
      "name": "evolve_multi_add",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/464_evolve_multi_add.sql",
      "read_script": "generator/spark-reads-df/verify_464_evolve_multi_add.py",
      "description": "Multiple ALTER ADD COLUMN operations interleaved with INSERTs. Tests multi-step schema growth from 2 columns to 6 columns. 1. INSERT 50 rows (2 cols: id, name) 2. ALTER ADD COLUMN val1 INT 3. INSERT 30 rows (id 51-80) with val1 4. ALTER ADD COLUMN val2 DOUBLE 5. INSERT 20 rows...",
      "status": "pass",
      "duration_ms": 7443,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:06:22.052450+00:00",
      "read_cold_ms": 2122,
      "read_warm_ms": 496,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 302,
      "write_warm_ms": 334,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/465_partition_five_regions",
      "num": 465,
      "name": "partition_five_regions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/465_partition_five_regions.sql",
      "read_script": "generator/spark-reads-df/verify_465_partition_five_regions.py",
      "description": "5-region partition + DML across all partitions. 1. INSERT 250 rows across 5 partitions (NA, EU, AP, SA, AF -- 50 each) 2. UPDATE SET value = value * 2 WHERE region = 'NA' OR region = 'SA' 3. DELETE WHERE region = 'AF' AND id % 5 = 0",
      "status": "pass",
      "duration_ms": 4179,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:06:26.232731+00:00",
      "read_cold_ms": 2278,
      "read_warm_ms": 808,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 284,
      "write_warm_ms": 343,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/466_dv_cdc_partition_evolve",
      "num": 466,
      "name": "dv_cdc_partition_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/466_dv_cdc_partition_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_466_dv_cdc_partition_evolve.py",
      "description": "Four-way combo: DV + CDC + partition + schema evolution. Most complex combo. 1. INSERT 120 rows across 3 regions (US, EU, AP -- 40 each) 2. DELETE WHERE id % 10 = 0 (12 rows removed) 3. ALTER TABLE ADD COLUMN priority INT 4. INSERT 30 rows (id 121-150) with priority 5. UPDATE...",
      "status": "pass",
      "duration_ms": 8109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:06:34.343588+00:00",
      "read_cold_ms": 5566,
      "read_warm_ms": 941,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 581,
      "write_warm_ms": 239,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/467_merge_three_clause",
      "num": 467,
      "name": "merge_three_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/467_merge_three_clause.sql",
      "read_script": "generator/spark-reads-df/verify_467_merge_three_clause.py",
      "description": "MERGE with all 3 clause types: MATCHED UPDATE, MATCHED DELETE, NOT MATCHED INSERT. 1. INSERT 200 rows with score = (i*53)%100, status = 'active' 2. MERGE from 250-row CTE: WHEN MATCHED AND target.score < 10 THEN DELETE WHEN MATCHED THEN UPDATE SET status = 'verified', score =...",
      "status": "pass",
      "duration_ms": 1188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:48.099094+00:00",
      "read_cold_ms": 626,
      "read_warm_ms": 281,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/468_cdc_dv_merge_partition",
      "num": 468,
      "name": "cdc_dv_merge_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/468_cdc_dv_merge_partition.sql",
      "read_script": "generator/spark-reads-df/verify_468_cdc_dv_merge_partition.py",
      "description": "Four-way: CDC + DV + MERGE + partition. 1. INSERT 200 rows across 4 partitions (W, X, Y, Z -- 50 each) 2. MERGE from 250-row CTE: WHEN MATCHED THEN UPDATE; WHEN NOT MATCHED THEN INSERT",
      "status": "pass",
      "duration_ms": 1385,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:49.484665+00:00",
      "read_cold_ms": 637,
      "read_warm_ms": 270,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 801,
      "write_warm_ms": 622,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/469_constraint_not_null",
      "num": 469,
      "name": "constraint_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/469_constraint_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_469_constraint_not_null.py",
      "description": "NOT NULL constraint + DML. Tests NOT NULL enforcement during UPDATE. 1. INSERT 100 rows with all non-null values 2. UPDATE SET score = NULL WHERE id <= 20 (score is nullable, should work) 3. DELETE WHERE id % 4 = 0",
      "status": "pass",
      "duration_ms": 7042,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:06:55.689566+00:00",
      "read_cold_ms": 2446,
      "read_warm_ms": 3586,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 205,
      "write_warm_ms": 218,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/46_variant_parquet_shredding",
      "num": 46,
      "name": "variant_parquet_shredding",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/46_variant_parquet_shredding.sql",
      "read_script": "generator/spark-reads-df/verify_46_variant_parquet_shredding.py",
      "description": "Demonstrates Variant data shredding in Parquet format. Optimizes Variant storage by extracting common fields into typed columns.",
      "status": "pass",
      "duration_ms": 6027,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:07:01.718098+00:00",
      "read_cold_ms": 2844,
      "read_warm_ms": 700,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 160,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/470_many_small_inserts",
      "num": 470,
      "name": "many_small_inserts",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/470_many_small_inserts.sql",
      "read_script": "generator/spark-reads-df/verify_470_many_small_inserts.py",
      "description": "Many small INSERT batches (20 batches of 10 rows). Tests fragmented table then DML. 1. 20 INSERT batches: batch 1 = ids 1-10, batch 2 = ids 11-20, ..., batch 20 = ids 191-200 2. DELETE WHERE batch_num <= 5 (removes 50 rows) 3. UPDATE SET payload = CONCAT('updated_', payload)...",
      "status": "pass",
      "duration_ms": 8175,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:07:09.894089+00:00",
      "read_cold_ms": 6078,
      "read_warm_ms": 1068,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1246,
      "write_warm_ms": 1049,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/471_optimize_partition",
      "num": 471,
      "name": "optimize_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/471_optimize_partition.sql",
      "read_script": "generator/spark-reads-df/verify_471_optimize_partition.py",
      "description": "OPTIMIZE on partitioned table then DML. Tests per-partition compaction. 1. INSERT 150 rows in 3 batches of 50 (creates 3 data files per partition) 2. OPTIMIZE to compact data files 3. DELETE WHERE id % 6 = 0 (25 rows removed) 4. UPDATE SET value = 0 WHERE region = 'US' AND id %...",
      "status": "pass",
      "duration_ms": 1122,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:50.607234+00:00",
      "read_cold_ms": 661,
      "read_warm_ms": 246,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 344,
      "write_warm_ms": 249,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/472_cdc_delete_update_merge",
      "num": 472,
      "name": "cdc_delete_update_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/472_cdc_delete_update_merge.sql",
      "read_script": "generator/spark-reads-df/verify_472_cdc_delete_update_merge.py",
      "description": "CDC with all 3 DML types (DELETE, UPDATE, MERGE) in one script. Tests that CDF captures all operation types. 1. INSERT 200 rows 2. DELETE WHERE id % 10 = 0 (removes 20 rows) 3. UPDATE SET status = 'modified' WHERE id <= 50 (updates surviving rows with id <= 50) 4. MERGE from...",
      "status": "pass",
      "duration_ms": 1466,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:39:06.635394+00:00",
      "read_cold_ms": 738,
      "read_warm_ms": 240,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 96,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/473_colmap_rename_merge",
      "num": 473,
      "name": "colmap_rename_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/473_colmap_rename_merge.sql",
      "read_script": "generator/spark-reads-df/verify_473_colmap_rename_merge.py",
      "description": "Column mapping + RENAME COLUMN + MERGE using the renamed column. Tests that MERGE works correctly after a column rename. 1. INSERT 100 rows with old_val column 2. ALTER TABLE RENAME COLUMN old_val TO display_val 3. MERGE from 120-row CTE: WHEN MATCHED UPDATE SET display_val...",
      "status": "pass",
      "duration_ms": 7593,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:07:31.065284+00:00",
      "read_cold_ms": 2606,
      "read_warm_ms": 3813,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 75,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/474_partition_evolve_merge",
      "num": 474,
      "name": "partition_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/474_partition_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_474_partition_evolve_merge.py",
      "description": "Partition + schema evolution (ADD COLUMN) + MERGE. Three-way combination. 1. INSERT 120 rows across 3 regions 2. ALTER TABLE ADD COLUMN priority INT 3. MERGE from 150-row CTE with priority set: WHEN MATCHED UPDATE SET priority; WHEN NOT MATCHED INSERT",
      "status": "pass",
      "duration_ms": 1101,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:52.822123+00:00",
      "read_cold_ms": 602,
      "read_warm_ms": 194,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 119,
      "write_warm_ms": 114,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/475_dv_cdc_delete_reinsert",
      "num": 475,
      "name": "dv_cdc_delete_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/475_dv_cdc_delete_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_475_dv_cdc_delete_reinsert.py",
      "description": "DV + CDC + delete-then-reinsert pattern. Tests CDF for delete+insert of same keys. 1. INSERT 100 rows (version_tag = 1) 2. DELETE WHERE id <= 20 (removes 20 rows) 3. INSERT 20 rows (id 1-20, version_tag = 2)",
      "status": "pass",
      "duration_ms": 6111,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:07:43.746340+00:00",
      "read_cold_ms": 2474,
      "read_warm_ms": 993,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 134,
      "write_warm_ms": 76,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/476_constraint_evolve_merge",
      "num": 476,
      "name": "constraint_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/476_constraint_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_476_constraint_evolve_merge.py",
      "description": "Constraint + schema evolution (ADD COLUMN) + MERGE. Three-way combination. 1. INSERT 80 rows with score = (i * 53) % 100 2. ALTER TABLE ADD CONSTRAINT score_valid CHECK (score >= 0 AND score <= 100) 3. ALTER TABLE ADD COLUMN tag STRING 4. MERGE from 100-row CTE with tag and...",
      "status": "pass",
      "duration_ms": 1479,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:54.302120+00:00",
      "read_cold_ms": 804,
      "read_warm_ms": 301,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 92,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/477_partition_constraint_merge",
      "num": 477,
      "name": "partition_constraint_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/477_partition_constraint_merge.sql",
      "read_script": "generator/spark-reads-df/verify_477_partition_constraint_merge.py",
      "description": "Partition + constraint + MERGE. Three-way combination. 1. INSERT 120 rows across 3 regions 2. ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) 3. MERGE from 150-row CTE (all value > 0): WHEN MATCHED UPDATE value; WHEN NOT MATCHED INSERT",
      "status": "pass",
      "duration_ms": 1282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:55.585488+00:00",
      "read_cold_ms": 672,
      "read_warm_ms": 314,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 100,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/478_colmap_cdc_evolve",
      "num": 478,
      "name": "colmap_cdc_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/478_colmap_cdc_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_478_colmap_cdc_evolve.py",
      "description": "Column mapping + CDC + schema evolution (ADD COLUMN). Three-way combination. 1. INSERT 100 rows 2. ALTER TABLE ADD COLUMN extra STRING 3. INSERT 50 rows (id 101-150) with extra populated 4. UPDATE SET extra = 'filled' WHERE id <= 30",
      "status": "pass",
      "duration_ms": 4746,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:07:59.920361+00:00",
      "read_cold_ms": 2639,
      "read_warm_ms": 1082,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 54,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/479_delete_between_range",
      "num": 479,
      "name": "delete_between_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/479_delete_between_range.sql",
      "read_script": "generator/spark-reads-df/verify_479_delete_between_range.py",
      "description": "DELETE with BETWEEN range predicate. Tests range-based deletion. 1. INSERT 500 rows 2. DELETE WHERE id BETWEEN 100 AND 200 (removes 101 rows) 3. UPDATE SET category = 'remaining' WHERE id BETWEEN 201 AND 300",
      "status": "pass",
      "duration_ms": 1186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:56.771774+00:00",
      "read_cold_ms": 649,
      "read_warm_ms": 275,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 39,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/47_variant_combined_with_features",
      "num": 47,
      "name": "variant_combined_with_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/47_variant_combined_with_features.sql",
      "read_script": "generator/spark-reads-df/verify_47_variant_combined_with_features.py",
      "description": "Demonstrates Variant data combined with other Delta features like CDC. Shows Variant type working alongside Change Data Feed, deletion vectors, etc.",
      "status": "pass",
      "duration_ms": 9957,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:14.226211+00:00",
      "read_cold_ms": 4351,
      "read_warm_ms": 1089,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1456,
      "write_warm_ms": 2638,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/480_update_string_ops",
      "num": 480,
      "name": "update_string_ops",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/480_update_string_ops.sql",
      "read_script": "generator/spark-reads-df/verify_480_update_string_ops.py",
      "description": "UPDATE with string operations (CONCAT). Tests string manipulation in SET clauses. 1. INSERT 100 rows with first_name, last_name, empty full_name and email 2. UPDATE SET full_name = CONCAT(first_name, '_', last_name) 3. UPDATE SET email = CONCAT(first_name, '.', last_name...",
      "status": "pass",
      "duration_ms": 7096,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:21.323325+00:00",
      "read_cold_ms": 4494,
      "read_warm_ms": 1352,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 82,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/481_merge_selective_update",
      "num": 481,
      "name": "merge_selective_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/481_merge_selective_update.sql",
      "read_script": "generator/spark-reads-df/verify_481_merge_selective_update.py",
      "description": "MERGE where only some matched rows are updated (conditional WHEN MATCHED). No WHEN NOT MATCHED clause -- source fully overlaps target. 1. INSERT 200 rows: score = (i * 53) % 100, tier = 'bronze' 2. MERGE from 200-row CTE: WHEN MATCHED AND target.score >= 80 THEN UPDATE SET tier...",
      "status": "pass",
      "duration_ms": 3648,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:24.972508+00:00",
      "read_cold_ms": 2217,
      "read_warm_ms": 788,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/482_dv_multi_delete",
      "num": 482,
      "name": "dv_multi_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/482_dv_multi_delete.sql",
      "read_script": "generator/spark-reads-df/verify_482_dv_multi_delete.py",
      "description": "Multiple DELETE operations creating accumulated deletion vectors. Tests DV stacking correctness across successive deletes. 1. INSERT 500 rows with round_num=0 2. DELETE WHERE id%2=0 (250 removed, round 1) 3. DELETE WHERE id%3=0 (of remaining 250 odd ids, removes ~83, round 2) 4...",
      "status": "pass",
      "duration_ms": 1168,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:57.940216+00:00",
      "read_cold_ms": 692,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 50,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/483_update_arithmetic",
      "num": 483,
      "name": "update_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/483_update_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_483_update_arithmetic.py",
      "description": "UPDATE with arithmetic expressions. Tests complex SET expressions computing multiple derived columns in a single UPDATE. 1. INSERT 100 rows with a, b populated, computed columns zeroed 2. UPDATE SET sum_ab=a+b, product_ab=a*b, ratio=ROUND(a/b, 4)",
      "status": "pass",
      "duration_ms": 4787,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:36.085311+00:00",
      "read_cold_ms": 2907,
      "read_warm_ms": 988,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 94,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/484_partition_null_update",
      "num": 484,
      "name": "partition_null_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/484_partition_null_update.sql",
      "read_script": "generator/spark-reads-df/verify_484_partition_null_update.py",
      "description": "Partitioned table with NULL partition values + UPDATE targeting the NULL partition. Tests that DV-based UPDATE correctly handles NULL partition keys. 1. INSERT 120 rows across 4 partitions (US, EU, AP, NULL) 2. UPDATE SET value=value*3 WHERE region IS NULL (30 rows) 3. DELETE...",
      "status": "pass",
      "duration_ms": 6375,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:42.462195+00:00",
      "read_cold_ms": 4266,
      "read_warm_ms": 993,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 137,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/485_cdc_dv_update_chain",
      "num": 485,
      "name": "cdc_dv_update_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/485_cdc_dv_update_chain.sql",
      "read_script": "generator/spark-reads-df/verify_485_cdc_dv_update_chain.py",
      "description": "CDC + DV + chained UPDATEs. Tests that CDF captures each UPDATE version and that successive DV-based updates on overlapping row sets work correctly. 1. INSERT 100 rows: counter=0, status='v0' 2. UPDATE counter+1, status='v1' WHERE id<=50 3. UPDATE counter+1, status='v2' WHERE...",
      "status": "pass",
      "duration_ms": 1196,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:35:59.136557+00:00",
      "read_cold_ms": 612,
      "read_warm_ms": 214,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 224,
      "write_warm_ms": 200,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/486_colmap_dv_optimize",
      "num": 486,
      "name": "colmap_dv_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/486_colmap_dv_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_486_colmap_dv_optimize.py",
      "description": "Column mapping + DV + OPTIMIZE. Tests that OPTIMIZE correctly compacts files on a column-mapped table after DV-based deletes. 1. INSERT 100 rows (batch 1, id 1-100) 2. INSERT 100 rows (batch 2, id 101-200) 3. INSERT 100 rows (batch 3, id 201-300) 4. DELETE WHERE id%5=0 (60 rows...",
      "status": "pass",
      "duration_ms": 4211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:53.468566+00:00",
      "read_cold_ms": 2765,
      "read_warm_ms": 665,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 282,
      "write_warm_ms": 185,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/487_evolve_constraint_delete",
      "num": 487,
      "name": "evolve_constraint_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/487_evolve_constraint_delete.sql",
      "read_script": "generator/spark-reads-df/verify_487_evolve_constraint_delete.py",
      "description": "Schema evolution + constraint + DELETE. Three-way feature interaction. 1. INSERT 100 rows 2. ALTER TABLE ADD COLUMN priority INT 3. ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) 4. UPDATE SET priority = id % 5 5. DELETE WHERE priority=0 AND value<10.0",
      "status": "pass",
      "duration_ms": 3827,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:08:57.296288+00:00",
      "read_cold_ms": 2225,
      "read_warm_ms": 773,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 215,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/488_merge_partition_evolve",
      "num": 488,
      "name": "merge_partition_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/488_merge_partition_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_488_merge_partition_evolve.py",
      "description": "MERGE + partition + schema evolution. Three-way feature interaction. 1. INSERT 120 rows across 3 regions 2. ALTER TABLE ADD COLUMN flag BOOLEAN 3. MERGE from 150-row CTE: MATCHED->UPDATE flag=true, NOT MATCHED->INSERT with flag=false",
      "status": "pass",
      "duration_ms": 6624,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:03.921086+00:00",
      "read_cold_ms": 2391,
      "read_warm_ms": 3003,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 124,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/489_dv_cdc_colmap",
      "num": 489,
      "name": "dv_cdc_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/489_dv_cdc_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_489_dv_cdc_colmap.py",
      "description": "DV + CDC + column mapping. Three-way feature interaction. Tests that deletion vectors, change data feed, and column mapping all work correctly together. 1. INSERT 200 rows 2. DELETE WHERE id%8=0 (25 rows removed via DVs) 3. UPDATE SET amount=amount+100 WHERE active=true",
      "status": "pass",
      "duration_ms": 4681,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:08.602769+00:00",
      "read_cold_ms": 2508,
      "read_warm_ms": 772,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 42,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/48_writer_atomic_log_entry_creation",
      "num": 48,
      "name": "writer_atomic_log_entry_creation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/48_writer_atomic_log_entry_creation.sql",
      "read_script": "generator/spark-reads-df/verify_48_writer_atomic_log_entry_creation.py",
      "description": "Demonstrates atomic creation of transaction log entries. Writers MUST never overwrite an existing log entry and should use atomic primitives of the underlying filesystem to ensure concurrent writers do not overwrite each other's entries.",
      "status": "pass",
      "duration_ms": 6838,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:15.441448+00:00",
      "read_cold_ms": 1859,
      "read_warm_ms": 800,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 233,
      "write_warm_ms": 161,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/490_optimize_constraint",
      "num": 490,
      "name": "optimize_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/490_optimize_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_490_optimize_constraint.py",
      "description": "OPTIMIZE + constraint interaction. Tests that constraint metadata survives OPTIMIZE file compaction. 1. INSERT 200 rows in 4 batches of 50 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0) 3. DELETE WHERE id%6=0 (~33 rows removed) 4. OPTIMIZE 5. INSERT 30 rows (id...",
      "status": "pass",
      "duration_ms": 2666,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:18.108327+00:00",
      "read_cold_ms": 1597,
      "read_warm_ms": 445,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
      "write_warm_ms": 313,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/491_merge_cdc_constraint",
      "num": 491,
      "name": "merge_cdc_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/491_merge_cdc_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_491_merge_cdc_constraint.py",
      "description": "MERGE + CDC + constraint. Three-way feature interaction. Tests that MERGE respects CHECK constraint and CDF captures merge changes. 1. INSERT 80 rows 2. ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) 3. MERGE from 100-row CTE: MATCHED->UPDATE value+tag, NOT MATCHED->INSERT",
      "status": "pass",
      "duration_ms": 5548,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:23.657988+00:00",
      "read_cold_ms": 3220,
      "read_warm_ms": 780,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 190,
      "write_warm_ms": 139,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/492_five_way_combo",
      "num": 492,
      "name": "five_way_combo",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/492_five_way_combo.sql",
      "read_script": "generator/spark-reads-df/verify_492_five_way_combo.py",
      "description": "Five-way stress test: DV + CDC + partition + constraint + schema evolution. Exercises every major Delta feature in a single table lifecycle. 1. INSERT 150 rows across 3 regions (US, EU, AP -- 50 each) 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0 AND score <= 100) 3...",
      "status": "pass",
      "duration_ms": 7929,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:31.588304+00:00",
      "read_cold_ms": 4522,
      "read_warm_ms": 1220,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 119,
      "write_warm_ms": 138,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/493_dv_cdc_optimize_dml",
      "num": 493,
      "name": "dv_cdc_optimize_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/493_dv_cdc_optimize_dml.sql",
      "read_script": "generator/spark-reads-df/verify_493_dv_cdc_optimize_dml.py",
      "description": "DV + CDC + OPTIMIZE + post-OPTIMIZE DML. Tests the full lifecycle: batched inserts, deletion vectors, OPTIMIZE compaction, then further DML. 1. INSERT 200 rows in 4 batches of 50 2. DELETE WHERE id % 5 = 0 (40 rows removed via DVs) 3. OPTIMIZE (materializes DVs, should not emit...",
      "status": "pass",
      "duration_ms": 3780,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:35.369276+00:00",
      "read_cold_ms": 1847,
      "read_warm_ms": 650,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
      "write_warm_ms": 228,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/494_colmap_constraint_evolve",
      "num": 494,
      "name": "colmap_constraint_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/494_colmap_constraint_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_494_colmap_constraint_evolve.py",
      "description": "Three-way combo: column mapping (name mode) + constraint + schema evolution. Validates that column mapping interacts correctly with CHECK constraints and ADD COLUMN operations. 1. INSERT 100 rows 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0) 3. ALTER TABLE ADD COLUMN...",
      "status": "pass",
      "duration_ms": 6130,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:41.500187+00:00",
      "read_cold_ms": 2601,
      "read_warm_ms": 1022,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 130,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/495_partition_optimize_merge",
      "num": 495,
      "name": "partition_optimize_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/495_partition_optimize_merge.sql",
      "read_script": "generator/spark-reads-df/verify_495_partition_optimize_merge.py",
      "description": "Three-way combo: partition + OPTIMIZE + MERGE. Tests that OPTIMIZE on a partitioned table produces correct file layout and that MERGE works correctly against optimized partitioned data. 1. INSERT 150 rows in 3 batches of 50 (3 regions) 2. OPTIMIZE 3. MERGE from 180-row CTE: WHEN...",
      "status": "pass",
      "duration_ms": 4179,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:45.680556+00:00",
      "read_cold_ms": 2122,
      "read_warm_ms": 971,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 320,
      "write_warm_ms": 218,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/496_evolve_dv_cdc_merge",
      "num": 496,
      "name": "evolve_dv_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/496_evolve_dv_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_496_evolve_dv_cdc_merge.py",
      "description": "Four-way combo: schema evolution + DV + CDC + MERGE. Tests that MERGE works correctly after schema evolution with deletion vectors and CDC enabled. 1. INSERT 100 rows 2. ALTER TABLE ADD COLUMN priority INT 3. MERGE from 120-row CTE with priority: WHEN MATCHED UPDATE; WHEN NOT...",
      "status": "pass",
      "duration_ms": 4615,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:50.297408+00:00",
      "read_cold_ms": 2108,
      "read_warm_ms": 660,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 283,
      "write_warm_ms": 404,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/497_large_insert_delete",
      "num": 497,
      "name": "large_insert_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/497_large_insert_delete.sql",
      "read_script": "generator/spark-reads-df/verify_497_large_insert_delete.py",
      "description": "Large-scale test: 5000-row INSERT followed by bulk DELETE and UPDATE. Exercises deletion vectors and DML at scale to stress file management. 1. INSERT 5000 rows in 5 batches of 1000 2. DELETE WHERE id % 3 = 0 (~1667 rows removed) 3. UPDATE SET bucket = bucket + 10 WHERE id % 2 =...",
      "status": "pass",
      "duration_ms": 7726,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:09:58.025359+00:00",
      "read_cold_ms": 2899,
      "read_warm_ms": 2894,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 439,
      "write_warm_ms": 378,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/498_merge_delete_insert_combo",
      "num": 498,
      "name": "merge_delete_insert_combo",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/498_merge_delete_insert_combo.sql",
      "read_script": "generator/spark-reads-df/verify_498_merge_delete_insert_combo.py",
      "description": "MERGE with DELETE clause + separate INSERT after. Tests the interaction between MERGE's WHEN MATCHED THEN DELETE clause and subsequent INSERT. 1. INSERT 200 rows 2. MERGE from 100-row CTE (id 1-100): WHEN MATCHED AND score<10 THEN DELETE; WHEN MATCHED THEN UPDATE...",
      "status": "pass",
      "duration_ms": 3662,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:01.690130+00:00",
      "read_cold_ms": 1832,
      "read_warm_ms": 870,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/499_colmap_partition_merge_evolve",
      "num": 499,
      "name": "colmap_partition_merge_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/499_colmap_partition_merge_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_499_colmap_partition_merge_evolve.py",
      "description": "Four-way combo: column mapping + partition + MERGE + schema evolution. Validates that column mapping interacts correctly with partitioning, MERGE operations, and ADD COLUMN after initial data load. 1. INSERT 120 rows (3 regions, 40 each) 2. ALTER TABLE ADD COLUMN flag BOOLEAN 3...",
      "status": "pass",
      "duration_ms": 5230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:06.921541+00:00",
      "read_cold_ms": 3121,
      "read_warm_ms": 1164,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 149,
      "write_warm_ms": 193,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/49_writer_metadata_data_file_consistency",
      "num": 49,
      "name": "writer_metadata_data_file_consistency",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/49_writer_metadata_data_file_consistency.sql",
      "read_script": "generator/spark-reads-df/verify_49_writer_metadata_data_file_consistency.py",
      "description": "Demonstrates consistency between table metadata and data files.",
      "status": "pass",
      "duration_ms": 12322,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:19.247056+00:00",
      "read_cold_ms": 2728,
      "read_warm_ms": 4303,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 351,
      "write_warm_ms": 270,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/500_comprehensive_all_features",
      "num": 500,
      "name": "comprehensive_all_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/500_comprehensive_all_features.sql",
      "read_script": "generator/spark-reads-df/verify_500_comprehensive_all_features.py",
      "description": "Grand finale: the ultimate combo test exercising every major Delta feature in a single table lifecycle. DV + CDC + column mapping + partition + constraint + schema evolution + OPTIMIZE + MERGE. 1. INSERT 200 rows (4 regions: US/EU/AP/SA, 50 each) 2. ALTER TABLE ADD CONSTRAINT...",
      "status": "pass",
      "duration_ms": 7991,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:27.240696+00:00",
      "read_cold_ms": 2675,
      "read_warm_ms": 1357,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 596,
      "write_warm_ms": 854,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/501_update_delete_same_rows",
      "num": 501,
      "name": "update_delete_same_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/501_update_delete_same_rows.sql",
      "read_script": "generator/spark-reads-df/verify_501_update_delete_same_rows.py",
      "description": "UPDATE then DELETE targeting overlapping rows. Tests DV stacking on same row set -- the DELETE must correctly apply on top of already-modified rows. 1. INSERT 200 rows with status='active' 2. UPDATE SET status='flagged' WHERE id <= 80 3. DELETE WHERE status='flagged' AND value <...",
      "status": "pass",
      "duration_ms": 7030,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:34.272219+00:00",
      "read_cold_ms": 2493,
      "read_warm_ms": 3345,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 82,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/502_delete_where_false",
      "num": 502,
      "name": "delete_where_false",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/502_delete_where_false.sql",
      "read_script": "generator/spark-reads-df/verify_502_delete_where_false.py",
      "description": "DELETE WHERE false (no-op). Verifies the engine handles a zero-match DELETE correctly -- no rows should be removed, no DVs created, and the table state should remain identical to the post-INSERT state.",
      "status": "pass",
      "duration_ms": 3769,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:38.043380+00:00",
      "read_cold_ms": 2661,
      "read_warm_ms": 600,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
      "write_warm_ms": 38,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/503_update_noop",
      "num": 503,
      "name": "update_noop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/503_update_noop.sql",
      "read_script": "generator/spark-reads-df/verify_503_update_noop.py",
      "description": "UPDATE that matches zero rows. Tests no-op UPDATE behavior. The WHERE clause references a value that no row can have, so the engine must correctly handle the zero-match case.",
      "status": "pass",
      "duration_ms": 6707,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:44.754555+00:00",
      "read_cold_ms": 2568,
      "read_warm_ms": 3461,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/504_merge_zero_matches",
      "num": 504,
      "name": "merge_zero_matches",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/504_merge_zero_matches.sql",
      "read_script": "generator/spark-reads-df/verify_504_merge_zero_matches.py",
      "description": "MERGE where source and target have zero key overlap. All source rows go through the NOT MATCHED path. Tests that MERGE correctly handles pure-insert scenarios with no matched rows.",
      "status": "pass",
      "duration_ms": 2838,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:47.593612+00:00",
      "read_cold_ms": 1886,
      "read_warm_ms": 337,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 70,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/505_merge_full_match",
      "num": 505,
      "name": "merge_full_match",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/505_merge_full_match.sql",
      "read_script": "generator/spark-reads-df/verify_505_merge_full_match.py",
      "description": "MERGE where every source row matches a target row. Zero NOT MATCHED rows. Tests that MERGE correctly handles a pure-update scenario.",
      "status": "pass",
      "duration_ms": 6831,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:54.426125+00:00",
      "read_cold_ms": 2316,
      "read_warm_ms": 860,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 73,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/506_cdc_optimize_partition",
      "num": 506,
      "name": "cdc_optimize_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/506_cdc_optimize_partition.sql",
      "read_script": "generator/spark-reads-df/verify_506_cdc_optimize_partition.py",
      "description": "CDC + OPTIMIZE + partition. Three-way feature combo. Tests that CDF correctly captures changes across partitioned data, and that OPTIMIZE compacts partitioned files without data loss. 1. INSERT 60 rows (batch 1: id 1-60, 3 regions) 2. INSERT 60 rows (batch 2: id 61-120, 3...",
      "status": "pass",
      "duration_ms": 4694,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:10:59.121078+00:00",
      "read_cold_ms": 2316,
      "read_warm_ms": 745,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 435,
      "write_warm_ms": 305,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/507_constraint_partition_evolve",
      "num": 507,
      "name": "constraint_partition_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/507_constraint_partition_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_507_constraint_partition_evolve.py",
      "description": "Constraint + partition + schema evolution. Three-way feature combo. Tests that CHECK constraints are enforced after schema evolution (adding a column), and that DML operations respect both constraints and partition structure. 1. INSERT 120 rows (3 regions, 40 each) 2. ALTER...",
      "status": "pass",
      "duration_ms": 6743,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:11:05.865049+00:00",
      "read_cold_ms": 4766,
      "read_warm_ms": 769,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/508_colmap_cdc_merge_dml",
      "num": 508,
      "name": "colmap_cdc_merge_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/508_colmap_cdc_merge_dml.sql",
      "read_script": "generator/spark-reads-df/verify_508_colmap_cdc_merge_dml.py",
      "description": "Column mapping + CDC + MERGE + subsequent DML. Four-way feature combo. Tests that column mapping (name mode) works correctly with CDF tracking through a MERGE and a subsequent DELETE. 1. INSERT 100 rows with status='new' 2. MERGE from 120-row CTE: MATCHED->UPDATE...",
      "status": "pass",
      "duration_ms": 4528,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:11:10.394760+00:00",
      "read_cold_ms": 2359,
      "read_warm_ms": 747,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 180,
      "write_warm_ms": 213,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/509_dv_partition_optimize_merge",
      "num": 509,
      "name": "dv_partition_optimize_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/509_dv_partition_optimize_merge.sql",
      "read_script": "generator/spark-reads-df/verify_509_dv_partition_optimize_merge.py",
      "description": "DV + partition + OPTIMIZE + MERGE. Four-way feature combo. Tests that deletion vectors survive OPTIMIZE compaction in a partitioned table, and that a subsequent MERGE correctly handles rows that were previously deleted and compacted away. 1. INSERT 50 rows batch 1 (id 1-50, 3...",
      "status": "pass",
      "duration_ms": 6832,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:11:17.227847+00:00",
      "read_cold_ms": 2318,
      "read_warm_ms": 3326,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 306,
      "write_warm_ms": 231,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/50_writer_checkpoint_metadata_cleanup",
      "num": 50,
      "name": "writer_checkpoint_metadata_cleanup",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/50_writer_checkpoint_metadata_cleanup.sql",
      "read_script": "generator/spark-reads-df/verify_50_writer_checkpoint_metadata_cleanup.py",
      "description": "Demonstrates checkpoint creation and old log file cleanup. accumulates many Delta log files. Checkpoints consolidate the metadata state, allowing safe cleanup of old log entries.",
      "status": "pass",
      "duration_ms": 5837,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:11:23.065878+00:00",
      "read_cold_ms": 2052,
      "read_warm_ms": 450,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 370,
      "write_warm_ms": 255,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/510_cdc_colmap_constraint_evolve",
      "num": 510,
      "name": "cdc_colmap_constraint_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/510_cdc_colmap_constraint_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_510_cdc_colmap_constraint_evolve.py",
      "description": "CDC + column mapping + constraint + schema evolution. Four-way feature combo. Tests that CDF correctly tracks changes under column mapping mode=name, with a CHECK constraint enforced, after schema evolution adds a new column. 1. INSERT 100 rows with score=(i*53)%100 2. ALTER...",
      "status": "pass",
      "duration_ms": 5286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:11:28.353134+00:00",
      "read_cold_ms": 2588,
      "read_warm_ms": 1001,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 84,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/511_large_merge_update",
      "num": 511,
      "name": "large_merge_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/511_large_merge_update.sql",
      "read_script": "generator/spark-reads-df/verify_511_large_merge_update.py",
      "description": "Large-scale MERGE with UPDATE only (2000 rows, full overlap). All source rows match target rows, so only the WHEN MATCHED branch fires. Tests DV performance under bulk UPDATE via MERGE.",
      "status": "pass",
      "duration_ms": 1082,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:00.219008+00:00",
      "read_cold_ms": 615,
      "read_warm_ms": 207,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/512_large_delete",
      "num": 512,
      "name": "large_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/512_large_delete.sql",
      "read_script": "generator/spark-reads-df/verify_512_large_delete.py",
      "description": "Large-scale DELETE (10000 rows, 90% deleted). Tests mass deletion vector creation under heavy DELETE load. Only 1 in 10 rows survives.",
      "status": "pass",
      "duration_ms": 1311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:01.531107+00:00",
      "read_cold_ms": 723,
      "read_warm_ms": 300,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 73,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/513_twenty_version_stress",
      "num": 513,
      "name": "twenty_version_stress",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/513_twenty_version_stress.sql",
      "read_script": "generator/spark-reads-df/verify_513_twenty_version_stress.py",
      "description": "20+ versions of mixed DML. Tests long version chain with interleaved INSERT, UPDATE, and DELETE operations across many commits.",
      "status": "pass",
      "duration_ms": 1631,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:03.162403+00:00",
      "read_cold_ms": 942,
      "read_warm_ms": 279,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 678,
      "write_warm_ms": 936,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/514_date_column_dml",
      "num": 514,
      "name": "date_column_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/514_date_column_dml.sql",
      "read_script": "generator/spark-reads-df/verify_514_date_column_dml.py",
      "description": "DATE columns with DML using date predicates. Uses arrow_cast for Date32 to ensure deterministic date handling without relying on string parsing.",
      "status": "pass",
      "duration_ms": 1148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:04.311313+00:00",
      "read_cold_ms": 682,
      "read_warm_ms": 221,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 64,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/515_multiple_type_columns",
      "num": 515,
      "name": "multiple_type_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/515_multiple_type_columns.sql",
      "read_script": "generator/spark-reads-df/verify_515_multiple_type_columns.py",
      "description": "Many column types in one table with DML operations. Tests that type diversity is preserved correctly through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 6335,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:11:57.600831+00:00",
      "read_cold_ms": 2740,
      "read_warm_ms": 2471,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 70,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/516_partition_three_col",
      "num": 516,
      "name": "partition_three_col",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/516_partition_three_col.sql",
      "read_script": "generator/spark-reads-df/verify_516_partition_three_col.py",
      "description": "Three-column partitioning (year, month, region) with DML operations. Tests partition pruning and DV creation across a multi-level partition scheme.",
      "status": "pass",
      "duration_ms": 4841,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:02.448031+00:00",
      "read_cold_ms": 2635,
      "read_warm_ms": 1262,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
      "write_warm_ms": 178,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/517_cdc_dv_delete_reinsert_merge",
      "num": 517,
      "name": "cdc_dv_delete_reinsert_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/517_cdc_dv_delete_reinsert_merge.sql",
      "read_script": "generator/spark-reads-df/verify_517_cdc_dv_delete_reinsert_merge.py",
      "description": "CDC + DV + delete-reinsert + MERGE. Complex lifecycle where rows are deleted and re-inserted with different generation markers, then a MERGE unifies everything. Tests CDF tracking across multiple lifecycle stages.",
      "status": "pass",
      "duration_ms": 1446,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:05.757872+00:00",
      "read_cold_ms": 718,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 167,
      "write_warm_ms": 187,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/518_evolve_three_adds",
      "num": 518,
      "name": "evolve_three_adds",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/518_evolve_three_adds.sql",
      "read_script": "generator/spark-reads-df/verify_518_evolve_three_adds.py",
      "description": "Three sequential ALTER TABLE ADD COLUMN operations with DML after each. Tests schema evolution where new columns are added incrementally and existing rows gain values through UPDATE.",
      "status": "pass",
      "duration_ms": 8126,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:14.000620+00:00",
      "read_cold_ms": 2646,
      "read_warm_ms": 4081,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 184,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/519_colmap_partition_evolve_merge",
      "num": 519,
      "name": "colmap_partition_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/519_colmap_partition_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_519_colmap_partition_evolve_merge.py",
      "description": "Column mapping (name mode) + partitioning + schema evolution + MERGE. Four-way feature interaction test. Column mapping with name mode requires minReaderVersion=2, minWriterVersion=5.",
      "status": "pass",
      "duration_ms": 7816,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:21.817360+00:00",
      "read_cold_ms": 2932,
      "read_warm_ms": 980,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 181,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/51_writer_version_requirements_matrix",
      "num": 51,
      "name": "writer_version_requirements_matrix",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/51_writer_version_requirements_matrix.sql",
      "read_script": "generator/spark-reads-df/verify_51_writer_version_requirements_matrix.py",
      "description": "Demonstrates writer version requirements for different features.",
      "status": "pass",
      "duration_ms": 4831,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:26.649720+00:00",
      "read_cold_ms": 2625,
      "read_warm_ms": 1217,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 212,
      "write_warm_ms": 132,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/520_optimize_cdc_constraint",
      "num": 520,
      "name": "optimize_cdc_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/520_optimize_cdc_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_520_optimize_cdc_constraint.py",
      "description": "OPTIMIZE + CDC + constraint. Three-way feature interaction. Tests that OPTIMIZE compaction works correctly with CDC enabled and a CHECK constraint enforced.",
      "status": "pass",
      "duration_ms": 5992,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:32.642666+00:00",
      "read_cold_ms": 1649,
      "read_warm_ms": 2841,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 250,
      "write_warm_ms": 276,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/521_merge_chain",
      "num": 521,
      "name": "merge_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/521_merge_chain.sql",
      "read_script": "generator/spark-reads-df/verify_521_merge_chain.py",
      "description": "Two sequential MERGEs on the same table. Tests MERGE-after-MERGE correctness where the second MERGE operates on data already modified by the first.",
      "status": "pass",
      "duration_ms": 5031,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:37.675290+00:00",
      "read_cold_ms": 2683,
      "read_warm_ms": 1201,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/522_update_chain_different_cols",
      "num": 522,
      "name": "update_chain_different_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/522_update_chain_different_cols.sql",
      "read_script": "generator/spark-reads-df/verify_522_update_chain_different_cols.py",
      "description": "Three UPDATEs each modifying different columns on overlapping row ranges. Tests that independent column updates do not interfere with each other when applied via deletion vectors.",
      "status": "pass",
      "duration_ms": 4605,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:42.281713+00:00",
      "read_cold_ms": 2488,
      "read_warm_ms": 1089,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 64,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/523_delete_then_merge",
      "num": 523,
      "name": "delete_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/523_delete_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_523_delete_then_merge.py",
      "description": "DELETE followed by MERGE on the same table. Tests that MERGE correctly operates on a table containing deletion vectors from a prior DELETE. The deleted rows are re-inserted by the MERGE's NOT MATCHED branch.",
      "status": "pass",
      "duration_ms": 7629,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:49.912131+00:00",
      "read_cold_ms": 5618,
      "read_warm_ms": 923,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 118,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/524_merge_then_delete",
      "num": 524,
      "name": "merge_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/524_merge_then_delete.sql",
      "read_script": "generator/spark-reads-df/verify_524_merge_then_delete.py",
      "description": "MERGE followed by DELETE. Tests that DELETE correctly operates on data that was just inserted/updated by a MERGE operation.",
      "status": "pass",
      "duration_ms": 7345,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:12:57.258597+00:00",
      "read_cold_ms": 2587,
      "read_warm_ms": 3754,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 130,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/525_partition_delete_all_one",
      "num": 525,
      "name": "partition_delete_all_one",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/525_partition_delete_all_one.sql",
      "read_script": "generator/spark-reads-df/verify_525_partition_delete_all_one.py",
      "description": "DELETE all rows from a single partition, then UPDATE a different partition. Tests partition-level deletion where an entire partition's data files are removed, followed by an UPDATE on surviving partitions.",
      "status": "pass",
      "duration_ms": 7707,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:04.966940+00:00",
      "read_cold_ms": 2867,
      "read_warm_ms": 1053,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 60,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/526_cdc_dv_colmap_partition",
      "num": 526,
      "name": "cdc_dv_colmap_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/526_cdc_dv_colmap_partition.sql",
      "read_script": "generator/spark-reads-df/verify_526_cdc_dv_colmap_partition.py",
      "description": "Four-way feature combination: CDC (Change Data Feed) + Deletion Vectors + Column Mapping (name mode) + Partitioning. Tests that all four features coexist and produce correct data after UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 4833,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:09.804267+00:00",
      "read_cold_ms": 2400,
      "read_warm_ms": 786,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 142,
      "write_warm_ms": 65,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/527_constraint_merge_update",
      "num": 527,
      "name": "constraint_merge_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/527_constraint_merge_update.sql",
      "read_script": "generator/spark-reads-df/verify_527_constraint_merge_update.py",
      "description": "CHECK constraint + MERGE + subsequent UPDATE. Tests that the constraint (value > 0) is enforced across a DML chain: the MERGE inserts/updates only valid rows, and the UPDATE further modifies values while keeping them within the constraint.",
      "status": "pass",
      "duration_ms": 3593,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:13.401172+00:00",
      "read_cold_ms": 2093,
      "read_warm_ms": 752,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 60,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/528_evolve_partition_cdc",
      "num": 528,
      "name": "evolve_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/528_evolve_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_528_evolve_partition_cdc.py",
      "description": "Three-way combination: Schema evolution (ADD COLUMN) + Partitioning + CDC. Tests that a column added after initial data load works correctly with partitioned data and change data feed tracking.",
      "status": "pass",
      "duration_ms": 7754,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:21.156154+00:00",
      "read_cold_ms": 5003,
      "read_warm_ms": 1193,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 169,
      "write_warm_ms": 150,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/529_dv_cdc_optimize_merge",
      "num": 529,
      "name": "dv_cdc_optimize_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/529_dv_cdc_optimize_merge.sql",
      "read_script": "generator/spark-reads-df/verify_529_dv_cdc_optimize_merge.py",
      "description": "Four-way combination: Deletion Vectors + CDC + OPTIMIZE + MERGE. Tests that MERGE works correctly after OPTIMIZE has materialized deletion vectors into compacted files. The OPTIMIZE step converts logical deletes (DVs) into physical deletes, then MERGE must correctly match...",
      "status": "pass",
      "duration_ms": 7837,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:28.994848+00:00",
      "read_cold_ms": 1942,
      "read_warm_ms": 3111,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 737,
      "write_warm_ms": 809,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/52_writer_append_only_enforcement",
      "num": 52,
      "name": "writer_append_only_enforcement",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/52_writer_append_only_enforcement.sql",
      "read_script": "generator/spark-reads-df/verify_52_writer_append_only_enforcement.py",
      "description": "Demonstrates append-only table constraint where only INSERT operations are allowed. Writers MUST NOT perform DELETE, UPDATE, or MERGE operations on append-only tables.",
      "status": "pass",
      "duration_ms": 5144,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:34.140240+00:00",
      "read_cold_ms": 1698,
      "read_warm_ms": 2651,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 147,
      "write_warm_ms": 136,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/530_colmap_constraint_merge",
      "num": 530,
      "name": "colmap_constraint_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/530_colmap_constraint_merge.sql",
      "read_script": "generator/spark-reads-df/verify_530_colmap_constraint_merge.py",
      "description": "Three-way combination: Column Mapping (name mode) + CHECK constraint + MERGE. Tests that a CHECK constraint (score >= 0 AND score <= 100) is enforced when MERGE inserts and updates rows on a column-mapped table.",
      "status": "pass",
      "duration_ms": 7409,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:41.550906+00:00",
      "read_cold_ms": 2824,
      "read_warm_ms": 3538,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 91,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/531_update_set_from_id",
      "num": 531,
      "name": "update_set_from_id",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/531_update_set_from_id.sql",
      "read_script": "generator/spark-reads-df/verify_531_update_set_from_id.py",
      "description": "UPDATE SET column values derived from id using mathematical expressions. Tests that UPDATE can compute multiple columns from existing column values.",
      "status": "pass",
      "duration_ms": 1117,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:06.875243+00:00",
      "read_cold_ms": 647,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 93,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/532_delete_modular",
      "num": 532,
      "name": "delete_modular",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/532_delete_modular.sql",
      "read_script": "generator/spark-reads-df/verify_532_delete_modular.py",
      "description": "DELETE with multiple modular predicates in sequence. Tests that each DELETE operates independently on the surviving rows from the previous DELETE.",
      "status": "pass",
      "duration_ms": 4070,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:50.201902+00:00",
      "read_cold_ms": 2028,
      "read_warm_ms": 951,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 94,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/533_partition_insert_new",
      "num": 533,
      "name": "partition_insert_new",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/533_partition_insert_new.sql",
      "read_script": "generator/spark-reads-df/verify_533_partition_insert_new.py",
      "description": "INSERT rows into a new partition value that did not exist at CREATE time. Tests dynamic partition creation and UPDATE within that new partition.",
      "status": "pass",
      "duration_ms": 6747,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:13:56.949939+00:00",
      "read_cold_ms": 4738,
      "read_warm_ms": 809,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 107,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/534_cdc_schema_evolve_merge",
      "num": 534,
      "name": "cdc_schema_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/534_cdc_schema_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_534_cdc_schema_evolve_merge.py",
      "description": "CDC + schema evolution + MERGE. Tests that Change Data Feed works correctly across a schema change (ADD COLUMN) combined with a MERGE operation.",
      "status": "pass",
      "duration_ms": 7997,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:04.948455+00:00",
      "read_cold_ms": 1911,
      "read_warm_ms": 3710,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 306,
      "write_warm_ms": 304,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/535_optimize_delete_optimize",
      "num": 535,
      "name": "optimize_delete_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/535_optimize_delete_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_535_optimize_delete_optimize.py",
      "description": "OPTIMIZE then DELETE then OPTIMIZE again. Tests double compaction where the second OPTIMIZE materializes deletion vectors left by the DELETE on the already-compacted files from the first OPTIMIZE.",
      "status": "pass",
      "duration_ms": 4041,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:08.990440+00:00",
      "read_cold_ms": 2222,
      "read_warm_ms": 908,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 338,
      "write_warm_ms": 242,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/536_merge_update_specific_cols",
      "num": 536,
      "name": "merge_update_specific_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/536_merge_update_specific_cols.sql",
      "read_script": "generator/spark-reads-df/verify_536_merge_update_specific_cols.py",
      "description": "MERGE that only updates specific columns (not all). Tests partial column update in MERGE where only score changes while name, category, and status are preserved from the original row.",
      "status": "pass",
      "duration_ms": 1068,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:07.943783+00:00",
      "read_cold_ms": 619,
      "read_warm_ms": 213,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/537_dv_cdc_partition_delete_update",
      "num": 537,
      "name": "dv_cdc_partition_delete_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/537_dv_cdc_partition_delete_update.sql",
      "read_script": "generator/spark-reads-df/verify_537_dv_cdc_partition_delete_update.py",
      "description": "DV + CDC + partition + DELETE + UPDATE. Tests combined DML operations on a partitioned table with both deletion vectors and change data feed enabled.",
      "status": "pass",
      "duration_ms": 7449,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:19.802961+00:00",
      "read_cold_ms": 4799,
      "read_warm_ms": 1044,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 134,
      "write_warm_ms": 192,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/538_constraint_two_checks",
      "num": 538,
      "name": "constraint_two_checks",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/538_constraint_two_checks.sql",
      "read_script": "generator/spark-reads-df/verify_538_constraint_two_checks.py",
      "description": "Two independent CHECK constraints on the same table followed by DML. Tests that both constraints are enforced simultaneously and that valid UPDATE and DELETE operations succeed.",
      "status": "pass",
      "duration_ms": 8096,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:27.900078+00:00",
      "read_cold_ms": 5709,
      "read_warm_ms": 1073,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 113,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/539_evolve_rename_add",
      "num": 539,
      "name": "evolve_rename_add",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/539_evolve_rename_add.sql",
      "read_script": "generator/spark-reads-df/verify_539_evolve_rename_add.py",
      "description": "RENAME COLUMN then ADD COLUMN. Tests stacked schema evolution where a column is renamed first, then a new column is added, followed by DML that uses both the renamed and new columns.",
      "status": "pass",
      "duration_ms": 4615,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:32.516758+00:00",
      "read_cold_ms": 3106,
      "read_warm_ms": 815,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 110,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/53_writer_column_invariants_not_null",
      "num": 53,
      "name": "writer_column_invariants_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/53_writer_column_invariants_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_53_writer_column_invariants_not_null.py",
      "description": "Demonstrates CHECK constraints that enforce data integrity rules. Writers MUST validate all CHECK constraints before writing data.",
      "status": "pass",
      "duration_ms": 6854,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:39.372854+00:00",
      "read_cold_ms": 4677,
      "read_warm_ms": 1056,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 186,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/540_insert_overwrite_then_merge",
      "num": 540,
      "name": "insert_overwrite_then_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/540_insert_overwrite_then_merge.sql",
      "read_script": "generator/spark-reads-df/verify_540_insert_overwrite_then_merge.py",
      "description": "INSERT OVERWRITE then MERGE. Tests that MERGE operates correctly on data seeded via INSERT OVERWRITE (which replaces all existing data).",
      "status": "pass",
      "duration_ms": 7363,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:46.736995+00:00",
      "read_cold_ms": 4624,
      "read_warm_ms": 1049,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 132,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/541_merge_delete_reinsert",
      "num": 541,
      "name": "merge_delete_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/541_merge_delete_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_541_merge_delete_reinsert.py",
      "description": "MERGE that deletes rows then re-inserts them. Tests row lifecycle where rows are removed via MERGE WHEN MATCHED DELETE then re-added via INSERT.",
      "status": "pass",
      "duration_ms": 8468,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:14:55.205898+00:00",
      "read_cold_ms": 3188,
      "read_warm_ms": 3842,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 103,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/542_cdc_partition_optimize_merge",
      "num": 542,
      "name": "cdc_partition_optimize_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/542_cdc_partition_optimize_merge.sql",
      "read_script": "generator/spark-reads-df/verify_542_cdc_partition_optimize_merge.py",
      "description": "CDC + partition + OPTIMIZE + MERGE. Four-way combination testing that Change Data Feed, partitioning, file compaction, and MERGE all cooperate.",
      "status": "pass",
      "duration_ms": 10644,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:05.851403+00:00",
      "read_cold_ms": 3485,
      "read_warm_ms": 771,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 466,
      "write_warm_ms": 585,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/543_colmap_optimize_evolve",
      "num": 543,
      "name": "colmap_optimize_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/543_colmap_optimize_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_543_colmap_optimize_evolve.py",
      "description": "Column mapping + OPTIMIZE + schema evolution. Three-way combination testing that column mapping mode=name, file compaction, and ADD COLUMN cooperate.",
      "status": "pass",
      "duration_ms": 9408,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:15.260389+00:00",
      "read_cold_ms": 3292,
      "read_warm_ms": 4605,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 389,
      "write_warm_ms": 284,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/544_constraint_cdc_partition",
      "num": 544,
      "name": "constraint_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/544_constraint_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_544_constraint_cdc_partition.py",
      "description": "Constraint + CDC + partition. Three-way combination testing that CHECK constraints, Change Data Feed, and partitioning cooperate correctly.",
      "status": "pass",
      "duration_ms": 6358,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:21.622043+00:00",
      "read_cold_ms": 3471,
      "read_warm_ms": 1201,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 119,
      "write_warm_ms": 148,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/545_dv_constraint_evolve_merge",
      "num": 545,
      "name": "dv_constraint_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/545_dv_constraint_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_545_dv_constraint_evolve_merge.py",
      "description": "DV + constraint + schema evolution + MERGE. Four-way combination testing that deletion vectors, CHECK constraints, ADD COLUMN, and MERGE cooperate.",
      "status": "pass",
      "duration_ms": 7043,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:28.667180+00:00",
      "read_cold_ms": 4811,
      "read_warm_ms": 625,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 122,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/546_partition_colmap_constraint",
      "num": 546,
      "name": "partition_colmap_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/546_partition_colmap_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_546_partition_colmap_constraint.py",
      "description": "Partition + column mapping + constraint. Three-way combination testing that partitioning, column mapping mode=name, and CHECK constraints cooperate.",
      "status": "pass",
      "duration_ms": 6989,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:35.662771+00:00",
      "read_cold_ms": 2791,
      "read_warm_ms": 3172,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 91,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/547_merge_partition_cdc_dml",
      "num": 547,
      "name": "merge_partition_cdc_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/547_merge_partition_cdc_dml.sql",
      "read_script": "generator/spark-reads-df/verify_547_merge_partition_cdc_dml.py",
      "description": "MERGE + partition + CDC + additional DML. Complex lifecycle testing that MERGE, partitioning, Change Data Feed, UPDATE, and DELETE all cooperate.",
      "status": "pass",
      "duration_ms": 8229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:43.893989+00:00",
      "read_cold_ms": 2468,
      "read_warm_ms": 3938,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 648,
      "write_warm_ms": 599,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/548_optimize_colmap_cdc",
      "num": 548,
      "name": "optimize_colmap_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/548_optimize_colmap_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_548_optimize_colmap_cdc.py",
      "description": "OPTIMIZE + column mapping + CDC. Three-way combination testing that file compaction, column mapping mode=name, and Change Data Feed cooperate.",
      "status": "pass",
      "duration_ms": 7321,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:51.216475+00:00",
      "read_cold_ms": 2381,
      "read_warm_ms": 768,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 339,
      "write_warm_ms": 332,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/549_evolve_optimize_delete",
      "num": 549,
      "name": "evolve_optimize_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/549_evolve_optimize_delete.sql",
      "read_script": "generator/spark-reads-df/verify_549_evolve_optimize_delete.py",
      "description": "Schema evolution + OPTIMIZE + DELETE. Three-way combination testing that ADD COLUMN, file compaction, and DELETE cooperate correctly.",
      "status": "pass",
      "duration_ms": 5172,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:15:56.389074+00:00",
      "read_cold_ms": 2987,
      "read_warm_ms": 1028,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 313,
      "write_warm_ms": 246,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/54_writer_check_constraints_validation",
      "num": 54,
      "name": "writer_check_constraints_validation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/54_writer_check_constraints_validation.sql",
      "read_script": "generator/spark-reads-df/verify_54_writer_check_constraints_validation.py",
      "description": "Demonstrates CHECK constraints that writers must validate before committing data.",
      "status": "pass",
      "duration_ms": 7386,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:03.775754+00:00",
      "read_cold_ms": 2254,
      "read_warm_ms": 3780,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 271,
      "write_warm_ms": 182,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/550_cdc_constraint_merge_partition",
      "num": 550,
      "name": "cdc_constraint_merge_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/550_cdc_constraint_merge_partition.sql",
      "read_script": "generator/spark-reads-df/verify_550_cdc_constraint_merge_partition.py",
      "description": "CDC + constraint + MERGE + partition. Four-way combination testing that Change Data Feed, CHECK constraints, MERGE, and partitioning cooperate.",
      "status": "pass",
      "duration_ms": 8471,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:12.249633+00:00",
      "read_cold_ms": 3019,
      "read_warm_ms": 3004,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
      "write_warm_ms": 242,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/551_delete_update_merge_chain",
      "num": 551,
      "name": "delete_update_merge_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/551_delete_update_merge_chain.sql",
      "read_script": "generator/spark-reads-df/verify_551_delete_update_merge_chain.py",
      "description": "DELETE then UPDATE then MERGE. Three sequential DML types in one script. Validates that the engine correctly chains heterogeneous DML operations.",
      "status": "pass",
      "duration_ms": 7429,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:19.682176+00:00",
      "read_cold_ms": 5377,
      "read_warm_ms": 960,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 151,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/552_merge_with_aggregated_source",
      "num": 552,
      "name": "merge_with_aggregated_source",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/552_merge_with_aggregated_source.sql",
      "read_script": "generator/spark-reads-df/verify_552_merge_with_aggregated_source.py",
      "description": "MERGE where the source is a CTE with computed totals. Tests that MERGE handles complex source expressions and additive UPDATE logic.",
      "status": "pass",
      "duration_ms": 5031,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:24.714194+00:00",
      "read_cold_ms": 3096,
      "read_warm_ms": 1018,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 58,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/553_partition_five_insert_optimize",
      "num": 553,
      "name": "partition_five_insert_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/553_partition_five_insert_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_553_partition_five_insert_optimize.py",
      "description": "Five INSERT batches into a partitioned table then OPTIMIZE. Tests per-partition compaction after multiple small writes.",
      "status": "pass",
      "duration_ms": 7023,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:31.738179+00:00",
      "read_cold_ms": 4448,
      "read_warm_ms": 1018,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 416,
      "write_warm_ms": 501,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/554_colmap_rename_drop",
      "num": 554,
      "name": "colmap_rename_drop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/554_colmap_rename_drop.sql",
      "read_script": "generator/spark-reads-df/verify_554_colmap_rename_drop.py",
      "description": "Column mapping with RENAME COLUMN and DROP COLUMN in the same script. Tests stacked column mutations under columnMapping.mode=name.",
      "status": "pass",
      "duration_ms": 4459,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:36.199315+00:00",
      "read_cold_ms": 2932,
      "read_warm_ms": 790,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 143,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/555_cdc_dv_all_dml",
      "num": 555,
      "name": "cdc_dv_all_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/555_cdc_dv_all_dml.sql",
      "read_script": "generator/spark-reads-df/verify_555_cdc_dv_all_dml.py",
      "description": "CDC + DV with every DML type: INSERT, UPDATE, DELETE, MERGE. Validates that change data feed captures all operation types correctly while deletion vectors are active.",
      "status": "pass",
      "duration_ms": 7902,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:44.103607+00:00",
      "read_cold_ms": 2313,
      "read_warm_ms": 3570,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 325,
      "write_warm_ms": 434,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/556_constraint_not_null_dml",
      "num": 556,
      "name": "constraint_not_null_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/556_constraint_not_null_dml.sql",
      "read_script": "generator/spark-reads-df/verify_556_constraint_not_null_dml.py",
      "description": "NOT NULL columns + CHECK constraints + DML. Validates that constraints are enforced correctly across UPDATE and DELETE operations, and that nullable columns accept NULL values.",
      "status": "pass",
      "duration_ms": 6236,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:50.340290+00:00",
      "read_cold_ms": 3824,
      "read_warm_ms": 1059,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 319,
      "write_warm_ms": 133,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/557_partition_evolve_constraint_cdc",
      "num": 557,
      "name": "partition_evolve_constraint_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/557_partition_evolve_constraint_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_557_partition_evolve_constraint_cdc.py",
      "description": "Partition + schema evolution + constraint + CDC. Four-way feature combination test validating that ADD COLUMN, CHECK constraint, and CDC all work correctly on a partitioned table.",
      "status": "pass",
      "duration_ms": 5758,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:16:56.100143+00:00",
      "read_cold_ms": 3192,
      "read_warm_ms": 1057,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 174,
      "write_warm_ms": 184,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/558_optimize_partition_merge_cdc",
      "num": 558,
      "name": "optimize_partition_merge_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/558_optimize_partition_merge_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_558_optimize_partition_merge_cdc.py",
      "description": "OPTIMIZE + partition + MERGE + CDC. Four-way combination test validating that MERGE operates correctly on an optimized partitioned table with change data feed enabled.",
      "status": "pass",
      "duration_ms": 8414,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:04.515320+00:00",
      "read_cold_ms": 2679,
      "read_warm_ms": 3597,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 599,
      "write_warm_ms": 574,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/559_colmap_cdc_dv_evolve_partition",
      "num": 559,
      "name": "colmap_cdc_dv_evolve_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/559_colmap_cdc_dv_evolve_partition.sql",
      "read_script": "generator/spark-reads-df/verify_559_colmap_cdc_dv_evolve_partition.py",
      "description": "Five-way: column mapping + CDC + DV + schema evolution + partition. Validates that all five features coexist without conflicts when performing DELETE, ADD COLUMN, INSERT, and UPDATE operations.",
      "status": "pass",
      "duration_ms": 4637,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:09.153629+00:00",
      "read_cold_ms": 2475,
      "read_warm_ms": 1184,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 254,
      "write_warm_ms": 213,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/55_writer_generated_columns_computed",
      "num": 55,
      "name": "writer_generated_columns_computed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/55_writer_generated_columns_computed.sql",
      "read_script": "generator/spark-reads-df/verify_55_writer_generated_columns_computed.py",
      "description": "Demonstrates generated columns that are automatically computed from other columns.",
      "status": "pass",
      "duration_ms": 4303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:13.457895+00:00",
      "read_cold_ms": 2234,
      "read_warm_ms": 872,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 198,
      "write_warm_ms": 71,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/560_all_features_stress",
      "num": 560,
      "name": "all_features_stress",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/560_all_features_stress.sql",
      "read_script": "generator/spark-reads-df/verify_560_all_features_stress.py",
      "description": "Grand stress test: DV + CDC + colmap + partition + constraint + schema evolution + OPTIMIZE + MERGE + DELETE + UPDATE. Exercises nearly every Delta feature in a single script.",
      "status": "pass",
      "duration_ms": 11547,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:25.007229+00:00",
      "read_cold_ms": 5390,
      "read_warm_ms": 912,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 677,
      "write_warm_ms": 781,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/561_update_multiple_where",
      "num": 561,
      "name": "update_multiple_where",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/561_update_multiple_where.sql",
      "read_script": "generator/spark-reads-df/verify_561_update_multiple_where.py",
      "description": "UPDATE with complex compound WHERE (AND + OR). Tests that the engine correctly evaluates compound predicates with mixed boolean logic.",
      "status": "pass",
      "duration_ms": 4209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:29.217526+00:00",
      "read_cold_ms": 2243,
      "read_warm_ms": 1040,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 67,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/562_delete_in_list",
      "num": 562,
      "name": "delete_in_list",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/562_delete_in_list.sql",
      "read_script": "generator/spark-reads-df/verify_562_delete_in_list.py",
      "description": "DELETE WHERE id IN (...). Tests IN-list predicate evaluation with deletion vectors.",
      "status": "pass",
      "duration_ms": 7318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:36.538093+00:00",
      "read_cold_ms": 1776,
      "read_warm_ms": 4076,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 157,
      "write_warm_ms": 73,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/563_merge_with_case_update",
      "num": 563,
      "name": "merge_with_case_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/563_merge_with_case_update.sql",
      "read_script": "generator/spark-reads-df/verify_563_merge_with_case_update.py",
      "description": "MERGE with CASE expression in UPDATE SET. Tests that the engine handles complex expressions within MERGE UPDATE clauses.",
      "status": "pass",
      "duration_ms": 1141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:09.085604+00:00",
      "read_cold_ms": 674,
      "read_warm_ms": 223,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/564_partition_evolve_delete_all",
      "num": 564,
      "name": "partition_evolve_delete_all",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/564_partition_evolve_delete_all.sql",
      "read_script": "generator/spark-reads-df/verify_564_partition_evolve_delete_all.py",
      "description": "Partition + schema evolution + DELETE all from one partition. Tests that deleting an entire partition works correctly after schema evolution, and that re-inserted rows use the evolved schema.",
      "status": "pass",
      "duration_ms": 7389,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:49.443958+00:00",
      "read_cold_ms": 2612,
      "read_warm_ms": 3750,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 156,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/565_cdc_merge_delete_chain",
      "num": 565,
      "name": "cdc_merge_delete_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/565_cdc_merge_delete_chain.sql",
      "read_script": "generator/spark-reads-df/verify_565_cdc_merge_delete_chain.py",
      "description": "CDC + MERGE then DELETE. Tests that CDF captures both MERGE and DELETE operations in sequence.",
      "status": "pass",
      "duration_ms": 9322,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:17:58.767661+00:00",
      "read_cold_ms": 2872,
      "read_warm_ms": 4054,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 339,
      "write_warm_ms": 410,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/566_constraint_evolve_cdc_dml",
      "num": 566,
      "name": "constraint_evolve_cdc_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/566_constraint_evolve_cdc_dml.sql",
      "read_script": "generator/spark-reads-df/verify_566_constraint_evolve_cdc_dml.py",
      "description": "Constraint + schema evolution + CDC + DML. Four-way feature combo. Tests that CHECK constraints, schema evolution, and CDF all interact correctly under DML operations.",
      "status": "pass",
      "duration_ms": 7709,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:06.478464+00:00",
      "read_cold_ms": 2268,
      "read_warm_ms": 3402,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/567_colmap_dv_delete_update",
      "num": 567,
      "name": "colmap_dv_delete_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/567_colmap_dv_delete_update.sql",
      "read_script": "generator/spark-reads-df/verify_567_colmap_dv_delete_update.py",
      "description": "Column mapping + DV + DELETE + UPDATE. Tests that deletion vectors and update operations work correctly with column mapping mode=name.",
      "status": "pass",
      "duration_ms": 4766,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:11.246581+00:00",
      "read_cold_ms": 2546,
      "read_warm_ms": 1300,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 162,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/568_optimize_evolve_merge",
      "num": 568,
      "name": "optimize_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/568_optimize_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_568_optimize_evolve_merge.py",
      "description": "OPTIMIZE + schema evolution + MERGE. Three-way feature combo. Tests that MERGE works correctly on a table that has been optimized and then schema-evolved.",
      "status": "pass",
      "duration_ms": 6657,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:17.905273+00:00",
      "read_cold_ms": 4173,
      "read_warm_ms": 1003,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 221,
      "write_warm_ms": 389,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/569_partition_cdc_colmap_merge",
      "num": 569,
      "name": "partition_cdc_colmap_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/569_partition_cdc_colmap_merge.sql",
      "read_script": "generator/spark-reads-df/verify_569_partition_cdc_colmap_merge.py",
      "description": "Partition + CDC + column mapping + MERGE. Four-way feature combo. Tests that MERGE works on a partitioned table with CDF and column mapping all enabled simultaneously.",
      "status": "pass",
      "duration_ms": 8680,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:26.587144+00:00",
      "read_cold_ms": 3129,
      "read_warm_ms": 3487,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 425,
      "write_warm_ms": 458,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/56_writer_default_columns_values",
      "num": 56,
      "name": "writer_default_columns_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/56_writer_default_columns_values.sql",
      "read_script": "generator/spark-reads-df/verify_56_writer_default_columns_values.py",
      "description": "Demonstrates handling of default column values in Delta table writes. Tests writer behavior when columns have default values assigned.",
      "status": "pass",
      "duration_ms": 7077,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:33.665396+00:00",
      "read_cold_ms": 4827,
      "read_warm_ms": 856,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 165,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/570_constraint_dv_optimize",
      "num": 570,
      "name": "constraint_dv_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/570_constraint_dv_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_570_constraint_dv_optimize.py",
      "description": "Constraint + DV + OPTIMIZE. Tests that constraint metadata survives deletion vector operations and file compaction.",
      "status": "pass",
      "duration_ms": 7306,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:40.973152+00:00",
      "read_cold_ms": 2361,
      "read_warm_ms": 3608,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 169,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/571_merge_partition_delete_insert",
      "num": 571,
      "name": "merge_partition_delete_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/571_merge_partition_delete_insert.sql",
      "read_script": "generator/spark-reads-df/verify_571_merge_partition_delete_insert.py",
      "description": "MERGE with DELETE clause on partitioned table + subsequent INSERT. Tests that MERGE DELETE conditions work on partitioned tables and that subsequent inserts are correctly placed.",
      "status": "pass",
      "duration_ms": 4939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:45.913471+00:00",
      "read_cold_ms": 3315,
      "read_warm_ms": 965,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 268,
      "write_warm_ms": 201,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/572_cdc_constraint_evolve_merge",
      "num": 572,
      "name": "cdc_constraint_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/572_cdc_constraint_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_572_cdc_constraint_evolve_merge.py",
      "description": "CDC + constraint + schema evolution + MERGE. Four-way feature combo. Tests that MERGE respects constraints after schema evolution on a CDF-enabled table.",
      "status": "pass",
      "duration_ms": 6757,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:52.672753+00:00",
      "read_cold_ms": 2108,
      "read_warm_ms": 3478,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 379,
      "write_warm_ms": 317,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/573_dv_partition_evolve_delete",
      "num": 573,
      "name": "dv_partition_evolve_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/573_dv_partition_evolve_delete.sql",
      "read_script": "generator/spark-reads-df/verify_573_dv_partition_evolve_delete.py",
      "description": "DV + partition + schema evolution + DELETE. Tests that schema evolution on a partitioned DV table works correctly with DELETE and UPDATE on the new column.",
      "status": "pass",
      "duration_ms": 6737,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:18:59.411969+00:00",
      "read_cold_ms": 4736,
      "read_warm_ms": 939,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 266,
      "write_warm_ms": 170,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/574_colmap_optimize_merge_dml",
      "num": 574,
      "name": "colmap_optimize_merge_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/574_colmap_optimize_merge_dml.sql",
      "read_script": "generator/spark-reads-df/verify_574_colmap_optimize_merge_dml.py",
      "description": "Column mapping + OPTIMIZE + MERGE + DML. Four-way feature combo. Tests that MERGE and DELETE work correctly on an optimized column-mapped table.",
      "status": "pass",
      "duration_ms": 7807,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:07.224368+00:00",
      "read_cold_ms": 2589,
      "read_warm_ms": 3481,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 269,
      "write_warm_ms": 389,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/575_cdc_dv_colmap_evolve",
      "num": 575,
      "name": "cdc_dv_colmap_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/575_cdc_dv_colmap_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_575_cdc_dv_colmap_evolve.py",
      "description": "CDC + DV + column mapping + schema evolution. Four-way feature combo. Tests that schema evolution works on a table with CDC, DVs, and column mapping all enabled.",
      "status": "pass",
      "duration_ms": 4250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:11.475931+00:00",
      "read_cold_ms": 2412,
      "read_warm_ms": 825,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 159,
      "write_warm_ms": 153,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/576_partition_constraint_optimize",
      "num": 576,
      "name": "partition_constraint_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/576_partition_constraint_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_576_partition_constraint_optimize.py",
      "description": "Partition + constraint + OPTIMIZE. Three-way feature combo. Tests that constraints survive OPTIMIZE on partitioned tables and that subsequent inserts still respect constraints.",
      "status": "pass",
      "duration_ms": 6913,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:18.390463+00:00",
      "read_cold_ms": 1945,
      "read_warm_ms": 3765,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 188,
      "write_warm_ms": 294,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/577_merge_evolve_cdc_dml",
      "num": 577,
      "name": "merge_evolve_cdc_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/577_merge_evolve_cdc_dml.sql",
      "read_script": "generator/spark-reads-df/verify_577_merge_evolve_cdc_dml.py",
      "description": "MERGE + schema evolution + CDC + DML. Four-way feature combo. Tests that MERGE correctly populates a new column added via schema evolution, and that subsequent DML operates correctly with CDF.",
      "status": "pass",
      "duration_ms": 4724,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:23.115962+00:00",
      "read_cold_ms": 2276,
      "read_warm_ms": 919,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 303,
      "write_warm_ms": 334,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/578_colmap_constraint_partition_dml",
      "num": 578,
      "name": "colmap_constraint_partition_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/578_colmap_constraint_partition_dml.sql",
      "read_script": "generator/spark-reads-df/verify_578_colmap_constraint_partition_dml.py",
      "description": "Column mapping + constraint + partition + DML. Four-way feature combo. Tests that constraints are enforced on a partitioned column-mapped table during UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 7337,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:30.454522+00:00",
      "read_cold_ms": 5036,
      "read_warm_ms": 874,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 124,
      "write_warm_ms": 191,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/579_optimize_evolve_cdc_partition",
      "num": 579,
      "name": "optimize_evolve_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/579_optimize_evolve_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_579_optimize_evolve_cdc_partition.py",
      "description": "OPTIMIZE + schema evolution + CDC + partition. Four-way feature combo. Tests that OPTIMIZE on a partitioned CDC table does not break schema evolution, and that subsequent inserts and updates work correctly.",
      "status": "pass",
      "duration_ms": 7452,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:37.907761+00:00",
      "read_cold_ms": 2289,
      "read_warm_ms": 3248,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 283,
      "write_warm_ms": 453,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/57_writer_identity_columns_auto",
      "num": 57,
      "name": "writer_identity_columns_auto",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/57_writer_identity_columns_auto.sql",
      "read_script": "generator/spark-reads-df/verify_57_writer_identity_columns_auto.py",
      "description": "Demonstrates handling of identity columns with auto-increment behavior.",
      "status": "pass",
      "duration_ms": 5620,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:43.529059+00:00",
      "read_cold_ms": 1904,
      "read_warm_ms": 2852,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 191,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/580_five_way_merge_stress",
      "num": 580,
      "name": "five_way_merge_stress",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/580_five_way_merge_stress.sql",
      "read_script": "generator/spark-reads-df/verify_580_five_way_merge_stress.py",
      "description": "DV + CDC + partition + constraint + MERGE. Five-way combo with MERGE. The most complex feature interaction test: all major Delta features active simultaneously.",
      "status": "pass",
      "duration_ms": 8211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:51.741138+00:00",
      "read_cold_ms": 2892,
      "read_warm_ms": 3475,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 323,
      "write_warm_ms": 326,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/581_merge_three_clause_partition",
      "num": 581,
      "name": "merge_three_clause_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/581_merge_three_clause_partition.sql",
      "read_script": "generator/spark-reads-df/verify_581_merge_three_clause_partition.py",
      "description": "MERGE with all 3 clauses (DELETE + UPDATE + INSERT) on a partitioned table. Validates that a single MERGE statement can delete low-scoring rows, update surviving matches, and insert new rows -- all in one pass on a partitioned table.",
      "status": "pass",
      "duration_ms": 6870,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:19:58.613529+00:00",
      "read_cold_ms": 2759,
      "read_warm_ms": 3369,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 167,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/582_cdc_dv_constraint_delete",
      "num": 582,
      "name": "cdc_dv_constraint_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/582_cdc_dv_constraint_delete.sql",
      "read_script": "generator/spark-reads-df/verify_582_cdc_dv_constraint_delete.py",
      "description": "CDC + DV + constraint + DELETE. Tests that constraint metadata is properly tracked in the CDF context, and that DELETE operations generate correct change data feed records when deletion vectors are enabled.",
      "status": "pass",
      "duration_ms": 5395,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:04.011260+00:00",
      "read_cold_ms": 2593,
      "read_warm_ms": 1186,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 42,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/583_colmap_evolve_rename_merge",
      "num": 583,
      "name": "colmap_evolve_rename_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/583_colmap_evolve_rename_merge.sql",
      "read_script": "generator/spark-reads-df/verify_583_colmap_evolve_rename_merge.py",
      "description": "Column mapping + schema evolution (ADD + RENAME) + MERGE. Tests that column rename and column addition work correctly with column mapping mode, and that a subsequent MERGE properly resolves the renamed/added columns.",
      "status": "pass",
      "duration_ms": 6892,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:10.904667+00:00",
      "read_cold_ms": 4845,
      "read_warm_ms": 995,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 179,
      "write_warm_ms": 97,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/584_partition_delete_update_insert",
      "num": 584,
      "name": "partition_delete_update_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/584_partition_delete_update_insert.sql",
      "read_script": "generator/spark-reads-df/verify_584_partition_delete_update_insert.py",
      "description": "Partition + DELETE + UPDATE + INSERT all targeting different partitions. Validates that DML operations correctly isolate to their target partitions and that cross-partition data remains untouched.",
      "status": "pass",
      "duration_ms": 6929,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:17.841221+00:00",
      "read_cold_ms": 2161,
      "read_warm_ms": 3682,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 211,
      "write_warm_ms": 114,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/585_optimize_constraint_cdc_partition",
      "num": 585,
      "name": "optimize_constraint_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/585_optimize_constraint_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_585_optimize_constraint_cdc_partition.py",
      "description": "OPTIMIZE + constraint + CDC + partition. Four-way feature combination. Validates that OPTIMIZE compacts files correctly on a partitioned table with CDC enabled and a CHECK constraint, and that subsequent DML still produces correct CDF records.",
      "status": "pass",
      "duration_ms": 7953,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:25.801476+00:00",
      "read_cold_ms": 2519,
      "read_warm_ms": 1102,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 368,
      "write_warm_ms": 493,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/586_merge_cdc_partition_evolve",
      "num": 586,
      "name": "merge_cdc_partition_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/586_merge_cdc_partition_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_586_merge_cdc_partition_evolve.py",
      "description": "MERGE + CDC + partition + schema evolution. Four-way feature combination. Validates that MERGE works correctly after adding a new column to a partitioned table with CDC enabled, and that CDF records capture the evolved schema.",
      "status": "pass",
      "duration_ms": 3819,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:29.622041+00:00",
      "read_cold_ms": 1871,
      "read_warm_ms": 581,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 501,
      "write_warm_ms": 566,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/587_colmap_dv_cdc_constraint",
      "num": 587,
      "name": "colmap_dv_cdc_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/587_colmap_dv_cdc_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_587_colmap_dv_cdc_constraint.py",
      "description": "Column mapping + DV + CDC + constraint. Four-way feature combination. Validates that all four features coexist: column mapping tracks physical IDs, deletion vectors handle deletes, CDC captures change records, and the CHECK constraint is enforced throughout.",
      "status": "pass",
      "duration_ms": 5219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:34.841788+00:00",
      "read_cold_ms": 3548,
      "read_warm_ms": 631,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 114,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/588_large_multi_batch_insert",
      "num": 588,
      "name": "large_multi_batch_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/588_large_multi_batch_insert.sql",
      "read_script": "generator/spark-reads-df/verify_588_large_multi_batch_insert.py",
      "description": "Large table: 20 INSERT batches of 100 rows each (2000 total) + DML. Validates that the engine handles many small commits correctly, producing a large number of log entries, and that subsequent DELETE and UPDATE operations work on a table with many data files.",
      "status": "pass",
      "duration_ms": 7761,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:42.604050+00:00",
      "read_cold_ms": 2853,
      "read_warm_ms": 3496,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1016,
      "write_warm_ms": 787,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:many-batches",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/589_merge_delete_clause_cdc",
      "num": 589,
      "name": "merge_delete_clause_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/589_merge_delete_clause_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_589_merge_delete_clause_cdc.py",
      "description": "MERGE with DELETE clause + CDC. Tests that CDF records capture MERGE-DELETE events correctly -- when a MERGE statement deletes rows via its WHEN MATCHED AND ... THEN DELETE clause, the change data feed must record those as deletes.",
      "status": "pass",
      "duration_ms": 5865,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:48.470568+00:00",
      "read_cold_ms": 1649,
      "read_warm_ms": 566,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 286,
      "write_warm_ms": 287,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/58_reader_version_requirements_matrix",
      "num": 58,
      "name": "reader_version_requirements_matrix",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/58_reader_version_requirements_matrix.sql",
      "read_script": "generator/spark-reads-df/verify_58_reader_version_requirements_matrix.py",
      "description": "Demonstrates reader version requirements to read tables with various features.",
      "status": "pass",
      "duration_ms": 4608,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:53.079485+00:00",
      "read_cold_ms": 2724,
      "read_warm_ms": 1053,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 241,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/590_evolve_constraint_optimize_merge",
      "num": 590,
      "name": "evolve_constraint_optimize_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/590_evolve_constraint_optimize_merge.sql",
      "read_script": "generator/spark-reads-df/verify_590_evolve_constraint_optimize_merge.py",
      "description": "Schema evolution + constraint + OPTIMIZE + MERGE. Four-way feature combo. Validates that adding a column, adding a constraint, compacting files, and then running a MERGE all work together correctly.",
      "status": "pass",
      "duration_ms": 5123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:20:58.203167+00:00",
      "read_cold_ms": 1615,
      "read_warm_ms": 619,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 272,
      "write_warm_ms": 266,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/591_partition_three_region_merge",
      "num": 591,
      "name": "partition_three_region_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/591_partition_three_region_merge.sql",
      "read_script": "generator/spark-reads-df/verify_591_partition_three_region_merge.py",
      "description": "3-region partition + MERGE that adds rows to each region. Validates that MERGE correctly handles both matched updates and unmatched inserts across all partitions evenly.",
      "status": "pass",
      "duration_ms": 3804,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:02.008470+00:00",
      "read_cold_ms": 2277,
      "read_warm_ms": 627,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 216,
      "write_warm_ms": 117,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/592_cdc_dv_optimize_delete_update",
      "num": 592,
      "name": "cdc_dv_optimize_delete_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/592_cdc_dv_optimize_delete_update.sql",
      "read_script": "generator/spark-reads-df/verify_592_cdc_dv_optimize_delete_update.py",
      "description": "CDC + DV + OPTIMIZE + DELETE + UPDATE. Full DML lifecycle with compaction. Validates that OPTIMIZE compacts DV-enabled files correctly, and that subsequent DELETE and UPDATE generate proper CDF records after compaction.",
      "status": "pass",
      "duration_ms": 11013,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:13.023157+00:00",
      "read_cold_ms": 5028,
      "read_warm_ms": 788,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 266,
      "write_warm_ms": 321,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/593_colmap_partition_delete_merge",
      "num": 593,
      "name": "colmap_partition_delete_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/593_colmap_partition_delete_merge.sql",
      "read_script": "generator/spark-reads-df/verify_593_colmap_partition_delete_merge.py",
      "description": "Column mapping + partition + DELETE + MERGE. Validates that column mapping works correctly with partitioned data, and that DELETE followed by MERGE properly handles physical column IDs across partition boundaries.",
      "status": "pass",
      "duration_ms": 7701,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:20.725543+00:00",
      "read_cold_ms": 2205,
      "read_warm_ms": 4164,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 142,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/594_constraint_two_evolve",
      "num": 594,
      "name": "constraint_two_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/594_constraint_two_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_594_constraint_two_evolve.py",
      "description": "Two constraints + two schema evolutions. Tests that multiple ADD CONSTRAINT and ADD COLUMN operations interleave correctly, and that constraints on newly-added columns are enforced properly (including NULL handling).",
      "status": "pass",
      "duration_ms": 6995,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:27.721124+00:00",
      "read_cold_ms": 5103,
      "read_warm_ms": 1037,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/595_optimize_merge_delete_chain",
      "num": 595,
      "name": "optimize_merge_delete_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/595_optimize_merge_delete_chain.sql",
      "read_script": "generator/spark-reads-df/verify_595_optimize_merge_delete_chain.py",
      "description": "OPTIMIZE then MERGE then DELETE. Three-step DML chain after compaction. Validates that MERGE works correctly on optimized (compacted) files, and that a subsequent DELETE properly handles rows that were just inserted by the MERGE.",
      "status": "pass",
      "duration_ms": 6463,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:34.184920+00:00",
      "read_cold_ms": 3844,
      "read_warm_ms": 942,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
      "write_warm_ms": 264,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/596_cdc_colmap_partition_merge_evolve",
      "num": 596,
      "name": "cdc_colmap_partition_merge_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/596_cdc_colmap_partition_merge_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_596_cdc_colmap_partition_merge_evolve.py",
      "description": "Five-way: CDC + column mapping + partition + MERGE + schema evolution. The most complex feature interaction test combining all major metadata features (CDC, colmap) with partitioning, schema evolution, and MERGE DML.",
      "status": "pass",
      "duration_ms": 8895,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:43.081112+00:00",
      "read_cold_ms": 3098,
      "read_warm_ms": 4086,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 398,
      "write_warm_ms": 565,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/597_dv_partition_constraint_cdc",
      "num": 597,
      "name": "dv_partition_constraint_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/597_dv_partition_constraint_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_597_dv_partition_constraint_cdc.py",
      "description": "DV + partition + constraint + CDC. Four-way feature combination. Validates that deletion vectors, partitioning, CHECK constraints, and change data feed all coexist correctly through DELETE and UPDATE operations.",
      "status": "pass",
      "duration_ms": 4942,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:48.025003+00:00",
      "read_cold_ms": 2607,
      "read_warm_ms": 1072,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 175,
      "write_warm_ms": 159,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/598_colmap_evolve_optimize_dml",
      "num": 598,
      "name": "colmap_evolve_optimize_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/598_colmap_evolve_optimize_dml.sql",
      "read_script": "generator/spark-reads-df/verify_598_colmap_evolve_optimize_dml.py",
      "description": "Column mapping + schema evolution + OPTIMIZE + DML. Four-way feature combo. Validates that OPTIMIZE correctly handles files with evolved schema under column mapping mode, and that subsequent DML (UPDATE + DELETE) works on the compacted, evolved table.",
      "status": "pass",
      "duration_ms": 7716,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:21:55.742258+00:00",
      "read_cold_ms": 2608,
      "read_warm_ms": 3856,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 282,
      "write_warm_ms": 310,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/599_merge_cdc_dv_colmap_constraint",
      "num": 599,
      "name": "merge_cdc_dv_colmap_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/599_merge_cdc_dv_colmap_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_599_merge_cdc_dv_colmap_constraint.py",
      "description": "Five-way: MERGE + CDC + DV + column mapping + constraint. Tests that all five major Delta features coexist: column mapping tracks physical IDs, DVs handle deletes, CDC captures changes, the constraint is enforced through MERGE inserts, and MERGE correctly resolves all of this.",
      "status": "pass",
      "duration_ms": 7509,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:03.252969+00:00",
      "read_cold_ms": 1730,
      "read_warm_ms": 3953,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 374,
      "write_warm_ms": 487,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/59_schema_all_primitive_types",
      "num": 59,
      "name": "schema_all_primitive_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/59_schema_all_primitive_types.sql",
      "read_script": "generator/spark-reads-df/verify_59_schema_all_primitive_types.py",
      "description": "decimal, string, binary, boolean, date, timestamp. - Temperature (float/double), humidity (byte), pressure (short) - Device IDs (long), timestamps (timestamp), locations (string) - Binary sensor payloads, boolean status flags, date-based partitions",
      "status": "pass",
      "duration_ms": 6182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:09.436767+00:00",
      "read_cold_ms": 1517,
      "read_warm_ms": 683,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 84,
      "tags": [
        "type:binary",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/600_ultimate_comprehensive",
      "num": 600,
      "name": "ultimate_comprehensive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/600_ultimate_comprehensive.sql",
      "read_script": "generator/spark-reads-df/verify_600_ultimate_comprehensive.py",
      "description": "ULTIMATE test: DV + CDC + colmap + partition + constraint + schema evolution + OPTIMIZE + MERGE + DELETE + UPDATE. All major Delta features and all DML types exercised in a single test. This is the most comprehensive integration test in the suite.",
      "status": "pass",
      "duration_ms": 5931,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:15.368726+00:00",
      "read_cold_ms": 2894,
      "read_warm_ms": 904,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 658,
      "write_warm_ms": 543,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/601_struct_update",
      "num": 601,
      "name": "struct_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/601_struct_update.sql",
      "read_script": "generator/spark-reads-df/verify_601_struct_update.py",
      "description": "STRUCT column + UPDATE on non-struct columns. Verifies that nested struct values are preserved unchanged through UPDATE and DELETE operations that only modify scalar columns.",
      "status": "pass",
      "duration_ms": 11269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:26.640312+00:00",
      "read_cold_ms": 4577,
      "read_warm_ms": 1233,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 53,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/602_struct_merge",
      "num": 602,
      "name": "struct_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/602_struct_merge.sql",
      "read_script": "generator/spark-reads-df/verify_602_struct_merge.py",
      "description": "STRUCT column + MERGE. Tests that struct values survive MERGE UPDATE and that new struct values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 6954,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:33.596764+00:00",
      "read_cold_ms": 2893,
      "read_warm_ms": 1202,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 111,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/603_struct_cdc",
      "num": 603,
      "name": "struct_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/603_struct_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_603_struct_cdc.py",
      "description": "STRUCT column + CDC (Change Data Feed). Tests that CDF correctly captures pre/post images for rows containing nested struct data through UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 5852,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:39.450423+00:00",
      "read_cold_ms": 1819,
      "read_warm_ms": 1065,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 224,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/604_timestamp_merge",
      "num": 604,
      "name": "timestamp_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/604_timestamp_merge.sql",
      "read_script": "generator/spark-reads-df/verify_604_timestamp_merge.py",
      "description": "TIMESTAMP column + MERGE. Tests that timestamp values survive MERGE UPDATE and that new timestamp values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 6656,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:46.108440+00:00",
      "read_cold_ms": 5265,
      "read_warm_ms": 542,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 90,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/605_timestamp_cdc",
      "num": 605,
      "name": "timestamp_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/605_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_605_timestamp_cdc.py",
      "description": "TIMESTAMP + CDC. Tests that CDF correctly captures pre/post images for rows containing TIMESTAMP values through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 11224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:22:57.333818+00:00",
      "read_cold_ms": 5516,
      "read_warm_ms": 1088,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 98,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/606_date_merge",
      "num": 606,
      "name": "date_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/606_date_merge.sql",
      "read_script": "generator/spark-reads-df/verify_606_date_merge.py",
      "description": "DATE column + MERGE. Tests that DATE values survive MERGE UPDATE and that new DATE values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 6197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:03.531670+00:00",
      "read_cold_ms": 3019,
      "read_warm_ms": 1455,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 109,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/607_date_partition",
      "num": 607,
      "name": "date_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/607_date_partition.sql",
      "read_script": "generator/spark-reads-df/verify_607_date_partition.py",
      "description": "DATE column + partitioning by a STRING month column. Tests DML operations (UPDATE, DELETE) on a partitioned table that also has a DATE-like partition key (event_month as STRING for partition directory naming).",
      "status": "pass",
      "duration_ms": 9155,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:12.687771+00:00",
      "read_cold_ms": 2594,
      "read_warm_ms": 5303,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 131,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/608_decimal_merge",
      "num": 608,
      "name": "decimal_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/608_decimal_merge.sql",
      "read_script": "generator/spark-reads-df/verify_608_decimal_merge.py",
      "description": "DECIMAL columns + MERGE. Tests that DECIMAL precision is preserved through MERGE UPDATE and that new DECIMAL values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 5580,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:18.268762+00:00",
      "read_cold_ms": 2717,
      "read_warm_ms": 1108,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 92,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/609_decimal_cdc",
      "num": 609,
      "name": "decimal_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/609_decimal_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_609_decimal_cdc.py",
      "description": "DECIMAL + CDC. Tests that CDF correctly captures pre/post images for DECIMAL(18,8) columns, including tiny fractional increments that exercise the full precision of the type.",
      "status": "pass",
      "duration_ms": 8652,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:26.922132+00:00",
      "read_cold_ms": 2679,
      "read_warm_ms": 773,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 100,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/60_schema_struct_type_nested",
      "num": 60,
      "name": "schema_struct_type_nested",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/60_schema_struct_type_nested.sql",
      "read_script": "generator/spark-reads-df/verify_60_schema_struct_type_nested.py",
      "description": "Demonstrates nested struct types with fields. Orders contain nested customer info (name, contact) and shipping address (street, city, country). Struct types model hierarchical data naturally without flattening, maintaining data relationships and enabling efficient nested field...",
      "status": "pass",
      "duration_ms": 14613,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:41.536648+00:00",
      "read_cold_ms": 1887,
      "read_warm_ms": 445,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 96,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/610_decimal_partition",
      "num": 610,
      "name": "decimal_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/610_decimal_partition.sql",
      "read_script": "generator/spark-reads-df/verify_610_decimal_partition.py",
      "description": "DECIMAL columns + partition + DML. Tests DECIMAL precision through UPDATE and DELETE on a partitioned table with a STRING partition key.",
      "status": "pass",
      "duration_ms": 8749,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:50.286892+00:00",
      "read_cold_ms": 6163,
      "read_warm_ms": 1193,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 74,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/611_no_dv_merge",
      "num": 611,
      "name": "no_dv_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/611_no_dv_merge.sql",
      "read_script": "generator/spark-reads-df/verify_611_no_dv_merge.py",
      "description": "No-DV table + MERGE + DELETE. Tests the full-rewrite code path for MERGE and DELETE when deletion vectors are disabled. All DML requires rewriting entire data files rather than using DVs.",
      "status": "pass",
      "duration_ms": 4210,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:54.497425+00:00",
      "read_cold_ms": 1305,
      "read_warm_ms": 303,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 185,
      "write_warm_ms": 97,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/612_no_dv_cdc",
      "num": 612,
      "name": "no_dv_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/612_no_dv_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_612_no_dv_cdc.py",
      "description": "No-DV + CDC. Tests CDF recording with full-rewrite DML path (no deletion vectors). Both UPDATE and DELETE require full file rewrites, and CDF must still correctly capture pre/post images.",
      "status": "pass",
      "duration_ms": 3965,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:23:58.464300+00:00",
      "read_cold_ms": 2429,
      "read_warm_ms": 335,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 44,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/613_no_dv_partition",
      "num": 613,
      "name": "no_dv_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/613_no_dv_partition.sql",
      "read_script": "generator/spark-reads-df/verify_613_no_dv_partition.py",
      "description": "No-DV + partition. Tests full-rewrite DML on a partitioned table with deletion vectors disabled. UPDATE and DELETE must rewrite entire partition files rather than using DVs.",
      "status": "pass",
      "duration_ms": 5235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:03.700249+00:00",
      "read_cold_ms": 1987,
      "read_warm_ms": 222,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 205,
      "write_warm_ms": 92,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/614_not_null_merge",
      "num": 614,
      "name": "not_null_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/614_not_null_merge.sql",
      "read_script": "generator/spark-reads-df/verify_614_not_null_merge.py",
      "description": "NOT NULL columns + MERGE + DELETE. Tests NOT NULL enforcement during MERGE INSERT (all source rows must provide non-null values for constrained columns) and NOT NULL metadata preservation through DML.",
      "status": "pass",
      "duration_ms": 7462,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:11.163037+00:00",
      "read_cold_ms": 2735,
      "read_warm_ms": 3710,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/615_not_null_evolve",
      "num": 615,
      "name": "not_null_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/615_not_null_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_615_not_null_evolve.py",
      "description": "NOT NULL + schema evolution. Tests that NOT NULL constraints survive ALTER TABLE ADD COLUMN, and that the new nullable column works correctly alongside existing NOT NULL columns through INSERT and UPDATE.",
      "status": "pass",
      "duration_ms": 4754,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:15.918326+00:00",
      "read_cold_ms": 2642,
      "read_warm_ms": 1291,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 143,
      "write_warm_ms": 98,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/616_not_null_cdc",
      "num": 616,
      "name": "not_null_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/616_not_null_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_616_not_null_cdc.py",
      "description": "NOT NULL + CDC. Tests that CDF correctly captures pre/post images for tables with NOT NULL constraints through UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 8669,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:24.589190+00:00",
      "read_cold_ms": 5820,
      "read_warm_ms": 973,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 116,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/617_insert_overwrite_merge",
      "num": 617,
      "name": "insert_overwrite_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/617_insert_overwrite_merge.sql",
      "read_script": "generator/spark-reads-df/verify_617_insert_overwrite_merge.py",
      "description": "INSERT OVERWRITE then MERGE then UPDATE. Tests a full DML chain after an OVERWRITE operation, which replaces all data. Verifies that MERGE and UPDATE work correctly on overwritten data.",
      "status": "pass",
      "duration_ms": 7718,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:32.308154+00:00",
      "read_cold_ms": 5323,
      "read_warm_ms": 890,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 189,
      "write_warm_ms": 273,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/618_insert_overwrite_cdc",
      "num": 618,
      "name": "insert_overwrite_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/618_insert_overwrite_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_618_insert_overwrite_cdc.py",
      "description": "INSERT OVERWRITE + CDC. Tests that CDF correctly records an OVERWRITE as delete+insert events, then verifies subsequent UPDATE is also captured in CDF.",
      "status": "pass",
      "duration_ms": 9339,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:41.647905+00:00",
      "read_cold_ms": 2838,
      "read_warm_ms": 4220,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 121,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/619_rename_cdc",
      "num": 619,
      "name": "rename_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/619_rename_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_619_rename_cdc.py",
      "description": "RENAME COLUMN + CDC. Tests that CDF correctly captures changes after a column rename operation. Requires column mapping mode=name to support rename. Verifies that UPDATE and DELETE work on the renamed column and CDF images reference the new column name.",
      "status": "pass",
      "duration_ms": 3797,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:24:45.445843+00:00",
      "read_cold_ms": 2408,
      "read_warm_ms": 405,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 208,
      "write_warm_ms": 126,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/61_schema_struct_field_metadata",
      "num": 61,
      "name": "schema_struct_field_metadata",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/61_schema_struct_field_metadata.sql",
      "read_script": "generator/spark-reads-df/verify_61_schema_struct_field_metadata.py",
      "description": "Demonstrates struct fields with metadata annotations. Trading records include nested position details with metadata for compliance tracking, data lineage, and schema evolution. Metadata annotations document column purposes, data sources, and transformations for regulatory audits.",
      "status": "pass",
      "duration_ms": 18192,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:03.639645+00:00",
      "read_cold_ms": 1453,
      "read_warm_ms": 376,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 57,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/620_drop_column_dml",
      "num": 620,
      "name": "drop_column_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/620_drop_column_dml.sql",
      "read_script": "generator/spark-reads-df/verify_620_drop_column_dml.py",
      "description": "DROP COLUMN + subsequent DML. Tests that UPDATE and DELETE work correctly after a column has been dropped. Requires column mapping mode=name. Verifies the dropped column is absent from the final schema and that remaining columns are intact.",
      "status": "pass",
      "duration_ms": 3902,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:07.543249+00:00",
      "read_cold_ms": 2695,
      "read_warm_ms": 552,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 176,
      "write_warm_ms": 105,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/621_rename_merge_cdc",
      "num": 621,
      "name": "rename_merge_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/621_rename_merge_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_621_rename_merge_cdc.py",
      "description": "RENAME COLUMN + MERGE + CDC (Change Data Feed). Tests that MERGE uses the renamed column name correctly under columnMapping.mode=name with CDC enabled.",
      "status": "pass",
      "duration_ms": 7975,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:15.520855+00:00",
      "read_cold_ms": 5737,
      "read_warm_ms": 985,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 335,
      "write_warm_ms": 353,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/622_drop_merge",
      "num": 622,
      "name": "drop_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/622_drop_merge.sql",
      "read_script": "generator/spark-reads-df/verify_622_drop_merge.py",
      "description": "DROP COLUMN + MERGE. Tests that MERGE operates correctly after a column has been dropped under columnMapping.mode=name.",
      "status": "pass",
      "duration_ms": 8190,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:23.712816+00:00",
      "read_cold_ms": 6263,
      "read_warm_ms": 913,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 56,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/623_struct_partition",
      "num": 623,
      "name": "struct_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/623_struct_partition.sql",
      "read_script": "generator/spark-reads-df/verify_623_struct_partition.py",
      "description": "STRUCT column + partitioned table. Tests that nested struct values survive partition-aware UPDATE and DELETE operations correctly.",
      "status": "pass",
      "duration_ms": 10038,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:33.752636+00:00",
      "read_cold_ms": 2372,
      "read_warm_ms": 4219,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 48,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/624_struct_evolve",
      "num": 624,
      "name": "struct_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/624_struct_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_624_struct_evolve.py",
      "description": "STRUCT column + schema evolution (ADD COLUMN). Tests that existing struct values are preserved when a new scalar column is added, and that new inserts populate both struct and new column.",
      "status": "pass",
      "duration_ms": 11440,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:45.196473+00:00",
      "read_cold_ms": 4701,
      "read_warm_ms": 1013,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 48,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/625_timestamp_partition",
      "num": 625,
      "name": "timestamp_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/625_timestamp_partition.sql",
      "read_script": "generator/spark-reads-df/verify_625_timestamp_partition.py",
      "description": "TIMESTAMP column + partitioned table (partition by month STRING). Tests that timestamp values survive partition-aware UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 4776,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:49.977471+00:00",
      "read_cold_ms": 3031,
      "read_warm_ms": 1044,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 49,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/626_decimal_constraint",
      "num": 626,
      "name": "decimal_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/626_decimal_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_626_decimal_constraint.py",
      "description": "DECIMAL column + CHECK constraint (amount > 0). Tests that constraint enforcement works with DECIMAL precision types through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 7927,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:25:57.907368+00:00",
      "read_cold_ms": 5926,
      "read_warm_ms": 1051,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 46,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/627_decimal_evolve",
      "num": 627,
      "name": "decimal_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/627_decimal_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_627_decimal_evolve.py",
      "description": "DECIMAL column + schema evolution (ADD COLUMN of DECIMAL type). Tests that existing DECIMAL data is preserved after adding a new DECIMAL column, and that UPDATE and DELETE work on both old and new DECIMAL columns.",
      "status": "pass",
      "duration_ms": 8682,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:26:06.590379+00:00",
      "read_cold_ms": 6301,
      "read_warm_ms": 1084,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 67,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/628_not_null_partition",
      "num": 628,
      "name": "not_null_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/628_not_null_partition.sql",
      "read_script": "generator/spark-reads-df/verify_628_not_null_partition.py",
      "description": "NOT NULL constraint + partitioned table. Tests that NOT NULL columns are preserved correctly through partition-aware UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 7624,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:26:14.215981+00:00",
      "read_cold_ms": 2340,
      "read_warm_ms": 3984,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 49,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/629_not_null_constraint",
      "num": 629,
      "name": "not_null_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/629_not_null_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_629_not_null_constraint.py",
      "description": "NOT NULL columns + CHECK constraint on the same table. Tests that both NOT NULL invariants and CHECK constraints coexist through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 7974,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:26:22.193514+00:00",
      "read_cold_ms": 2435,
      "read_warm_ms": 4033,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 38,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/62_schema_array_type_elements",
      "num": 62,
      "name": "schema_array_type_elements",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/62_schema_array_type_elements.sql",
      "read_script": "generator/spark-reads-df/verify_62_schema_array_type_elements.py",
      "description": "Demonstrates array type with element types for storing multi-valued attributes.",
      "status": "pass",
      "duration_ms": 20528,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:26:42.723012+00:00",
      "read_cold_ms": 2185,
      "read_warm_ms": 3489,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 71,
      "tags": [
        "type:array",
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/630_insert_overwrite_partition",
      "num": 630,
      "name": "insert_overwrite_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/630_insert_overwrite_partition.sql",
      "read_script": "generator/spark-reads-df/verify_630_insert_overwrite_partition.py",
      "description": "INSERT OVERWRITE on a partitioned table followed by UPDATE and DELETE. Tests that OVERWRITE correctly replaces partition data and subsequent DML operates on the overwritten state.",
      "status": "pass",
      "duration_ms": 4954,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:26:47.677969+00:00",
      "read_cold_ms": 2439,
      "read_warm_ms": 1148,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/631_merge_update_delete_chain",
      "num": 631,
      "name": "merge_update_delete_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/631_merge_update_delete_chain.sql",
      "read_script": "generator/spark-reads-df/verify_631_merge_update_delete_chain.py",
      "description": "MERGE then UPDATE then DELETE chain. Different ordering from 524 (MERGE then DELETE) and 551 (DELETE+UPDATE+MERGE). Tests that UPDATE and DELETE operate correctly on data that includes both matched-updated and not-matched-inserted rows from a prior MERGE.",
      "status": "pass",
      "duration_ms": 7604,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:26:55.283621+00:00",
      "read_cold_ms": 2125,
      "read_warm_ms": 4037,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 137,
      "write_warm_ms": 78,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/632_update_merge_update",
      "num": 632,
      "name": "update_merge_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/632_update_merge_update.sql",
      "read_script": "generator/spark-reads-df/verify_632_update_merge_update.py",
      "description": "UPDATE then MERGE then UPDATE (DML sandwich). Tests that a MERGE correctly reads data already modified by UPDATE, and that a subsequent UPDATE works on MERGE output (both matched and inserted rows).",
      "status": "pass",
      "duration_ms": 8535,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:03.820382+00:00",
      "read_cold_ms": 2507,
      "read_warm_ms": 4773,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 220,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/633_delete_merge_delete",
      "num": 633,
      "name": "delete_merge_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/633_delete_merge_delete.sql",
      "read_script": "generator/spark-reads-df/verify_633_delete_merge_delete.py",
      "description": "DELETE then MERGE then DELETE (DML sandwich). Tests that MERGE correctly handles a table with gaps from prior DELETE, and that a second DELETE works on the merged result.",
      "status": "pass",
      "duration_ms": 5659,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:09.481019+00:00",
      "read_cold_ms": 3498,
      "read_warm_ms": 935,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 102,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/634_one_row_all_features",
      "num": 634,
      "name": "one_row_all_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/634_one_row_all_features.sql",
      "read_script": "generator/spark-reads-df/verify_634_one_row_all_features.py",
      "description": "Single row with Deletion Vectors + CDC enabled. Tests all features at minimum scale: INSERT 1, UPDATE, DELETE, INSERT 1 new. Validates that DV and CDC work correctly even with a single row.",
      "status": "pass",
      "duration_ms": 9813,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:19.295463+00:00",
      "read_cold_ms": 6875,
      "read_warm_ms": 796,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/635_two_row_merge",
      "num": 635,
      "name": "two_row_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/635_two_row_merge.sql",
      "read_script": "generator/spark-reads-df/verify_635_two_row_merge.py",
      "description": "Two rows + MERGE. Minimum viable MERGE test with the smallest possible table. MERGE from a 3-row CTE: 2 matched updates + 1 new insert.",
      "status": "pass",
      "duration_ms": 5302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:24.599277+00:00",
      "read_cold_ms": 3100,
      "read_warm_ms": 1132,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 57,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/636_power_of_two_rows",
      "num": 636,
      "name": "power_of_two_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/636_power_of_two_rows.sql",
      "read_script": "generator/spark-reads-df/verify_636_power_of_two_rows.py",
      "description": "Exactly 256 rows (2^8) + DML. Tests alignment boundaries that may affect Parquet row group sizing and deletion vector bitmaps.",
      "status": "pass",
      "duration_ms": 6797,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:31.397760+00:00",
      "read_cold_ms": 5240,
      "read_warm_ms": 954,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 43,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "storage:rowgroup-stats",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/637_thousand_row_merge",
      "num": 637,
      "name": "thousand_row_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/637_thousand_row_merge.sql",
      "read_script": "generator/spark-reads-df/verify_637_thousand_row_merge.py",
      "description": "1000 rows + 1000-row MERGE with full overlap (all matched, no inserts). Scale test for MERGE correctness when every row matches.",
      "status": "pass",
      "duration_ms": 10994,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:42.392749+00:00",
      "read_cold_ms": 6248,
      "read_warm_ms": 895,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/638_no_dv_evolve_merge",
      "num": 638,
      "name": "no_dv_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/638_no_dv_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_638_no_dv_evolve_merge.py",
      "description": "No Deletion Vectors + schema evolution + MERGE. Tests the full-rewrite code path (no DV) combined with ADD COLUMN and MERGE that populates the new column.",
      "status": "pass",
      "duration_ms": 3290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:45.684660+00:00",
      "read_cold_ms": 2272,
      "read_warm_ms": 554,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 82,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/639_no_dv_constraint",
      "num": 639,
      "name": "no_dv_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/639_no_dv_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_639_no_dv_constraint.py",
      "description": "No Deletion Vectors + CHECK constraint + DML. Tests the full-rewrite code path (no DV) with constraint enforcement through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 4836,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:27:50.523218+00:00",
      "read_cold_ms": 1643,
      "read_warm_ms": 161,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 96,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/63_schema_map_type_key_value",
      "num": 63,
      "name": "schema_map_type_key_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/63_schema_map_type_key_value.sql",
      "read_script": "generator/spark-reads-df/verify_63_schema_map_type_key_value.py",
      "description": "Demonstrates map type with key-value pairs for storing dynamic configurations.",
      "status": "pass",
      "duration_ms": 11763,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:02.287567+00:00",
      "read_cold_ms": 2572,
      "read_warm_ms": 885,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 40,
      "tags": [
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/640_rename_drop_merge",
      "num": 640,
      "name": "rename_drop_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/640_rename_drop_merge.sql",
      "read_script": "generator/spark-reads-df/verify_640_rename_drop_merge.py",
      "description": "RENAME COLUMN + DROP COLUMN + MERGE in one script. Tests stacked column mutations under columnMapping.mode=name followed by a MERGE that uses the renamed column and omits the dropped column.",
      "status": "pass",
      "duration_ms": 4871,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:07.160229+00:00",
      "read_cold_ms": 2931,
      "read_warm_ms": 1087,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 103,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/641_struct_colmap",
      "num": 641,
      "name": "struct_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/641_struct_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_641_struct_colmap.py",
      "description": "STRUCT column + column mapping (name mode). Verifies that nested struct values work correctly with physical-to-logical column name mapping through UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 7231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:14.392366+00:00",
      "read_cold_ms": 2472,
      "read_warm_ms": 3349,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/642_struct_constraint",
      "num": 642,
      "name": "struct_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/642_struct_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_642_struct_constraint.py",
      "description": "STRUCT column + CHECK constraint on a non-struct scalar column. Verifies that constraints coexist with nested struct data and are enforced after being added mid-lifecycle.",
      "status": "pass",
      "duration_ms": 4314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:18.706994+00:00",
      "read_cold_ms": 1802,
      "read_warm_ms": 567,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/643_timestamp_constraint",
      "num": 643,
      "name": "timestamp_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/643_timestamp_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_643_timestamp_constraint.py",
      "description": "TIMESTAMP column + CHECK constraint on a numeric column. Verifies that constraints work alongside timestamp data and that DML preserves both.",
      "status": "pass",
      "duration_ms": 7673,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:26.383545+00:00",
      "read_cold_ms": 5887,
      "read_warm_ms": 842,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/644_timestamp_evolve",
      "num": 644,
      "name": "timestamp_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/644_timestamp_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_644_timestamp_evolve.py",
      "description": "TIMESTAMP column + schema evolution (ADD COLUMN). Verifies that adding a new column works correctly when the table already contains timestamp data, and that subsequent DML operates on the evolved schema.",
      "status": "pass",
      "duration_ms": 7441,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:33.825751+00:00",
      "read_cold_ms": 5988,
      "read_warm_ms": 757,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 117,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/645_decimal_colmap",
      "num": 645,
      "name": "decimal_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/645_decimal_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_645_decimal_colmap.py",
      "description": "DECIMAL(15,6) column + column mapping (name mode). Verifies that high- precision decimal values are preserved correctly through column mapping with UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 3938,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:37.765602+00:00",
      "read_cold_ms": 2133,
      "read_warm_ms": 667,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 203,
      "write_warm_ms": 120,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/646_decimal_cdc_merge",
      "num": 646,
      "name": "decimal_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/646_decimal_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_646_decimal_cdc_merge.py",
      "description": "DECIMAL(15,6) + CDC + MERGE. Three-way combination testing that CDF correctly captures pre/post images for decimal columns through MERGE.",
      "status": "pass",
      "duration_ms": 9130,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:46.899150+00:00",
      "read_cold_ms": 6217,
      "read_warm_ms": 716,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 334,
      "write_warm_ms": 377,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/647_no_dv_optimize",
      "num": 647,
      "name": "no_dv_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/647_no_dv_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_647_no_dv_optimize.py",
      "description": "No deletion vectors + OPTIMIZE. Tests that OPTIMIZE compacts fragmented data files on a table without deletion vectors enabled. Data is inserted in 4 small batches to create file fragmentation, then an UPDATE creates more files, and OPTIMIZE compacts them.",
      "status": "pass",
      "duration_ms": 5296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:52.197065+00:00",
      "read_cold_ms": 1246,
      "read_warm_ms": 3197,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 404,
      "write_warm_ms": 243,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/648_no_dv_evolve_cdc",
      "num": 648,
      "name": "no_dv_evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/648_no_dv_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_648_no_dv_evolve_cdc.py",
      "description": "No deletion vectors + schema evolution (ADD COLUMN) + CDC. Three-way combination testing that CDF works correctly across schema changes without deletion vectors.",
      "status": "pass",
      "duration_ms": 2958,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:28:55.156447+00:00",
      "read_cold_ms": 2074,
      "read_warm_ms": 366,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 166,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/649_insert_overwrite_evolve",
      "num": 649,
      "name": "insert_overwrite_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/649_insert_overwrite_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_649_insert_overwrite_evolve.py",
      "description": "INSERT OVERWRITE + schema evolution (ADD COLUMN). Tests that OVERWRITE replaces all data, then ADD COLUMN extends the schema, and a subsequent INSERT populates the new column.",
      "status": "pass",
      "duration_ms": 6056,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:29:01.214557+00:00",
      "read_cold_ms": 1925,
      "read_warm_ms": 400,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 130,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/64_schema_column_metadata_extended",
      "num": 64,
      "name": "schema_column_metadata_extended",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/64_schema_column_metadata_extended.sql",
      "read_script": "generator/spark-reads-df/verify_64_schema_column_metadata_extended.py",
      "description": "Demonstrates extended column metadata for constraints and properties. Column metadata can store: - delta.columnMapping.* (physical/logical name mapping) - delta.identity.* (identity column properties) - delta.invariants (column-level constraints)",
      "status": "pass",
      "duration_ms": 24253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:29:25.471393+00:00",
      "read_cold_ms": 1852,
      "read_warm_ms": 876,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 69,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/650_not_null_merge_cdc",
      "num": 650,
      "name": "not_null_merge_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/650_not_null_merge_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_650_not_null_merge_cdc.py",
      "description": "NOT NULL constraints + MERGE + CDC. Three-way combination testing that NOT NULL enforcement is maintained through MERGE operations while CDF correctly captures change records.",
      "status": "pass",
      "duration_ms": 9438,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:29:34.912076+00:00",
      "read_cold_ms": 3360,
      "read_warm_ms": 3935,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 246,
      "write_warm_ms": 242,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/651_rename_partition",
      "num": 651,
      "name": "rename_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/651_rename_partition.sql",
      "read_script": "generator/spark-reads-df/verify_651_rename_partition.py",
      "description": "RENAME COLUMN + partitioned table with column mapping. Verifies that renaming a column works correctly on a partitioned table and that subsequent DML uses the new column name.",
      "status": "pass",
      "duration_ms": 7947,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:29:42.859910+00:00",
      "read_cold_ms": 5914,
      "read_warm_ms": 972,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 206,
      "write_warm_ms": 125,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/652_rename_constraint",
      "num": 652,
      "name": "rename_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/652_rename_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_652_rename_constraint.py",
      "description": "RENAME COLUMN + CHECK constraint with column mapping. Verifies that renaming a column and then adding a constraint on a different column works correctly together.",
      "status": "pass",
      "duration_ms": 4404,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:29:47.264884+00:00",
      "read_cold_ms": 2523,
      "read_warm_ms": 908,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/653_drop_partition",
      "num": 653,
      "name": "drop_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/653_drop_partition.sql",
      "read_script": "generator/spark-reads-df/verify_653_drop_partition.py",
      "description": "DROP COLUMN + partitioned table with column mapping. Verifies that dropping a non-partition column works correctly on a partitioned table and that subsequent DML operates on the reduced schema.",
      "status": "pass",
      "duration_ms": 8841,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:29:56.108678+00:00",
      "read_cold_ms": 6284,
      "read_warm_ms": 1433,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 124,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/654_drop_cdc",
      "num": 654,
      "name": "drop_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/654_drop_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_654_drop_cdc.py",
      "description": "DROP COLUMN + CDC with column mapping. Verifies that CDF correctly captures changes after a column has been dropped from the schema.",
      "status": "pass",
      "duration_ms": 8403,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:30:04.513591+00:00",
      "read_cold_ms": 6096,
      "read_warm_ms": 1333,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 108,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/655_multi_rename",
      "num": 655,
      "name": "multi_rename",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/655_multi_rename.sql",
      "read_script": "generator/spark-reads-df/verify_655_multi_rename.py",
      "description": "Two sequential RENAME COLUMN operations with column mapping. Verifies that multiple renames work correctly and that subsequent DML uses the final column names.",
      "status": "pass",
      "duration_ms": 11746,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:30:16.261405+00:00",
      "read_cold_ms": 7184,
      "read_warm_ms": 3495,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 250,
      "write_warm_ms": 133,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/656_delete_not_in",
      "num": 656,
      "name": "delete_not_in",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/656_delete_not_in.sql",
      "read_script": "generator/spark-reads-df/verify_656_delete_not_in.py",
      "description": "DELETE with compound range predicate simulating NOT IN behavior. Tests DELETE WHERE id<20 OR id>80, which removes rows outside the [20,80] range.",
      "status": "pass",
      "duration_ms": 8216,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:30:24.479862+00:00",
      "read_cold_ms": 2747,
      "read_warm_ms": 4351,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 58,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/657_update_between",
      "num": 657,
      "name": "update_between",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/657_update_between.sql",
      "read_script": "generator/spark-reads-df/verify_657_update_between.py",
      "description": "UPDATE with BETWEEN predicate on different columns. Tests that BETWEEN range predicates work correctly for UPDATE operations on both id and score columns.",
      "status": "pass",
      "duration_ms": 6765,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:30:31.245938+00:00",
      "read_cold_ms": 2256,
      "read_warm_ms": 3724,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 197,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/658_merge_multiple_matched",
      "num": 658,
      "name": "merge_multiple_matched",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/658_merge_multiple_matched.sql",
      "read_script": "generator/spark-reads-df/verify_658_merge_multiple_matched.py",
      "description": "MERGE with two WHEN MATCHED clauses using different conditions. Tests that conditional MERGE correctly applies different updates based on the matched row's data.",
      "status": "pass",
      "duration_ms": 7554,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:30:38.802909+00:00",
      "read_cold_ms": 2445,
      "read_warm_ms": 4096,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/659_delete_multi_condition",
      "num": 659,
      "name": "delete_multi_condition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/659_delete_multi_condition.sql",
      "read_script": "generator/spark-reads-df/verify_659_delete_multi_condition.py",
      "description": "DELETE with complex multi-condition OR predicate on different columns. Tests that DELETE correctly evaluates compound predicates involving both score and category columns.",
      "status": "pass",
      "duration_ms": 8568,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:30:47.375246+00:00",
      "read_cold_ms": 6469,
      "read_warm_ms": 895,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/65_schema_deeply_nested_complex",
      "num": 65,
      "name": "schema_deeply_nested_complex",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/65_schema_deeply_nested_complex.sql",
      "read_script": "generator/spark-reads-df/verify_65_schema_deeply_nested_complex.py",
      "description": "This table is IGNORED in automated tests due to an Arrow 57.x limitation.",
      "status": "pass",
      "duration_ms": 16883,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:04.260042+00:00",
      "read_cold_ms": 2698,
      "read_warm_ms": 744,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 331,
      "write_warm_ms": 379,
      "tags": [
        "type:array",
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/660_merge_insert_overwrite",
      "num": 660,
      "name": "merge_insert_overwrite",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/660_merge_insert_overwrite.sql",
      "read_script": "generator/spark-reads-df/verify_660_merge_insert_overwrite.py",
      "description": "MERGE then INSERT OVERWRITE. Tests that INSERT OVERWRITE completely replaces all data after a MERGE has been applied.",
      "status": "pass",
      "duration_ms": 5463,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:09.726600+00:00",
      "read_cold_ms": 2949,
      "read_warm_ms": 1121,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 141,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/661_struct_optimize",
      "num": 661,
      "name": "struct_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/661_struct_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_661_struct_optimize.py",
      "description": "STRUCT column + OPTIMIZE. INSERT 200 rows in 4 batches with named_struct, then OPTIMIZE to compact, followed by DELETE and UPDATE. Verifies struct values survive compaction and subsequent DML.",
      "status": "pass",
      "duration_ms": 12709,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:22.439951+00:00",
      "read_cold_ms": 5827,
      "read_warm_ms": 1064,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 325,
      "write_warm_ms": 264,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/662_struct_dv_cdc",
      "num": 662,
      "name": "struct_dv_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/662_struct_dv_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_662_struct_dv_cdc.py",
      "description": "STRUCT + Deletion Vectors + CDC. INSERT 100 rows with struct, DELETE using DVs, UPDATE. CDF tracks all changes. Verifies struct values survive DV-based deletes and CDC captures struct mutations correctly.",
      "status": "pass",
      "duration_ms": 10073,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:32.516807+00:00",
      "read_cold_ms": 2442,
      "read_warm_ms": 750,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 79,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/663_timestamp_merge_cdc",
      "num": 663,
      "name": "timestamp_merge_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/663_timestamp_merge_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_663_timestamp_merge_cdc.py",
      "description": "TIMESTAMP + MERGE + CDC. INSERT 100 rows with timestamp columns, then MERGE from 120-row source. CDC enabled to track insert/update changes. Verifies timestamp precision is preserved through MERGE operations.",
      "status": "pass",
      "duration_ms": 9422,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:41.944569+00:00",
      "read_cold_ms": 2489,
      "read_warm_ms": 5064,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 274,
      "write_warm_ms": 454,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/664_decimal_constraint_partition",
      "num": 664,
      "name": "decimal_constraint_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/664_decimal_constraint_partition.sql",
      "read_script": "generator/spark-reads-df/verify_664_decimal_constraint_partition.py",
      "description": "DECIMAL + CHECK constraint + partitioning. INSERT 120 rows across 3 partitions with DECIMAL columns, ADD CONSTRAINT, UPDATE, DELETE. Verifies that DECIMAL precision is preserved with constraints on partitioned data.",
      "status": "pass",
      "duration_ms": 4466,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:46.414341+00:00",
      "read_cold_ms": 2457,
      "read_warm_ms": 904,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 227,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/665_no_dv_colmap",
      "num": 665,
      "name": "no_dv_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/665_no_dv_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_665_no_dv_colmap.py",
      "description": "No Deletion Vectors + column mapping (name mode). enableDeletionVectors=false forces copy-on-write for DELETE/UPDATE. Column mapping=name tracks physical IDs. Verifies correct behavior without DVs under column mapping.",
      "status": "pass",
      "duration_ms": 5367,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:51.783097+00:00",
      "read_cold_ms": 4512,
      "read_warm_ms": 406,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 78,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/666_insert_overwrite_cdc_partition",
      "num": 666,
      "name": "insert_overwrite_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/666_insert_overwrite_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_666_insert_overwrite_cdc_partition.py",
      "description": "INSERT OVERWRITE + CDC + partitioning. Partitioned table with CDC enabled. INSERT data, then INSERT OVERWRITE a single partition, then DML. Tests that CDF correctly captures overwrite events and partition-level replacement.",
      "status": "pass",
      "duration_ms": 8081,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:31:59.866009+00:00",
      "read_cold_ms": 2541,
      "read_warm_ms": 3985,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 166,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/667_not_null_constraint_evolve",
      "num": 667,
      "name": "not_null_constraint_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/667_not_null_constraint_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_667_not_null_constraint_evolve.py",
      "description": "NOT NULL constraint + CHECK constraint + schema evolution. Three-way combo: NOT NULL on existing column, CHECK constraint, then ADD COLUMN (schema evolution). Verifies constraints survive schema changes and new column defaults to NULL for existing rows.",
      "status": "pass",
      "duration_ms": 3855,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:32:03.722383+00:00",
      "read_cold_ms": 2243,
      "read_warm_ms": 805,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 87,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/668_rename_cdc_merge",
      "num": 668,
      "name": "rename_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/668_rename_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_668_rename_cdc_merge.py",
      "description": "RENAME COLUMN + CDC + MERGE. Column mapping=name with CDC enabled. INSERT 100 rows, RENAME a column, then MERGE 120 rows using the new column name. Verifies MERGE resolves renamed columns correctly and CDF captures changes.",
      "status": "pass",
      "duration_ms": 8760,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:32:12.482933+00:00",
      "read_cold_ms": 6559,
      "read_warm_ms": 1144,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 333,
      "write_warm_ms": 445,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/669_drop_optimize",
      "num": 669,
      "name": "drop_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/669_drop_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_669_drop_optimize.py",
      "description": "DROP COLUMN + OPTIMIZE. Column mapping=name required for DROP. INSERT 200 rows in 4 batches, DROP a column, OPTIMIZE to compact, then DML. Verifies that OPTIMIZE correctly handles schema after column drop.",
      "status": "pass",
      "duration_ms": 15208,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:32:27.692202+00:00",
      "read_cold_ms": 8021,
      "read_warm_ms": 874,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 257,
      "write_warm_ms": 126,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/66_statistics_per_file_minmax",
      "num": 66,
      "name": "statistics_per_file_minmax",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/66_statistics_per_file_minmax.sql",
      "read_script": "generator/spark-reads-df/verify_66_statistics_per_file_minmax.py",
      "description": "Demonstrates per-file statistics with min/max/null counts. Statistics are stored in the add action's stats field as JSON.",
      "status": "pass",
      "duration_ms": 39435,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:33:07.197890+00:00",
      "read_cold_ms": 2733,
      "read_warm_ms": 792,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 124,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/670_wide_mixed_types",
      "num": 670,
      "name": "wide_mixed_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/670_wide_mixed_types.sql",
      "read_script": "generator/spark-reads-df/verify_670_wide_mixed_types.py",
      "description": "20 columns of mixed types + DML. Tests wide schema with diverse data types including STRING, INT, DOUBLE, BOOLEAN, DECIMAL, TIMESTAMP, DATE, BIGINT, FLOAT, SHORT. Verifies all types survive UPDATE and DELETE correctly.",
      "status": "pass",
      "duration_ms": 11028,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:33:18.226744+00:00",
      "read_cold_ms": 4535,
      "read_warm_ms": 918,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 54,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/671_thirty_batch_insert",
      "num": 671,
      "name": "thirty_batch_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/671_thirty_batch_insert.sql",
      "read_script": "generator/spark-reads-df/verify_671_thirty_batch_insert.py",
      "description": "30 INSERT batches of 10 rows each (300 total) + DML. Tests extreme file fragmentation from many small commits. DELETE and UPDATE operate on the fragmented file set.",
      "status": "pass",
      "duration_ms": 9513,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:33:27.741015+00:00",
      "read_cold_ms": 3553,
      "read_warm_ms": 944,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2160,
      "write_warm_ms": 2143,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/672_merge_then_optimize",
      "num": 672,
      "name": "merge_then_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/672_merge_then_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_672_merge_then_optimize.py",
      "description": "MERGE then OPTIMIZE then DML. Tests that DML operations work correctly on data that has been through both MERGE and OPTIMIZE compaction.",
      "status": "pass",
      "duration_ms": 9046,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:33:36.789377+00:00",
      "read_cold_ms": 6272,
      "read_warm_ms": 1098,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 199,
      "write_warm_ms": 181,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/673_struct_partition_cdc",
      "num": 673,
      "name": "struct_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/673_struct_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_673_struct_partition_cdc.py",
      "description": "STRUCT + partition + CDC. Three-way combination. INSERT 90 rows with struct across 3 region partitions, CDC enabled. DELETE and UPDATE test that struct values survive partitioned DML with change tracking.",
      "status": "pass",
      "duration_ms": 9591,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:33:46.382898+00:00",
      "read_cold_ms": 2242,
      "read_warm_ms": 4926,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 126,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/674_timestamp_colmap",
      "num": 674,
      "name": "timestamp_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/674_timestamp_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_674_timestamp_colmap.py",
      "description": "TIMESTAMP + column mapping (name mode). Tests that timestamp precision is preserved under column mapping, where physical column IDs differ from logical names. UPDATE and DELETE verify timestamp values survive.",
      "status": "pass",
      "duration_ms": 7320,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:33:53.705074+00:00",
      "read_cold_ms": 5814,
      "read_warm_ms": 697,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 126,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/675_decimal_optimize",
      "num": 675,
      "name": "decimal_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/675_decimal_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_675_decimal_optimize.py",
      "description": "DECIMAL + OPTIMIZE. INSERT 200 rows in 4 batches with DECIMAL columns, OPTIMIZE to compact, then UPDATE and DELETE. Verifies DECIMAL precision survives OPTIMIZE compaction and subsequent DML.",
      "status": "pass",
      "duration_ms": 8283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:34:01.989933+00:00",
      "read_cold_ms": 5970,
      "read_warm_ms": 852,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 276,
      "write_warm_ms": 293,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/676_not_null_partition_merge",
      "num": 676,
      "name": "not_null_partition_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/676_not_null_partition_merge.sql",
      "read_script": "generator/spark-reads-df/verify_676_not_null_partition_merge.py",
      "description": "NOT NULL + partition + MERGE. Three-way combination. Partitioned table with NOT NULL constraints, then MERGE. Verifies NOT NULL enforcement through MERGE inserts on partitioned data.",
      "status": "pass",
      "duration_ms": 11412,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:34:13.406562+00:00",
      "read_cold_ms": 6008,
      "read_warm_ms": 842,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 134,
      "write_warm_ms": 119,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/677_rename_partition_merge",
      "num": 677,
      "name": "rename_partition_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/677_rename_partition_merge.sql",
      "read_script": "generator/spark-reads-df/verify_677_rename_partition_merge.py",
      "description": "RENAME COLUMN + partition + MERGE. Column mapping=name with partitioning. INSERT 120 rows across 3 regions, RENAME a non-partition column, then MERGE 150 rows using the new name. Verifies MERGE handles renamed columns on partitioned tables.",
      "status": "pass",
      "duration_ms": 4589,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:34:17.997350+00:00",
      "read_cold_ms": 2578,
      "read_warm_ms": 795,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 136,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/678_drop_cdc_merge",
      "num": 678,
      "name": "drop_cdc_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/678_drop_cdc_merge.sql",
      "read_script": "generator/spark-reads-df/verify_678_drop_cdc_merge.py",
      "description": "DROP COLUMN + CDC + MERGE. Column mapping=name with CDC enabled. INSERT 100 rows, DROP a column, then MERGE 120 rows on the reduced schema. Verifies MERGE works correctly after column drop with CDF tracking.",
      "status": "pass",
      "duration_ms": 10437,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:34:28.436340+00:00",
      "read_cold_ms": 6183,
      "read_warm_ms": 654,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 389,
      "write_warm_ms": 411,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/679_insert_overwrite_constraint",
      "num": 679,
      "name": "insert_overwrite_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/679_insert_overwrite_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_679_insert_overwrite_constraint.py",
      "description": "INSERT OVERWRITE + CHECK constraint. INSERT initial rows, ADD CONSTRAINT requiring val>0, then INSERT OVERWRITE with all-valid data. Verifies that the constraint is enforced during overwrite and the table is fully replaced.",
      "status": "pass",
      "duration_ms": 2465,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:34:30.902512+00:00",
      "read_cold_ms": 1789,
      "read_warm_ms": 389,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121,
      "write_warm_ms": 89,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/67_partition_value_serialization_types",
      "num": 67,
      "name": "partition_value_serialization_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/67_partition_value_serialization_types.sql",
      "read_script": "generator/spark-reads-df/verify_67_partition_value_serialization_types.py",
      "description": "Demonstrates partition value serialization for different data types. Partition values are serialized to strings in the add action. Rules: - null: empty string - boolean: \"true\"/\"false\" - numeric: string representation - date: \"yyyy-MM-dd\" - timestamp: \"yyyy-MM-dd HH:mm:ss.SSS",
      "status": "pass",
      "duration_ms": 27207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:34:58.111601+00:00",
      "read_cold_ms": 5308,
      "read_warm_ms": 971,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2824,
      "write_warm_ms": 3015,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/680_no_dv_partition_merge",
      "num": 680,
      "name": "no_dv_partition_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/680_no_dv_partition_merge.sql",
      "read_script": "generator/spark-reads-df/verify_680_no_dv_partition_merge.py",
      "description": "No Deletion Vectors + partition + MERGE. enableDeletionVectors=false forces copy-on-write for all DML. Partitioned table with MERGE. Verifies that MERGE correctly rewrites partition files without DVs.",
      "status": "pass",
      "duration_ms": 2879,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:00.991984+00:00",
      "read_cold_ms": 2124,
      "read_warm_ms": 255,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 155,
      "write_warm_ms": 166,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/681_struct_rename",
      "num": 681,
      "name": "struct_rename",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/681_struct_rename.sql",
      "read_script": "generator/spark-reads-df/verify_681_struct_rename.py",
      "description": "STRUCT column + RENAME non-struct column with column mapping. Verifies that struct values survive a column rename on a sibling column and that subsequent DML works correctly with the new column name.",
      "status": "pass",
      "duration_ms": 7756,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:08.749597+00:00",
      "read_cold_ms": 2443,
      "read_warm_ms": 3594,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 118,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/682_struct_drop",
      "num": 682,
      "name": "struct_drop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/682_struct_drop.sql",
      "read_script": "generator/spark-reads-df/verify_682_struct_drop.py",
      "description": "STRUCT column + DROP non-struct column with column mapping. Verifies that struct values survive a column drop on a sibling column and that subsequent DML works correctly on the reduced schema.",
      "status": "pass",
      "duration_ms": 7938,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:16.689625+00:00",
      "read_cold_ms": 5291,
      "read_warm_ms": 1101,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 103,
      "write_warm_ms": 83,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/683_timestamp_optimize",
      "num": 683,
      "name": "timestamp_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/683_timestamp_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_683_timestamp_optimize.py",
      "description": "TIMESTAMP column + OPTIMIZE. INSERT 200 rows in 4 batches with timestamp values, OPTIMIZE to compact, then DML. Verifies timestamp precision is preserved through compaction and subsequent operations.",
      "status": "pass",
      "duration_ms": 11632,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:28.322762+00:00",
      "read_cold_ms": 5422,
      "read_warm_ms": 913,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 416,
      "write_warm_ms": 182,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/684_decimal_rename",
      "num": 684,
      "name": "decimal_rename",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/684_decimal_rename.sql",
      "read_script": "generator/spark-reads-df/verify_684_decimal_rename.py",
      "description": "DECIMAL column + RENAME with column mapping. Verifies that DECIMAL precision is preserved after renaming a DECIMAL column and that subsequent reads use the new column name correctly.",
      "status": "pass",
      "duration_ms": 3444,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:31.768464+00:00",
      "read_cold_ms": 2445,
      "read_warm_ms": 617,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 60,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/685_not_null_optimize",
      "num": 685,
      "name": "not_null_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/685_not_null_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_685_not_null_optimize.py",
      "description": "NOT NULL constraints + OPTIMIZE. INSERT 200 rows in 4 batches with NOT NULL columns, OPTIMIZE to compact, then DELETE. Verifies NOT NULL metadata survives compaction and that deleted rows are correctly removed.",
      "status": "pass",
      "duration_ms": 9104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:40.873929+00:00",
      "read_cold_ms": 6522,
      "read_warm_ms": 914,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 195,
      "write_warm_ms": 298,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/686_insert_overwrite_optimize",
      "num": 686,
      "name": "insert_overwrite_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/686_insert_overwrite_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_686_insert_overwrite_optimize.py",
      "description": "INSERT OVERWRITE + OPTIMIZE. Partitioned table with INSERT OVERWRITE followed by OPTIMIZE compaction, then UPDATE and DELETE. Verifies that OPTIMIZE correctly compacts files after an overwrite and DML works post-compact.",
      "status": "pass",
      "duration_ms": 9662,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:50.537700+00:00",
      "read_cold_ms": 7282,
      "read_warm_ms": 1344,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 212,
      "write_warm_ms": 156,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/687_rename_evolve",
      "num": 687,
      "name": "rename_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/687_rename_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_687_rename_evolve.py",
      "description": "RENAME COLUMN + ADD COLUMN (schema evolution) with column mapping. Verifies that a rename followed by schema evolution works correctly and that the new column defaults to NULL for existing rows.",
      "status": "pass",
      "duration_ms": 6023,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:35:56.565304+00:00",
      "read_cold_ms": 3081,
      "read_warm_ms": 1648,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 81,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/688_drop_evolve",
      "num": 688,
      "name": "drop_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/688_drop_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_688_drop_evolve.py",
      "description": "DROP COLUMN + ADD COLUMN (schema evolution) with column mapping. Verifies that dropping a column followed by adding a new column works correctly. The new column defaults to NULL for existing rows.",
      "status": "pass",
      "duration_ms": 13079,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:36:09.649350+00:00",
      "read_cold_ms": 7248,
      "read_warm_ms": 729,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 73,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/689_struct_decimal_mix",
      "num": 689,
      "name": "struct_decimal_mix",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/689_struct_decimal_mix.sql",
      "read_script": "generator/spark-reads-df/verify_689_struct_decimal_mix.py",
      "description": "STRUCT + DECIMAL in same table. Verifies that both complex nested types (struct) and high-precision numeric types (decimal) coexist correctly through INSERT, UPDATE, and DELETE operations.",
      "status": "pass",
      "duration_ms": 12276,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:36:21.927212+00:00",
      "read_cold_ms": 3136,
      "read_warm_ms": 4139,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 133,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/68_feature_names_registry_validation",
      "num": 68,
      "name": "feature_names_registry_validation",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/68_feature_names_registry_validation.sql",
      "read_script": "generator/spark-reads-df/verify_68_feature_names_registry_validation.py",
      "description": "Demonstrates valid feature names in table features registry. generatedColumns, allowColumnDefaults, changeDataFeed, columnMapping, identityColumns, deletionVectors, timestampNtz, v2Checkpoint, etc.",
      "status": "pass",
      "duration_ms": 25379,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:36:47.308229+00:00",
      "read_cold_ms": 2302,
      "read_warm_ms": 698,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 336,
      "write_warm_ms": 290,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/690_timestamp_decimal_mix",
      "num": 690,
      "name": "timestamp_decimal_mix",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/690_timestamp_decimal_mix.sql",
      "read_script": "generator/spark-reads-df/verify_690_timestamp_decimal_mix.py",
      "description": "TIMESTAMP + DECIMAL in same table. Verifies that timestamp precision and decimal precision coexist correctly through INSERT, UPDATE, and DELETE.",
      "status": "pass",
      "duration_ms": 12590,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:36:59.900403+00:00",
      "read_cold_ms": 7276,
      "read_warm_ms": 663,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 197,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/691_five_col_partition",
      "num": 691,
      "name": "five_col_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/691_five_col_partition.sql",
      "read_script": "generator/spark-reads-df/verify_691_five_col_partition.py",
      "description": "Multi-column partition with 3 partition columns (region, year, quarter). Tests extreme partition fanout with DML across many partition combinations. Verifies correct file placement and data isolation per partition.",
      "status": "pass",
      "duration_ms": 8357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:37:08.262188+00:00",
      "read_cold_ms": 6668,
      "read_warm_ms": 959,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 264,
      "write_warm_ms": 312,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/692_merge_chain_three",
      "num": 692,
      "name": "merge_chain_three",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/692_merge_chain_three.sql",
      "read_script": "generator/spark-reads-df/verify_692_merge_chain_three.py",
      "description": "Three sequential MERGE operations. Tests MERGE-MERGE-MERGE chain where each MERGE updates existing rows and inserts new ones. Verifies that multiple MERGE commits stack correctly in the transaction log.",
      "status": "pass",
      "duration_ms": 14296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:37:22.563420+00:00",
      "read_cold_ms": 7421,
      "read_warm_ms": 578,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 197,
      "write_warm_ms": 110,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/693_optimize_chain",
      "num": 693,
      "name": "optimize_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/693_optimize_chain.sql",
      "read_script": "generator/spark-reads-df/verify_693_optimize_chain.py",
      "description": "Three sequential OPTIMIZEs with DML between each. Tests repeated compaction to verify that OPTIMIZE is idempotent and that interleaved DML between compactions produces correct results.",
      "status": "pass",
      "duration_ms": 6092,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:37:28.657111+00:00",
      "read_cold_ms": 1758,
      "read_warm_ms": 3355,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 142,
      "write_warm_ms": 165,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/694_cdc_no_dv_merge",
      "num": 694,
      "name": "cdc_no_dv_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/694_cdc_no_dv_merge.sql",
      "read_script": "generator/spark-reads-df/verify_694_cdc_no_dv_merge.py",
      "description": "CDC + no Deletion Vectors + MERGE. Tests Change Data Feed capture through a MERGE operation when deletion vectors are disabled (copy-on-write mode). Verifies CDF events are correctly generated under copy-on-write semantics.",
      "status": "pass",
      "duration_ms": 4021,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:37:32.680853+00:00",
      "read_cold_ms": 2163,
      "read_warm_ms": 265,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 157,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/695_struct_cdc_partition",
      "num": 695,
      "name": "struct_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/695_struct_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_695_struct_cdc_partition.py",
      "description": "STRUCT + CDC + partition. Three-way combination: struct column with Change Data Feed enabled on a partitioned table. Verifies CDF correctly captures struct values in change events across partitions.",
      "status": "pass",
      "duration_ms": 10294,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:37:42.976100+00:00",
      "read_cold_ms": 2579,
      "read_warm_ms": 703,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
      "write_warm_ms": 56,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/696_decimal_not_null",
      "num": 696,
      "name": "decimal_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/696_decimal_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_696_decimal_not_null.py",
      "description": "DECIMAL + NOT NULL constraints. Verifies that DECIMAL columns with NOT NULL constraints work correctly through INSERT, UPDATE, and DELETE operations.",
      "status": "pass",
      "duration_ms": 9896,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:37:52.873579+00:00",
      "read_cold_ms": 8158,
      "read_warm_ms": 814,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 42,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/697_timestamp_not_null",
      "num": 697,
      "name": "timestamp_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/697_timestamp_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_697_timestamp_not_null.py",
      "description": "TIMESTAMP + NOT NULL constraints. Verifies that TIMESTAMP columns with NOT NULL constraints work correctly through INSERT, UPDATE, and DELETE operations.",
      "status": "pass",
      "duration_ms": 8745,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:38:01.622002+00:00",
      "read_cold_ms": 6657,
      "read_warm_ms": 939,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 42,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/698_colmap_no_dv_evolve",
      "num": 698,
      "name": "colmap_no_dv_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/698_colmap_no_dv_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_698_colmap_no_dv_evolve.py",
      "description": "Column mapping + no Deletion Vectors + schema evolution. Three-way combo: column mapping name mode with DVs disabled (copy-on-write) and ADD COLUMN. Verifies schema evolution works under copy-on-write with column mapping.",
      "status": "pass",
      "duration_ms": 6953,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:38:08.579160+00:00",
      "read_cold_ms": 2766,
      "read_warm_ms": 3757,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 69,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/699_partition_merge_delete_update",
      "num": 699,
      "name": "partition_merge_delete_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/699_partition_merge_delete_update.sql",
      "read_script": "generator/spark-reads-df/verify_699_partition_merge_delete_update.py",
      "description": "Partition + MERGE + DELETE + UPDATE. All three DML types plus MERGE on a partitioned table. Verifies that all four operation types produce correct results when interleaved on a partitioned table.",
      "status": "pass",
      "duration_ms": 8828,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:38:17.409304+00:00",
      "read_cold_ms": 3320,
      "read_warm_ms": 757,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 227,
      "write_warm_ms": 235,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/69_checkpoint_schema_full_spec",
      "num": 69,
      "name": "checkpoint_schema_full_spec",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/69_checkpoint_schema_full_spec.sql",
      "read_script": "generator/spark-reads-df/verify_69_checkpoint_schema_full_spec.py",
      "description": "Demonstrates full checkpoint schema specification with all action types.",
      "status": "pass",
      "duration_ms": 24602,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:38:42.013284+00:00",
      "read_cold_ms": 2767,
      "read_warm_ms": 429,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 846,
      "write_warm_ms": 692,
      "tags": [
        "type:array",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/700_grand_finale",
      "num": 700,
      "name": "grand_finale",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/700_grand_finale.sql",
      "read_script": "generator/spark-reads-df/verify_700_grand_finale.py",
      "description": "Test 700: the ultimate combination test. Combines all features that can coexist: Deletion Vectors + CDC + column mapping + partitioning + CHECK constraint + schema evolution (ADD COLUMN) + OPTIMIZE + MERGE + DELETE + UPDATE + RENAME COLUMN. Verifies that all features work...",
      "status": "pass",
      "duration_ms": 7635,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:38:49.651461+00:00",
      "read_cold_ms": 5520,
      "read_warm_ms": 623,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1205,
      "write_warm_ms": 677,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/701_update_partition_key",
      "num": 701,
      "name": "update_partition_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/701_update_partition_key.sql",
      "read_script": "generator/spark-reads-df/verify_701_update_partition_key.py",
      "description": "UPDATE that changes the partition column value, moving rows between partitions. This is a common production pattern where status changes cause rows to migrate between partitions (e.g., order status transitions). Engines must correctly handle cross-partition row movement in a...",
      "status": "pass",
      "duration_ms": 10817,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:00.470470+00:00",
      "read_cold_ms": 5907,
      "read_warm_ms": 584,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 142,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/702_merge_composite_key",
      "num": 702,
      "name": "merge_composite_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/702_merge_composite_key.sql",
      "read_script": "generator/spark-reads-df/verify_702_merge_composite_key.py",
      "description": "MERGE on a composite key (two columns). Production pattern where natural keys like (region, product_id) are used instead of a single surrogate key. The engine must correctly match on multiple join conditions in the MERGE ON clause.",
      "status": "pass",
      "duration_ms": 4936,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:05.408279+00:00",
      "read_cold_ms": 3432,
      "read_warm_ms": 812,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 39,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/703_merge_scd_type2",
      "num": 703,
      "name": "merge_scd_type2",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/703_merge_scd_type2.sql",
      "read_script": "generator/spark-reads-df/verify_703_merge_scd_type2.py",
      "description": "SCD Type 2 (Slowly Changing Dimension) pattern: close old records by setting is_current=false and effective_to to a cutoff date, then insert new versions of those records. This is a classic production pattern in data warehousing for maintaining history of dimension changes.",
      "status": "pass",
      "duration_ms": 11874,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:17.284528+00:00",
      "read_cold_ms": 6255,
      "read_warm_ms": 1061,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 46,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/704_upsert_heavy",
      "num": 704,
      "name": "upsert_heavy",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/704_upsert_heavy.sql",
      "read_script": "generator/spark-reads-df/verify_704_upsert_heavy.py",
      "description": "Upsert-heavy pattern: 5 sequential MERGEs simulating incremental ETL loads. Each batch overlaps partially with existing data. This stresses the engine's ability to handle many small MERGE transactions with varying overlap ratios, a common pattern in production incremental...",
      "status": "pass",
      "duration_ms": 8800,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:26.085756+00:00",
      "read_cold_ms": 3334,
      "read_warm_ms": 771,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 137,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/705_append_compact_cycle",
      "num": 705,
      "name": "append_compact_cycle",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/705_append_compact_cycle.sql",
      "read_script": "generator/spark-reads-df/verify_705_append_compact_cycle.py",
      "description": "Append-then-compact cycle: many small INSERTs (simulating streaming micro-batches) followed by OPTIMIZE. This is the standard production pattern for streaming ingestion where micro-batches create many small files that must be compacted. Tests that OPTIMIZE correctly rewrites...",
      "status": "pass",
      "duration_ms": 5059,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:31.147652+00:00",
      "read_cold_ms": 3828,
      "read_warm_ms": 326,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 480,
      "write_warm_ms": 640,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/706_late_arriving_data",
      "num": 706,
      "name": "late_arriving_data",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/706_late_arriving_data.sql",
      "read_script": "generator/spark-reads-df/verify_706_late_arriving_data.py",
      "description": "Late-arriving data: INSERTs into older partitions after newer data already exists. This is a common production pattern for delayed event processing, where events arrive out of order due to network delays, batch reprocessing, or timezone issues. Tests that the engine correctly...",
      "status": "pass",
      "duration_ms": 10718,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:41.869636+00:00",
      "read_cold_ms": 7575,
      "read_warm_ms": 1512,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 75,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/707_bulk_delete_reinsert",
      "num": 707,
      "name": "bulk_delete_reinsert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/707_bulk_delete_reinsert.sql",
      "read_script": "generator/spark-reads-df/verify_707_bulk_delete_reinsert.py",
      "description": "Bulk delete + re-insert: production data correction pattern. A batch of rows is discovered to be incorrect, deleted by range, and replaced with corrected data. This tests the engine's handling of large contiguous deletes followed by inserts that reuse the same id space.",
      "status": "pass",
      "duration_ms": 10048,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:51.918895+00:00",
      "read_cold_ms": 7013,
      "read_warm_ms": 949,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 120,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/708_skewed_partition",
      "num": 708,
      "name": "skewed_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/708_skewed_partition.sql",
      "read_script": "generator/spark-reads-df/verify_708_skewed_partition.py",
      "description": "Heavily skewed partition distribution: 95% of data in one partition, 5% in others. Tests partition pruning efficiency and DML operations on unbalanced data. Production pattern: most events are \"default\" category with rare exceptions. Engine must handle tiny-partition UPDATE and...",
      "status": "pass",
      "duration_ms": 7899,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:39:59.819342+00:00",
      "read_cold_ms": 5730,
      "read_warm_ms": 964,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 82,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/709_sparse_columns",
      "num": 709,
      "name": "sparse_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/709_sparse_columns.sql",
      "read_script": "generator/spark-reads-df/verify_709_sparse_columns.py",
      "description": "Sparse table where most columns are NULL for most rows. Production pattern for wide event tables where different event types populate different columns. Tests Parquet encoding efficiency, NULL statistics, and DML operations that filter on NULL/non-NULL combinations.",
      "status": "pass",
      "duration_ms": 7789,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:40:07.610147+00:00",
      "read_cold_ms": 5520,
      "read_warm_ms": 1035,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 45,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "storage:parquet-encoding",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/70_last_checkpoint_schema_checksum",
      "num": 70,
      "name": "last_checkpoint_schema_checksum",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/70_last_checkpoint_schema_checksum.sql",
      "read_script": "generator/spark-reads-df/verify_70_last_checkpoint_schema_checksum.py",
      "description": "Demonstrates last checkpoint file schema with checksum validation.",
      "status": "pass",
      "duration_ms": 21853,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:40:29.466822+00:00",
      "read_cold_ms": 1959,
      "read_warm_ms": 3750,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 185,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/710_all_null_columns_dml",
      "num": 710,
      "name": "all_null_columns_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/710_all_null_columns_dml.sql",
      "read_script": "generator/spark-reads-df/verify_710_all_null_columns_dml.py",
      "description": "Table where some columns start as ALL NULL (no non-null values in any row), then DML populates them. Production pattern: pre-allocated schema where columns are added in advance but only populated later. Tests Parquet column statistics when min/max are both null, and correct...",
      "status": "pass",
      "duration_ms": 9452,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:40:38.925232+00:00",
      "read_cold_ms": 5194,
      "read_warm_ms": 543,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 68,
      "write_warm_ms": 48,
      "tags": [
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "storage:statistics",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/711_single_column_table",
      "num": 711,
      "name": "single_column_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/711_single_column_table.sql",
      "read_script": "generator/spark-reads-df/verify_711_single_column_table.py",
      "description": "Minimal table with only a single column (id). Tests that the engine handles the minimum possible schema correctly for INSERT, DELETE, and Parquet file generation. Edge case: no non-key columns to update, statistics cover only one column.",
      "status": "pass",
      "duration_ms": 8114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:40:47.048529+00:00",
      "read_cold_ms": 2930,
      "read_warm_ms": 975,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 21,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/712_all_same_values",
      "num": 712,
      "name": "all_same_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/712_all_same_values.sql",
      "read_script": "generator/spark-reads-df/verify_712_all_same_values.py",
      "description": "Every row has identical values (except id). This creates a statistics edge case where min=max for all non-id columns. Tests that the engine correctly writes and reads Parquet statistics when there is zero variance, and that DML operations work when predicate evaluation sees...",
      "status": "pass",
      "duration_ms": 7109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:40:54.160038+00:00",
      "read_cold_ms": 2947,
      "read_warm_ms": 513,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 42,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/713_negative_values",
      "num": 713,
      "name": "negative_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/713_negative_values.sql",
      "read_script": "generator/spark-reads-df/verify_713_negative_values.py",
      "description": "All numeric values are negative. Tests sign handling through DML operations, Parquet min/max statistics with negative ranges, and predicate evaluation with negative comparisons. Production pattern: financial systems with debit/loss columns, temperature readings below zero.",
      "status": "pass",
      "duration_ms": 8649,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:02.810688+00:00",
      "read_cold_ms": 3741,
      "read_warm_ms": 3853,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 69,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/714_zero_values",
      "num": 714,
      "name": "zero_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/714_zero_values.sql",
      "read_script": "generator/spark-reads-df/verify_714_zero_values.py",
      "description": "All numeric values start as zero, then DML changes some. Tests zero-value handling, Parquet statistics where min=max=0 initially, and correct transition from all-zero to mixed values. Edge case for engines that may confuse zero with NULL or skip zero-value encoding.",
      "status": "pass",
      "duration_ms": 7426,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:10.237848+00:00",
      "read_cold_ms": 3255,
      "read_warm_ms": 2973,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 119,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/715_update_same_value",
      "num": 715,
      "name": "update_same_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/715_update_same_value.sql",
      "read_script": "generator/spark-reads-df/verify_715_update_same_value.py",
      "description": "UPDATE SET col=col (no actual change to the value). Tests that the engine handles no-op value assignment correctly. The UPDATE must still create a new transaction version even though the data is unchanged. Production pattern: conditional updates where the SET clause does not...",
      "status": "pass",
      "duration_ms": 8101,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:18.339933+00:00",
      "read_cold_ms": 2472,
      "read_warm_ms": 3834,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 87,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/716_merge_all_delete",
      "num": 716,
      "name": "merge_all_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/716_merge_all_delete.sql",
      "read_script": "generator/spark-reads-df/verify_716_merge_all_delete.py",
      "description": "MERGE where every matched row hits the DELETE clause (no UPDATE, no NOT MATCHED INSERT in the MERGE itself). Tests the engine's handling of MERGE-as-delete, which is a production pattern for deduplication or purge operations driven by a \"delete list\" table.",
      "status": "pass",
      "duration_ms": 8264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:26.606332+00:00",
      "read_cold_ms": 5681,
      "read_warm_ms": 1373,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 73,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/717_merge_source_duplicates",
      "num": 717,
      "name": "merge_source_duplicates",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/717_merge_source_duplicates.sql",
      "read_script": "generator/spark-reads-df/verify_717_merge_source_duplicates.py",
      "description": "MERGE where the source CTE deduplicates via GROUP BY before merging. Production pattern: source data has duplicate keys (e.g., multiple events for the same entity) and must be deduplicated before upsert. This is the safe production pattern vs. letting the engine encounter...",
      "status": "pass",
      "duration_ms": 6068,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:32.675858+00:00",
      "read_cold_ms": 4310,
      "read_warm_ms": 1023,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 70,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/718_incremental_schema_migration",
      "num": 718,
      "name": "incremental_schema_migration",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/718_incremental_schema_migration.sql",
      "read_script": "generator/spark-reads-df/verify_718_incremental_schema_migration.py",
      "description": "Incremental schema migration: 4 schema versions over time. Production pattern for data pipeline evolution where new columns are added across releases. Each INSERT uses the schema available at that point. Earlier rows have NULL for later-added columns. Tests that the engine...",
      "status": "pass",
      "duration_ms": 5780,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:38.458011+00:00",
      "read_cold_ms": 4460,
      "read_warm_ms": 718,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 206,
      "write_warm_ms": 245,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/719_delete_to_empty_then_evolve",
      "num": 719,
      "name": "delete_to_empty_then_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/719_delete_to_empty_then_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_719_delete_to_empty_then_evolve.py",
      "description": "DELETE all rows (table becomes empty), then schema evolution (ADD COLUMN), then INSERT with the new schema. Tests that the engine correctly handles an empty table state, schema changes on empty tables, and subsequent inserts after the empty+evolve sequence. Production pattern...",
      "status": "pass",
      "duration_ms": 5489,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:43.949195+00:00",
      "read_cold_ms": 1801,
      "read_warm_ms": 347,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 152,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/71_parquet_type_mappings_complete",
      "num": 71,
      "name": "parquet_type_mappings_complete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/71_parquet_type_mappings_complete.sql",
      "read_script": "generator/spark-reads-df/verify_71_parquet_type_mappings_complete.py",
      "description": "Complete Delta to Parquet type mappings.",
      "status": "pass",
      "duration_ms": 9672,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:41:53.624080+00:00",
      "read_cold_ms": 2299,
      "read_warm_ms": 554,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 78,
      "tags": [
        "type:array",
        "type:binary",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/720_concurrent_style_dml",
      "num": 720,
      "name": "concurrent_style_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/720_concurrent_style_dml.sql",
      "read_script": "generator/spark-reads-df/verify_720_concurrent_style_dml.py",
      "description": "Simulates a concurrent-writer pattern: alternating INSERT and UPDATE on overlapping ranges. Common in multi-writer production setups where one writer appends new data while another updates existing data. Tests that the engine correctly handles interleaved DML that touches...",
      "status": "pass",
      "duration_ms": 11630,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:05.255773+00:00",
      "read_cold_ms": 6424,
      "read_warm_ms": 851,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
      "write_warm_ms": 107,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/721_long_string_values",
      "num": 721,
      "name": "long_string_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/721_long_string_values.sql",
      "read_script": "generator/spark-reads-df/verify_721_long_string_values.py",
      "description": "Very long string values (~200+ chars per row). Tests Parquet page handling, dictionary encoding thresholds, and string column statistics when values exceed typical inline sizes.",
      "status": "pass",
      "duration_ms": 3639,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:08.895897+00:00",
      "read_cold_ms": 2189,
      "read_warm_ms": 622,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 169,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "storage:parquet-encoding",
        "storage:statistics",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/722_empty_string_partition_dml",
      "num": 722,
      "name": "empty_string_partition_dml",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/722_empty_string_partition_dml.sql",
      "read_script": "generator/spark-reads-df/verify_722_empty_string_partition_dml.py",
      "description": "Empty string '' as a partition value combined with DML operations. Tests that the engine correctly handles empty-string partition values through UPDATE and DELETE, including predicates that target the '' partition specifically. Different from test 425 which tests empty strings...",
      "status": "pass",
      "duration_ms": 7867,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:16.764202+00:00",
      "read_cold_ms": 5837,
      "read_warm_ms": 897,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 245,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:partition-spec",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/723_null_in_all_predicates",
      "num": 723,
      "name": "null_in_all_predicates",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/723_null_in_all_predicates.sql",
      "read_script": "generator/spark-reads-df/verify_723_null_in_all_predicates.py",
      "description": "DML operations using IS NULL and IS NOT NULL in every predicate. Tests null-aware predicate evaluation chains where the engine must correctly handle three-valued logic across DELETE and UPDATE operations on columns with mixed NULL/non-NULL values.",
      "status": "pass",
      "duration_ms": 7519,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:24.285560+00:00",
      "read_cold_ms": 5443,
      "read_warm_ms": 724,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 215,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/724_update_to_null",
      "num": 724,
      "name": "update_to_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/724_update_to_null.sql",
      "read_script": "generator/spark-reads-df/verify_724_update_to_null.py",
      "description": "UPDATE that introduces NULLs into previously non-NULL columns. Tests that the engine correctly writes NULL values through UPDATE when the original data had no NULLs. This is a common production pattern when \"clearing\" fields or soft-resetting data.",
      "status": "pass",
      "duration_ms": 8647,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:32.934751+00:00",
      "read_cold_ms": 3250,
      "read_warm_ms": 4435,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 96,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/725_delete_leaves_one_row",
      "num": 725,
      "name": "delete_leaves_one_row",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/725_delete_leaves_one_row.sql",
      "read_script": "generator/spark-reads-df/verify_725_delete_leaves_one_row.py",
      "description": "DELETE that leaves exactly 1 row in the table. Tests statistics and metadata at minimum cardinality (single-row table after bulk delete). The engine must correctly produce min/max stats, row counts, and deletion vectors when nearly all rows are removed.",
      "status": "pass",
      "duration_ms": 7724,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:40.660227+00:00",
      "read_cold_ms": 2430,
      "read_warm_ms": 3862,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 43,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/726_delete_leaves_zero_rows",
      "num": 726,
      "name": "delete_leaves_zero_rows",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/726_delete_leaves_zero_rows.sql",
      "read_script": "generator/spark-reads-df/verify_726_delete_leaves_zero_rows.py",
      "description": "DELETE all rows without reinserting, leaving a completely empty table. Tests empty table state: zero-row Parquet files, statistics on empty data, and correct handling of a table that has data files but all rows are logically deleted via deletion vectors.",
      "status": "pass",
      "duration_ms": 8966,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:49.631774+00:00",
      "read_cold_ms": 2575,
      "read_warm_ms": 370,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/727_merge_doubles_table",
      "num": 727,
      "name": "merge_doubles_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/727_merge_doubles_table.sql",
      "read_script": "generator/spark-reads-df/verify_727_merge_doubles_table.py",
      "description": "MERGE that doubles table size because all source rows are NOT MATCHED. Tests the pure-insert path of MERGE when no rows in the source match the target. The engine must correctly handle a MERGE where the MATCHED clause is never triggered and all rows flow through NOT MATCHED...",
      "status": "pass",
      "duration_ms": 2697,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:42:52.330510+00:00",
      "read_cold_ms": 1608,
      "read_warm_ms": 296,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 45,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/728_merge_halves_table",
      "num": 728,
      "name": "merge_halves_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/728_merge_halves_table.sql",
      "read_script": "generator/spark-reads-df/verify_728_merge_halves_table.py",
      "description": "MERGE with a DELETE clause that halves the table. Tests the WHEN MATCHED AND <condition> THEN DELETE path where half the matched rows are deleted and the other half are updated. The engine must handle conditional branching within MATCHED clauses correctly.",
      "status": "pass",
      "duration_ms": 7864,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:00.196044+00:00",
      "read_cold_ms": 5772,
      "read_warm_ms": 1176,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 58,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/729_update_swap_columns",
      "num": 729,
      "name": "update_swap_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/729_update_swap_columns.sql",
      "read_script": "generator/spark-reads-df/verify_729_update_swap_columns.py",
      "description": "UPDATE SET a=b, b=a (column swap). Tests that the engine evaluates SET expressions from the pre-update row snapshot per SQL standard. If the engine incorrectly evaluates SET sequentially (a=b first, then b=a uses the new a), both columns would end up with the same value. Correct...",
      "status": "pass",
      "duration_ms": 7125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:07.324608+00:00",
      "read_cold_ms": 2492,
      "read_warm_ms": 895,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/72_schema_serialization_complete_example",
      "num": 72,
      "name": "schema_serialization_complete_example",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/72_schema_serialization_complete_example.sql",
      "read_script": "generator/spark-reads-df/verify_72_schema_serialization_complete_example.py",
      "description": "Demonstrates complete schema serialization example from the Delta protocol specification. This creates the exact example schema from protocol.md: |-- a: integer (nullable = true) |-- b: struct (nullable = true) | |-- d: integer (nullable = true)",
      "status": "pass",
      "duration_ms": 12362,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:19.691820+00:00",
      "read_cold_ms": 1654,
      "read_warm_ms": 456,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 55,
      "tags": [
        "type:array",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/730_timestamp_epoch_boundary",
      "num": 730,
      "name": "timestamp_epoch_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/730_timestamp_epoch_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_730_timestamp_epoch_boundary.py",
      "description": "Timestamps at epoch (1970-01-01), near the 2038 boundary, and in the far future. Tests timestamp boundary handling in Parquet encoding, Delta statistics, and predicate evaluation. These are production edge cases that surface in ETL pipelines processing historical or far-future...",
      "status": "pass",
      "duration_ms": 3957,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:23.650058+00:00",
      "read_cold_ms": 2686,
      "read_warm_ms": 507,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 145,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "storage:parquet-encoding",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/731_decimal_max_precision",
      "num": 731,
      "name": "decimal_max_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/731_decimal_max_precision.sql",
      "read_script": "generator/spark-reads-df/verify_731_decimal_max_precision.py",
      "description": "DECIMAL(38,0) and DECIMAL(38,18) at maximum precision limits. Tests that the engine handles the largest Decimal128 values without precision loss through INSERT, UPDATE, and DELETE. These edge cases surface in financial and scientific data pipelines.",
      "status": "pass",
      "duration_ms": 6992,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:30.643711+00:00",
      "read_cold_ms": 5760,
      "read_warm_ms": 564,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 97,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/732_partition_many_values",
      "num": 732,
      "name": "partition_many_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/732_partition_many_values.sql",
      "read_script": "generator/spark-reads-df/verify_732_partition_many_values.py",
      "description": "50 distinct partition values (high-cardinality partitioning). Tests that the engine handles many partitions correctly: directory layout, per-partition statistics, and targeted DELETE across specific partition subsets. This is a common production pattern with date or bucket...",
      "status": "pass",
      "duration_ms": 6861,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:37.506003+00:00",
      "read_cold_ms": 5378,
      "read_warm_ms": 606,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 501,
      "write_warm_ms": 450,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/733_many_small_deletes",
      "num": 733,
      "name": "many_small_deletes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/733_many_small_deletes.sql",
      "read_script": "generator/spark-reads-df/verify_733_many_small_deletes.py",
      "description": "20 sequential small DELETEs (1 row each). Tests deletion vector accumulation from many individual operations. Each DELETE creates a new DV entry, and the engine must correctly compose them. This pattern occurs in production when individual record deletions (e.g., GDPR...",
      "status": "pass",
      "duration_ms": 8868,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:46.375588+00:00",
      "read_cold_ms": 3581,
      "read_warm_ms": 3658,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 611,
      "write_warm_ms": 735,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/734_many_small_updates",
      "num": 734,
      "name": "many_small_updates",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/734_many_small_updates.sql",
      "read_script": "generator/spark-reads-df/verify_734_many_small_updates.py",
      "description": "20 sequential small UPDATEs (1 row each). Tests deletion vector stacking from individual UPDATE operations, where each UPDATE rewrites one row (delete old + add new). The engine must correctly stack 20 DV entries from update-style rewrites.",
      "status": "pass",
      "duration_ms": 7060,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:43:53.437479+00:00",
      "read_cold_ms": 5162,
      "read_warm_ms": 1139,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1350,
      "write_warm_ms": 1168,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/735_merge_then_merge_then_delete",
      "num": 735,
      "name": "merge_then_merge_then_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/735_merge_then_merge_then_delete.sql",
      "read_script": "generator/spark-reads-df/verify_735_merge_then_merge_then_delete.py",
      "description": "Production ETL pattern: initial load, two reconciliation MERGEs, then a cleanup DELETE. Tests multi-step DML pipelines where MERGE operations stack on top of each other and a final DELETE prunes obsolete records. This is the standard load-reconcile-cleanup cycle.",
      "status": "pass",
      "duration_ms": 8417,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:01.861406+00:00",
      "read_cold_ms": 6817,
      "read_warm_ms": 673,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 180,
      "write_warm_ms": 182,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/736_backfill_pattern",
      "num": 736,
      "name": "backfill_pattern",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/736_backfill_pattern.sql",
      "read_script": "generator/spark-reads-df/verify_736_backfill_pattern.py",
      "description": "Production backfill: old data inserted after newer data already exists, then reconciled via MERGE. Tests that the engine handles out-of-order inserts followed by a normalizing MERGE. This pattern occurs when historical data is backfilled into a table that already has current...",
      "status": "pass",
      "duration_ms": 4512,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:06.374583+00:00",
      "read_cold_ms": 2344,
      "read_warm_ms": 1006,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 140,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/737_dedup_after_insert",
      "num": 737,
      "name": "dedup_after_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/737_dedup_after_insert.sql",
      "read_script": "generator/spark-reads-df/verify_737_dedup_after_insert.py",
      "description": "Production dedup: INSERT creates duplicates, then DELETE removes the older generation. Tests deduplication via generational markers, a common ETL pattern where duplicate records are cleaned up after a reload or retry.",
      "status": "pass",
      "duration_ms": 8854,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:15.230324+00:00",
      "read_cold_ms": 6750,
      "read_warm_ms": 1184,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/738_partition_rebalance",
      "num": 738,
      "name": "partition_rebalance",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/738_partition_rebalance.sql",
      "read_script": "generator/spark-reads-df/verify_738_partition_rebalance.py",
      "description": "Move data between partitions to rebalance. Production pattern where UPDATE changes partition column values, causing rows to migrate across partitions. Tests cross-partition row movement through multiple UPDATE operations that redistribute data.",
      "status": "pass",
      "duration_ms": 11685,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:26.916815+00:00",
      "read_cold_ms": 6872,
      "read_warm_ms": 4007,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 82,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/739_cdc_production_etl",
      "num": 739,
      "name": "cdc_production_etl",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/739_cdc_production_etl.sql",
      "read_script": "generator/spark-reads-df/verify_739_cdc_production_etl.py",
      "description": "CDC-enabled table through a realistic ETL cycle: load, transform, correct, add late-arriving data, and optimize. Tests that Change Data Capture metadata (_change_type, _commit_version, _commit_timestamp) is correctly maintained through multiple DML operations on a CDF- enabled...",
      "status": "pass",
      "duration_ms": 7887,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:34.806075+00:00",
      "read_cold_ms": 5211,
      "read_warm_ms": 1029,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 222,
      "write_warm_ms": 152,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/73_schema_serialization_complete_example_v2",
      "num": 73,
      "name": "schema_serialization_complete_example_v2",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/73_schema_serialization_complete_example_v2.sql",
      "read_script": "generator/spark-reads-df/verify_73_schema_serialization_complete_example_v2.py",
      "description": "Validates the simplified v2 of the schema serialization example. 2 sensor records (1001, 1002) with 5 columns: a, b, c, e, f. This is a minimal test with reduced data.",
      "status": "pass",
      "duration_ms": 8265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:43.073636+00:00",
      "read_cold_ms": 5355,
      "read_warm_ms": 645,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 49,
      "tags": [
        "type:array",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/740_evolve_production_migration",
      "num": 740,
      "name": "evolve_production_migration",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/740_evolve_production_migration.sql",
      "read_script": "generator/spark-reads-df/verify_740_evolve_production_migration.py",
      "description": "Schema migration pattern: add columns, backfill them, then continue normal DML. Tests schema evolution through ALTER TABLE ADD COLUMN followed by UPDATE to backfill existing rows. This is the standard production migration where new columns are added and populated retroactively.",
      "status": "pass",
      "duration_ms": 7601,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:50.677495+00:00",
      "read_cold_ms": 5566,
      "read_warm_ms": 907,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 242,
      "write_warm_ms": 242,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/741_merge_not_matched_by_source_delete",
      "num": 741,
      "name": "merge_not_matched_by_source_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/741_merge_not_matched_by_source_delete.sql",
      "read_script": "generator/spark-reads-df/verify_741_merge_not_matched_by_source_delete.py",
      "description": "MERGE with WHEN NOT MATCHED BY SOURCE THEN DELETE. Verifies that target rows with no matching source row are deleted by the NOT MATCHED BY SOURCE clause, while matched rows are updated normally.",
      "status": "pass",
      "duration_ms": 4300,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:44:54.979215+00:00",
      "read_cold_ms": 2568,
      "read_warm_ms": 656,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 182,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/742_merge_not_matched_by_source_update",
      "num": 742,
      "name": "merge_not_matched_by_source_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/742_merge_not_matched_by_source_update.sql",
      "read_script": "generator/spark-reads-df/verify_742_merge_not_matched_by_source_update.py",
      "description": "MERGE with WHEN NOT MATCHED BY SOURCE THEN UPDATE. Marks unmatched target rows as 'orphaned' instead of deleting them.",
      "status": "pass",
      "duration_ms": 7703,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:02.684559+00:00",
      "read_cold_ms": 2926,
      "read_warm_ms": 3864,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/743_merge_all_four_clauses",
      "num": 743,
      "name": "merge_all_four_clauses",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/743_merge_all_four_clauses.sql",
      "read_script": "generator/spark-reads-df/verify_743_merge_all_four_clauses.py",
      "description": "MERGE with all four clause types: MATCHED UPDATE, MATCHED DELETE, NOT MATCHED INSERT, and NOT MATCHED BY SOURCE DELETE. This exercises the full MERGE capability in a single statement.",
      "status": "pass",
      "duration_ms": 4180,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:06.866947+00:00",
      "read_cold_ms": 2611,
      "read_warm_ms": 821,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 73,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/744_merge_not_matched_by_source_conditional",
      "num": 744,
      "name": "merge_not_matched_by_source_conditional",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/744_merge_not_matched_by_source_conditional.sql",
      "read_script": "generator/spark-reads-df/verify_744_merge_not_matched_by_source_conditional.py",
      "description": "MERGE with conditional WHEN NOT MATCHED BY SOURCE. Low-score orphan rows are deleted; remaining orphan rows are deactivated. Tests that conditions on the NOT MATCHED BY SOURCE clause filter correctly.",
      "status": "pass",
      "duration_ms": 6177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:13.045434+00:00",
      "read_cold_ms": 4598,
      "read_warm_ms": 845,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 88,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/745_merge_nmbys_partition",
      "num": 745,
      "name": "merge_nmbys_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/745_merge_nmbys_partition.sql",
      "read_script": "generator/spark-reads-df/verify_745_merge_nmbys_partition.py",
      "description": "WHEN NOT MATCHED BY SOURCE on a partitioned table. Tests that the NM-BY-SOURCE clause correctly deletes rows across multiple partitions.",
      "status": "pass",
      "duration_ms": 4506,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:17.552055+00:00",
      "read_cold_ms": 3499,
      "read_warm_ms": 555,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 216,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/746_merge_nmbys_cdc",
      "num": 746,
      "name": "merge_nmbys_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/746_merge_nmbys_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_746_merge_nmbys_cdc.py",
      "description": "WHEN NOT MATCHED BY SOURCE + CDC enabled. Tests that Change Data Feed correctly captures NM-BY-SOURCE delete events alongside matched updates.",
      "status": "pass",
      "duration_ms": 7316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:24.868670+00:00",
      "read_cold_ms": 2150,
      "read_warm_ms": 3606,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 192,
      "write_warm_ms": 334,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/747_constraint_violation_insert",
      "num": 747,
      "name": "constraint_violation_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/747_constraint_violation_insert.sql",
      "read_script": "generator/spark-reads-df/verify_747_constraint_violation_insert.py",
      "description": "Constraint lifecycle: ADD CONSTRAINT, insert valid data, DROP CONSTRAINT, then insert data that would have violated the old constraint. Verifies that the dropped constraint no longer blocks inserts.",
      "status": "pass",
      "duration_ms": 858,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:09.944585+00:00",
      "read_cold_ms": 541,
      "read_warm_ms": 157,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 227,
      "write_warm_ms": 248,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/748_constraint_violation_update",
      "num": 748,
      "name": "constraint_violation_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/748_constraint_violation_update.sql",
      "read_script": "generator/spark-reads-df/verify_748_constraint_violation_update.py",
      "description": "Constraint DROP then UPDATE to previously-violating values. Verifies that a dropped constraint no longer blocks UPDATE operations that would have produced invalid data.",
      "status": "pass",
      "duration_ms": 6629,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:35.074214+00:00",
      "read_cold_ms": 5215,
      "read_warm_ms": 745,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 122,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/749_constraint_violation_merge",
      "num": 749,
      "name": "constraint_violation_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/749_constraint_violation_merge.sql",
      "read_script": "generator/spark-reads-df/verify_749_constraint_violation_merge.py",
      "description": "Constraint DROP then MERGE with data that would have violated the old constraint. Verifies that MERGE can insert violating rows after the constraint is removed.",
      "status": "pass",
      "duration_ms": 6391,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:45:41.466795+00:00",
      "read_cold_ms": 4806,
      "read_warm_ms": 538,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 245,
      "write_warm_ms": 131,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/74_type_widening_safe_column_promotion",
      "num": 74,
      "name": "type_widening_safe_column_promotion",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/74_type_widening_safe_column_promotion.sql",
      "read_script": "generator/spark-reads-df/verify_74_type_widening_safe_column_promotion.py",
      "description": "Type widening for safe column type promotion.",
      "status": "pass",
      "duration_ms": 30571,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:46:12.039697+00:00",
      "read_cold_ms": 3950,
      "read_warm_ms": 623,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 257,
      "write_warm_ms": 126,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "schema:type-widening",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/750_rename_multiple_columns",
      "num": 750,
      "name": "rename_multiple_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/750_rename_multiple_columns.sql",
      "read_script": "generator/spark-reads-df/verify_750_rename_multiple_columns.py",
      "description": "Renaming 3 columns in sequence with column mapping (name mode). Tests multiple column mapping mutations followed by DML that uses the new column names.",
      "status": "pass",
      "duration_ms": 6915,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:46:18.956045+00:00",
      "read_cold_ms": 4888,
      "read_warm_ms": 1077,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 160,
      "write_warm_ms": 69,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/751_drop_multiple_columns",
      "num": 751,
      "name": "drop_multiple_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/751_drop_multiple_columns.sql",
      "read_script": "generator/spark-reads-df/verify_751_drop_multiple_columns.py",
      "description": "Dropping 2 columns in sequence with column mapping. Tests that multiple column drops are correctly tracked and that subsequent DML operates on the reduced schema.",
      "status": "pass",
      "duration_ms": 8957,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:46:27.914920+00:00",
      "read_cold_ms": 6587,
      "read_warm_ms": 1051,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 106,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/752_rename_then_drop",
      "num": 752,
      "name": "rename_then_drop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/752_rename_then_drop.sql",
      "read_script": "generator/spark-reads-df/verify_752_rename_then_drop.py",
      "description": "RENAME one column then DROP another, followed by DML and MERGE. Tests stacked column mapping mutations with subsequent complex DML.",
      "status": "pass",
      "duration_ms": 8778,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:46:36.695774+00:00",
      "read_cold_ms": 3550,
      "read_warm_ms": 3796,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 193,
      "write_warm_ms": 93,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/753_drop_then_rename",
      "num": 753,
      "name": "drop_then_rename",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/753_drop_then_rename.sql",
      "read_script": "generator/spark-reads-df/verify_753_drop_then_rename.py",
      "description": "DROP a column then RENAME another. Reverse order from test 752. Tests that column mapping tracks drops and renames correctly regardless of operation order.",
      "status": "pass",
      "duration_ms": 7786,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:46:44.483699+00:00",
      "read_cold_ms": 3306,
      "read_warm_ms": 3672,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 78,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/754_rename_drop_add",
      "num": 754,
      "name": "rename_drop_add",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/754_rename_drop_add.sql",
      "read_script": "generator/spark-reads-df/verify_754_rename_drop_add.py",
      "description": "RENAME + DROP + ADD COLUMN in sequence. Tests the full column mutation lifecycle: rename an existing column, drop another, then add a new one.",
      "status": "pass",
      "duration_ms": 10447,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:46:54.932445+00:00",
      "read_cold_ms": 4213,
      "read_warm_ms": 4429,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 196,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/755_cdc_exact_counts",
      "num": 755,
      "name": "cdc_exact_counts",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/755_cdc_exact_counts.sql",
      "read_script": "generator/spark-reads-df/verify_755_cdc_exact_counts.py",
      "description": "CDC test designed for exact CDF row count verification. Each DML version produces a precise, predictable number of Change Data Feed rows, enabling verify scripts to assert exact CDF counts per change type.",
      "status": "pass",
      "duration_ms": 5556,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:47:00.489002+00:00",
      "read_cold_ms": 3181,
      "read_warm_ms": 825,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 68,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/756_cdc_merge_exact_counts",
      "num": 756,
      "name": "cdc_merge_exact_counts",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/756_cdc_merge_exact_counts.sql",
      "read_script": "generator/spark-reads-df/verify_756_cdc_merge_exact_counts.py",
      "description": "CDC + MERGE with exact CDF count verification. The MERGE produces a precise mix of matched updates and not-matched inserts, enabling exact CDF row count assertions.",
      "status": "pass",
      "duration_ms": 8682,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:47:09.172413+00:00",
      "read_cold_ms": 2535,
      "read_warm_ms": 4273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/757_cdc_nmbys_exact_counts",
      "num": 757,
      "name": "cdc_nmbys_exact_counts",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/757_cdc_nmbys_exact_counts.sql",
      "read_script": "generator/spark-reads-df/verify_757_cdc_nmbys_exact_counts.py",
      "description": "CDC + WHEN NOT MATCHED BY SOURCE with exact CDF counts. Tests that Change Data Feed correctly captures NM-BY-SOURCE delete events with precise row counts.",
      "status": "pass",
      "duration_ms": 7704,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:47:16.877672+00:00",
      "read_cold_ms": 2073,
      "read_warm_ms": 4498,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 149,
      "write_warm_ms": 101,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/758_colmap_rename_merge_cdc",
      "num": 758,
      "name": "colmap_rename_merge_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/758_colmap_rename_merge_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_758_colmap_rename_merge_cdc.py",
      "description": "Column mapping + RENAME COLUMN + MERGE + CDC. Tests that Change Data Feed uses the renamed column name (not the original) in CDF output.",
      "status": "pass",
      "duration_ms": 8014,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:47:24.893527+00:00",
      "read_cold_ms": 2565,
      "read_warm_ms": 4417,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 417,
      "write_warm_ms": 287,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/759_colmap_drop_evolve_merge",
      "num": 759,
      "name": "colmap_drop_evolve_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/759_colmap_drop_evolve_merge.sql",
      "read_script": "generator/spark-reads-df/verify_759_colmap_drop_evolve_merge.py",
      "description": "Column mapping + DROP COLUMN + ADD COLUMN + MERGE. Tests MERGE across combined column mutation (drop) and schema evolution (add).",
      "status": "pass",
      "duration_ms": 6781,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:47:31.676086+00:00",
      "read_cold_ms": 2580,
      "read_warm_ms": 3358,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 52,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/75_coordinated_commits_managed_transactions",
      "num": 75,
      "name": "coordinated_commits_managed_transactions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/75_coordinated_commits_managed_transactions.sql",
      "read_script": "generator/spark-reads-df/verify_75_coordinated_commits_managed_transactions.py",
      "description": "Coordinated commits via external commit coordinator.",
      "status": "pass",
      "duration_ms": 29614,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:01.296295+00:00",
      "read_cold_ms": 2495,
      "read_warm_ms": 310,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 85,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/760_merge_nmbys_evolve_cdc",
      "num": 760,
      "name": "merge_nmbys_evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/760_merge_nmbys_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_760_merge_nmbys_evolve_cdc.py",
      "description": "MERGE with NOT MATCHED BY SOURCE + schema evolution + CDC. Complex combination test: add a new column, then use MERGE with NM-BY-SOURCE to populate it differently for matched vs unmatched rows.",
      "status": "pass",
      "duration_ms": 11644,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:12.942027+00:00",
      "read_cold_ms": 6701,
      "read_warm_ms": 745,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 327,
      "write_warm_ms": 375,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/761_merge_nmbys_update_delete",
      "num": 761,
      "name": "merge_nmbys_update_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/761_merge_nmbys_update_delete.sql",
      "read_script": "generator/spark-reads-df/verify_761_merge_nmbys_update_delete.py",
      "description": "MERGE with WHEN NOT MATCHED BY SOURCE having both UPDATE and DELETE conditions. Tests that the engine correctly branches NM-BY-SOURCE rows into UPDATE or DELETE based on conditional predicates.",
      "status": "pass",
      "duration_ms": 5318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:18.262082+00:00",
      "read_cold_ms": 3635,
      "read_warm_ms": 760,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 142,
      "write_warm_ms": 223,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/762_merge_nmbys_colmap",
      "num": 762,
      "name": "merge_nmbys_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/762_merge_nmbys_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_762_merge_nmbys_colmap.py",
      "description": "NM-BY-SOURCE + column mapping (name mode). Tests that WHEN NOT MATCHED BY SOURCE DELETE works correctly when column mapping is enabled, ensuring physical/logical name indirection does not break source row identification.",
      "status": "pass",
      "duration_ms": 8069,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:26.332346+00:00",
      "read_cold_ms": 6503,
      "read_warm_ms": 659,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 134,
      "write_warm_ms": 120,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/763_merge_nmbys_constraint",
      "num": 763,
      "name": "merge_nmbys_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/763_merge_nmbys_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_763_merge_nmbys_constraint.py",
      "description": "NM-BY-SOURCE + CHECK constraint. Tests that the WHEN NOT MATCHED BY SOURCE UPDATE clause respects active CHECK constraints. The NM-BY-SOURCE UPDATE sets score=0, which must satisfy the constraint score>=0.",
      "status": "pass",
      "duration_ms": 8352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:34.686094+00:00",
      "read_cold_ms": 6326,
      "read_warm_ms": 892,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 106,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/764_merge_nmbys_optimize",
      "num": 764,
      "name": "merge_nmbys_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/764_merge_nmbys_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_764_merge_nmbys_optimize.py",
      "description": "NM-BY-SOURCE MERGE after OPTIMIZE. Tests that MERGE correctly identifies not-matched-by-source rows when operating on compacted Parquet files (post-OPTIMIZE), which changes the physical file layout.",
      "status": "pass",
      "duration_ms": 8600,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:43.290770+00:00",
      "read_cold_ms": 2984,
      "read_warm_ms": 4368,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 443,
      "write_warm_ms": 229,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/765_constraint_add_valid_drop_violate_add",
      "num": 765,
      "name": "constraint_add_valid_drop_violate_add",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/765_constraint_add_valid_drop_violate_add.sql",
      "read_script": "generator/spark-reads-df/verify_765_constraint_add_valid_drop_violate_add.py",
      "description": "Constraint lifecycle: add -> valid data -> drop -> violating data -> re-add a different constraint. Tests that dropping a constraint truly removes enforcement, and that a new constraint only validates current data.",
      "status": "pass",
      "duration_ms": 5719,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:49.012030+00:00",
      "read_cold_ms": 2572,
      "read_warm_ms": 271,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 212,
      "write_warm_ms": 197,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/766_constraint_multiple_add_drop",
      "num": 766,
      "name": "constraint_multiple_add_drop",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/766_constraint_multiple_add_drop.sql",
      "read_script": "generator/spark-reads-df/verify_766_constraint_multiple_add_drop.py",
      "description": "Complex constraint metadata evolution: add 3 constraints, drop 2, add 1 new. Tests that the metadata correctly tracks which constraints are active after multiple add/drop operations, and that new data only needs to satisfy the remaining active constraints.",
      "status": "pass",
      "duration_ms": 3462,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:52.475083+00:00",
      "read_cold_ms": 2229,
      "read_warm_ms": 532,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/767_cdc_delete_exact",
      "num": 767,
      "name": "cdc_delete_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/767_cdc_delete_exact.sql",
      "read_script": "generator/spark-reads-df/verify_767_cdc_delete_exact.py",
      "description": "CDC with exactly N deletes. Verifies that the exact delete count appears in the Change Data Feed output. Uses explicit id list for precise control over which rows are deleted.",
      "status": "pass",
      "duration_ms": 5071,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:48:57.547296+00:00",
      "read_cold_ms": 2521,
      "read_warm_ms": 982,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 27,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/768_cdc_update_exact",
      "num": 768,
      "name": "cdc_update_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/768_cdc_update_exact.sql",
      "read_script": "generator/spark-reads-df/verify_768_cdc_update_exact.py",
      "description": "CDC with exactly N updates. Verifies that the exact update preimage and postimage counts appear in the Change Data Feed output.",
      "status": "pass",
      "duration_ms": 8837,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:49:06.385259+00:00",
      "read_cold_ms": 6822,
      "read_warm_ms": 1044,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 104,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/769_cdc_mixed_exact",
      "num": 769,
      "name": "cdc_mixed_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/769_cdc_mixed_exact.sql",
      "read_script": "generator/spark-reads-df/verify_769_cdc_mixed_exact.py",
      "description": "CDC with mixed DML operations, each producing an exact predictable number of CDF rows. Tests INSERT + INSERT + UPDATE + DELETE in sequence, verifying every CDF change type count is precise.",
      "status": "pass",
      "duration_ms": 7039,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:49:13.425636+00:00",
      "read_cold_ms": 5084,
      "read_warm_ms": 832,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 76,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/76_coordinated_commits_managed_transactions_simple",
      "num": 76,
      "name": "coordinated_commits_managed_transactions_simple",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/76_coordinated_commits_managed_transactions_simple.sql",
      "read_script": "generator/spark-reads-df/verify_76_coordinated_commits_managed_transactions_simple.py",
      "description": "Proof 75 (simple): coordinated_commits_managed_transactions_simple -- Delta Data Verification (PySpark)",
      "status": "pass",
      "duration_ms": 26376,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:49:39.804085+00:00",
      "read_cold_ms": 1452,
      "read_warm_ms": 3642,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 143,
      "write_warm_ms": 43,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/770_rename_nmbys_merge",
      "num": 770,
      "name": "rename_nmbys_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/770_rename_nmbys_merge.sql",
      "read_script": "generator/spark-reads-df/verify_770_rename_nmbys_merge.py",
      "description": "RENAME COLUMN + NM-BY-SOURCE MERGE under column mapping. Tests that NM-BY-SOURCE correctly uses the renamed column name after ALTER TABLE RENAME COLUMN, ensuring physical/logical name indirection works.",
      "status": "pass",
      "duration_ms": 4508,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:49:44.314566+00:00",
      "read_cold_ms": 2855,
      "read_warm_ms": 794,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 71,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/771_drop_nmbys_merge",
      "num": 771,
      "name": "drop_nmbys_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/771_drop_nmbys_merge.sql",
      "read_script": "generator/spark-reads-df/verify_771_drop_nmbys_merge.py",
      "description": "DROP COLUMN + NM-BY-SOURCE MERGE under column mapping. Tests that NM-BY-SOURCE correctly operates after a column has been dropped, ensuring the remaining columns are correctly matched and written.",
      "status": "pass",
      "duration_ms": 8426,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:49:52.743630+00:00",
      "read_cold_ms": 6109,
      "read_warm_ms": 1253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 111,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/772_evolve_nmbys_merge",
      "num": 772,
      "name": "evolve_nmbys_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/772_evolve_nmbys_merge.sql",
      "read_script": "generator/spark-reads-df/verify_772_evolve_nmbys_merge.py",
      "description": "ADD COLUMN + NM-BY-SOURCE MERGE. Tests that NM-BY-SOURCE correctly handles rows that were written before the column was added (those rows have NULL for the new column), and that the MERGE UPDATE populates the new column for matched rows.",
      "status": "pass",
      "duration_ms": 7059,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:49:59.804567+00:00",
      "read_cold_ms": 5125,
      "read_warm_ms": 1075,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 90,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/773_partition_nmbys_cdc",
      "num": 773,
      "name": "partition_nmbys_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/773_partition_nmbys_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_773_partition_nmbys_cdc.py",
      "description": "Partition + NM-BY-SOURCE + CDC. Three-way feature combination. Tests that NM-BY-SOURCE DELETE correctly generates CDF delete events across multiple partitions, and that partition pruning does not skip unmatched source rows.",
      "status": "pass",
      "duration_ms": 7352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:07.158347+00:00",
      "read_cold_ms": 5270,
      "read_warm_ms": 754,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 349,
      "write_warm_ms": 199,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/774_constraint_nmbys_merge",
      "num": 774,
      "name": "constraint_nmbys_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/774_constraint_nmbys_merge.sql",
      "read_script": "generator/spark-reads-df/verify_774_constraint_nmbys_merge.py",
      "description": "Constraint + NM-BY-SOURCE MERGE. Tests that NM-BY-SOURCE UPDATE must respect active CHECK constraints. The NM-BY-SOURCE UPDATE sets value=1.0, which satisfies the constraint value > 0.",
      "status": "pass",
      "duration_ms": 7492,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:14.652820+00:00",
      "read_cold_ms": 5728,
      "read_warm_ms": 890,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 42,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/775_cdc_partition_nmbys",
      "num": 775,
      "name": "cdc_partition_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/775_cdc_partition_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_775_cdc_partition_nmbys.py",
      "description": "CDC + partition + NM-BY-SOURCE UPDATE. Tests that CDF correctly records update_preimage and update_postimage events for NM-BY-SOURCE UPDATE across multiple partitions. Unlike 773 which deletes unmatched rows, this test updates them to status='stale'.",
      "status": "pass",
      "duration_ms": 8942,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:23.601429+00:00",
      "read_cold_ms": 6151,
      "read_warm_ms": 937,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 298,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/776_large_nmbys",
      "num": 776,
      "name": "large_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/776_large_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_776_large_nmbys.py",
      "description": "Large-scale NM-BY-SOURCE: 1000-row target, 200-row source. 800 rows deleted by NM-BY-SOURCE. Stress-tests the engine's ability to handle large NM-BY-SOURCE DELETE batches efficiently.",
      "status": "pass",
      "duration_ms": 4230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:27.832542+00:00",
      "read_cold_ms": 2589,
      "read_warm_ms": 913,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 41,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/777_constraint_enforcement_chain",
      "num": 777,
      "name": "constraint_enforcement_chain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/777_constraint_enforcement_chain.sql",
      "read_script": "generator/spark-reads-df/verify_777_constraint_enforcement_chain.py",
      "description": "Chain of DML operations all respecting a CHECK constraint. Tests that UPDATE, DELETE, MERGE, and a second UPDATE all enforce the constraint across multiple versions. Ensures constraint is validated on every write.",
      "status": "pass",
      "duration_ms": 11439,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:39.272457+00:00",
      "read_cold_ms": 6240,
      "read_warm_ms": 1035,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 83,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/778_colmap_rename_partition_cdc",
      "num": 778,
      "name": "colmap_rename_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/778_colmap_rename_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_778_colmap_rename_partition_cdc.py",
      "description": "Column mapping + RENAME COLUMN + partition + CDC. Four-way feature combination with rename. Tests that CDF events use the renamed column name, and that partition-aware operations still work after rename under column mapping.",
      "status": "pass",
      "duration_ms": 9667,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:48.943194+00:00",
      "read_cold_ms": 6426,
      "read_warm_ms": 1458,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 120,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/779_colmap_drop_constraint_merge",
      "num": 779,
      "name": "colmap_drop_constraint_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/779_colmap_drop_constraint_merge.sql",
      "read_script": "generator/spark-reads-df/verify_779_colmap_drop_constraint_merge.py",
      "description": "Column mapping + DROP COLUMN + constraint + MERGE. Four-way feature combination. Tests that MERGE respects CHECK constraints after a column has been dropped under column mapping, and that the dropped column does not interfere with constraint evaluation or merge logic.",
      "status": "pass",
      "duration_ms": 6809,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:50:55.755395+00:00",
      "read_cold_ms": 4067,
      "read_warm_ms": 1387,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/77_in_commit_timestamps_reliable_time_travel",
      "num": 77,
      "name": "in_commit_timestamps_reliable_time_travel",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/77_in_commit_timestamps_reliable_time_travel.sql",
      "read_script": "generator/spark-reads-df/verify_77_in_commit_timestamps_reliable_time_travel.py",
      "description": "In-commit timestamps for reliable time travel queries.",
      "status": "pass",
      "duration_ms": 55533,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:51:51.157076+00:00",
      "read_cold_ms": 6705,
      "read_warm_ms": 794,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 268,
      "write_warm_ms": 250,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:time-travel",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/780_nmbys_all_features",
      "num": 780,
      "name": "nmbys_all_features",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/780_nmbys_all_features.sql",
      "read_script": "generator/spark-reads-df/verify_780_nmbys_all_features.py",
      "description": "NM-BY-SOURCE + DV + CDC + partition + constraint. Five-way stress test with conditional NM-BY-SOURCE (both UPDATE and DELETE branches). Tests the most complex feature interaction: partitioned CDC table with active constraint, deletion vectors, and conditional NM-BY-SOURCE logic.",
      "status": "pass",
      "duration_ms": 10925,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:02.082475+00:00",
      "read_cold_ms": 5037,
      "read_warm_ms": 925,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 409,
      "write_warm_ms": 350,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/781_cdc_nmbys_colmap",
      "num": 781,
      "name": "cdc_nmbys_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/781_cdc_nmbys_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_781_cdc_nmbys_colmap.py",
      "description": "CDC + NOT MATCHED BY SOURCE + column mapping (name mode). Three-way combination: CDF must use logical column names from the column mapping, and NM-BY-SOURCE DELETE must produce correct CDF delete events.",
      "status": "pass",
      "duration_ms": 5271,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:07.354357+00:00",
      "read_cold_ms": 2945,
      "read_warm_ms": 915,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 99,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/782_nmbys_evolve_partition",
      "num": 782,
      "name": "nmbys_evolve_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/782_nmbys_evolve_partition.sql",
      "read_script": "generator/spark-reads-df/verify_782_nmbys_evolve_partition.py",
      "description": "NOT MATCHED BY SOURCE + schema evolution + partitioning. Three-way combination: partitioned table gets a new column via ALTER TABLE, then MERGE with NM-BY-SOURCE uses the new column. Verifies that schema evolution interacts correctly with partitioned NM-BY-SOURCE.",
      "status": "pass",
      "duration_ms": 7638,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:14.995309+00:00",
      "read_cold_ms": 2614,
      "read_warm_ms": 661,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 73,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/783_constraint_two_violations",
      "num": 783,
      "name": "constraint_two_violations",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/783_constraint_two_violations.sql",
      "read_script": "generator/spark-reads-df/verify_783_constraint_two_violations.py",
      "description": "Two CHECK constraints added, one dropped, then data inserted that violates the dropped constraint but satisfies the remaining one. Verifies that DROP CONSTRAINT only removes the targeted constraint and the other remains enforced.",
      "status": "pass",
      "duration_ms": 4426,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:19.422924+00:00",
      "read_cold_ms": 2853,
      "read_warm_ms": 489,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 43,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/784_rename_three_plus_merge",
      "num": 784,
      "name": "rename_three_plus_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/784_rename_three_plus_merge.sql",
      "read_script": "generator/spark-reads-df/verify_784_rename_three_plus_merge.py",
      "description": "Three consecutive RENAME COLUMN operations followed by a MERGE that uses all the new column names. Verifies that column mapping tracks multiple renames correctly and MERGE references the final names.",
      "status": "pass",
      "duration_ms": 8077,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:27.500130+00:00",
      "read_cold_ms": 2329,
      "read_warm_ms": 4356,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 124,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/785_drop_two_plus_merge",
      "num": 785,
      "name": "drop_two_plus_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/785_drop_two_plus_merge.sql",
      "read_script": "generator/spark-reads-df/verify_785_drop_two_plus_merge.py",
      "description": "Two DROP COLUMN operations followed by a MERGE on the reduced schema. Verifies that column mapping correctly handles multiple column drops and that MERGE operates on the surviving columns only.",
      "status": "pass",
      "duration_ms": 8230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:35.732544+00:00",
      "read_cold_ms": 3203,
      "read_warm_ms": 3722,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 55,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:drop-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/786_cdc_every_dml_type",
      "num": 786,
      "name": "cdc_every_dml_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/786_cdc_every_dml_type.sql",
      "read_script": "generator/spark-reads-df/verify_786_cdc_every_dml_type.py",
      "description": "CDC table exercising all 4 DML types: INSERT, UPDATE, DELETE, MERGE. Each operation produces exact, verifiable CDF row counts. This is the most comprehensive single-table CDF test.",
      "status": "pass",
      "duration_ms": 6491,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:42.225826+00:00",
      "read_cold_ms": 2122,
      "read_warm_ms": 581,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 220,
      "write_warm_ms": 196,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/787_merge_nmbys_delete_then_insert",
      "num": 787,
      "name": "merge_nmbys_delete_then_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/787_merge_nmbys_delete_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_787_merge_nmbys_delete_then_insert.py",
      "description": "NM-BY-SOURCE DELETE followed by re-INSERT of the same ID range. Tests that rows purged by NOT MATCHED BY SOURCE can be re-inserted in a subsequent operation with new generation markers.",
      "status": "pass",
      "duration_ms": 7219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:49.445948+00:00",
      "read_cold_ms": 2773,
      "read_warm_ms": 3494,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 66,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/788_partition_key_update_cdc",
      "num": 788,
      "name": "partition_key_update_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/788_partition_key_update_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_788_partition_key_update_cdc.py",
      "description": "UPDATE on a partition key column with CDC enabled. Tests that CDF correctly captures cross-partition row moves (delete from old partition + insert into new partition in CDF terms).",
      "status": "pass",
      "duration_ms": 4833,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:54.280777+00:00",
      "read_cold_ms": 2545,
      "read_warm_ms": 1071,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 54,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/789_sparse_wide_merge",
      "num": 789,
      "name": "sparse_wide_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/789_sparse_wide_merge.sql",
      "read_script": "generator/spark-reads-df/verify_789_sparse_wide_merge.py",
      "description": "Wide table with 10 columns, mostly NULL at insert time, then a MERGE that partially fills in sparse columns. Tests that NULL-heavy Parquet files and partial column updates work correctly.",
      "status": "pass",
      "duration_ms": 4833,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:52:59.114214+00:00",
      "read_cold_ms": 2698,
      "read_warm_ms": 860,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 111,
      "tags": [
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/78_domain_metadata_row_tracking_domain",
      "num": 78,
      "name": "domain_metadata_row_tracking_domain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/78_domain_metadata_row_tracking_domain.sql",
      "read_script": "generator/spark-reads-df/verify_78_domain_metadata_row_tracking_domain.py",
      "description": "Demonstrates domain metadata for row tracking feature. The row tracking feature stores its configuration and state in domain metadata.",
      "status": "pass",
      "duration_ms": 66198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:05.309833+00:00",
      "read_cold_ms": 7775,
      "read_warm_ms": 1826,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 428,
      "write_warm_ms": 248,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/790_monotonic_id_delete_gap",
      "num": 790,
      "name": "monotonic_id_delete_gap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/790_monotonic_id_delete_gap.sql",
      "read_script": "generator/spark-reads-df/verify_790_monotonic_id_delete_gap.py",
      "description": "Sequential IDs with a gap created by DELETE, then re-INSERT into the gap with a different generation marker. Tests that deletion vectors handle mid-range deletes and that re-inserted rows with the same IDs coexist correctly with surviving rows.",
      "status": "pass",
      "duration_ms": 5238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:10.548312+00:00",
      "read_cold_ms": 3769,
      "read_warm_ms": 754,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 74,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/791_merge_nmbys_partition_cdc_constraint",
      "num": 791,
      "name": "merge_nmbys_partition_cdc_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/791_merge_nmbys_partition_cdc_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_791_merge_nmbys_partition_cdc_constraint.py",
      "description": "Four-way combination: NM-BY-SOURCE + partition + CDC + CHECK constraint. Tests that CDF captures NM-BY-SOURCE events across partitions, and that the constraint is enforced during NM-BY-SOURCE UPDATE operations.",
      "status": "pass",
      "duration_ms": 11491,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:22.039539+00:00",
      "read_cold_ms": 4091,
      "read_warm_ms": 4469,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 458,
      "write_warm_ms": 327,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/792_colmap_rename_drop_evolve",
      "num": 792,
      "name": "colmap_rename_drop_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/792_colmap_rename_drop_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_792_colmap_rename_drop_evolve.py",
      "description": "Full column mutation trilogy: RENAME + DROP + ADD COLUMN, all with column mapping enabled. Tests that the column mapping metadata correctly tracks all three mutation types in sequence.",
      "status": "pass",
      "duration_ms": 7845,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:29.896143+00:00",
      "read_cold_ms": 4664,
      "read_warm_ms": 1253,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
      "write_warm_ms": 119,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/793_constraint_across_merge_nmbys",
      "num": 793,
      "name": "constraint_across_merge_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/793_constraint_across_merge_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_793_constraint_across_merge_nmbys.py",
      "description": "CHECK constraint must be enforced during a MERGE with NOT MATCHED BY SOURCE UPDATE. The NM-BY-SOURCE clause updates value using an expression that keeps it positive, satisfying the constraint. Verifies that constraint validation applies to NM-BY-SOURCE UPDATE paths.",
      "status": "pass",
      "duration_ms": 9260,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:39.160065+00:00",
      "read_cold_ms": 6280,
      "read_warm_ms": 1429,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 112,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/794_cdc_schema_evolve_nmbys",
      "num": 794,
      "name": "cdc_schema_evolve_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/794_cdc_schema_evolve_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_794_cdc_schema_evolve_nmbys.py",
      "description": "CDC + schema evolution + NM-BY-SOURCE UPDATE. CDF must correctly capture events across a schema change (ADD COLUMN) with NM-BY-SOURCE populating the new column differently for matched vs unmatched rows.",
      "status": "pass",
      "duration_ms": 10460,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:49.621352+00:00",
      "read_cold_ms": 7402,
      "read_warm_ms": 1346,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 311,
      "write_warm_ms": 496,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/795_large_partition_nmbys",
      "num": 795,
      "name": "large_partition_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/795_large_partition_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_795_large_partition_nmbys.py",
      "description": "Large-scale partitioned table: 500 rows across 5 partitions, then NM-BY-SOURCE DELETE removes 300 rows. Tests that NM-BY-SOURCE DELETE works correctly at scale across multiple partitions.",
      "status": "pass",
      "duration_ms": 4476,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:54:54.097810+00:00",
      "read_cold_ms": 2893,
      "read_warm_ms": 728,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 194,
      "write_warm_ms": 200,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/796_merge_nmbys_optimize_cdc",
      "num": 796,
      "name": "merge_nmbys_optimize_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/796_merge_nmbys_optimize_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_796_merge_nmbys_optimize_cdc.py",
      "description": "NM-BY-SOURCE + OPTIMIZE + CDC. Tests that CDF is correct after file compaction (OPTIMIZE), and that NM-BY-SOURCE DELETE after OPTIMIZE produces valid CDF events from compacted files.",
      "status": "pass",
      "duration_ms": 9312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:55:03.410796+00:00",
      "read_cold_ms": 2990,
      "read_warm_ms": 1738,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 595,
      "write_warm_ms": 607,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/797_constraint_survive_optimize_merge",
      "num": 797,
      "name": "constraint_survive_optimize_merge",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/797_constraint_survive_optimize_merge.sql",
      "read_script": "generator/spark-reads-df/verify_797_constraint_survive_optimize_merge.py",
      "description": "CHECK constraint added after multi-batch inserts, survives OPTIMIZE, then enforced during a MERGE (all data valid throughout). Verifies that OPTIMIZE does not lose constraint metadata and that MERGE respects the constraint post-compaction.",
      "status": "pass",
      "duration_ms": 5425,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:55:08.836936+00:00",
      "read_cold_ms": 3276,
      "read_warm_ms": 983,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 476,
      "write_warm_ms": 427,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/798_colmap_nmbys_cdc_partition",
      "num": 798,
      "name": "colmap_nmbys_cdc_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/798_colmap_nmbys_cdc_partition.sql",
      "read_script": "generator/spark-reads-df/verify_798_colmap_nmbys_cdc_partition.py",
      "description": "Four-way combination: column mapping + NM-BY-SOURCE + CDC + partition. Tests that CDF uses logical column names from colmap, NM-BY-SOURCE DELETE works across partitions, and all features interoperate.",
      "status": "pass",
      "duration_ms": 5443,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:55:14.283281+00:00",
      "read_cold_ms": 2279,
      "read_warm_ms": 1302,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 299,
      "write_warm_ms": 351,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/799_all_gaps_combined",
      "num": 799,
      "name": "all_gaps_combined",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/799_all_gaps_combined.sql",
      "read_script": "generator/spark-reads-df/verify_799_all_gaps_combined.py",
      "description": "All 4 gap areas in one test: NM-BY-SOURCE + column mapping mutations (RENAME) + constraint lifecycle (add then drop then violate) + CDC with exact counts. This is the comprehensive gap-coverage test.",
      "status": "pass",
      "duration_ms": 5739,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:55:20.024534+00:00",
      "read_cold_ms": 4036,
      "read_warm_ms": 749,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 305,
      "write_warm_ms": 438,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/79_domain_metadata_clustering_domain",
      "num": 79,
      "name": "domain_metadata_clustering_domain",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/79_domain_metadata_clustering_domain.sql",
      "read_script": "generator/spark-reads-df/verify_79_domain_metadata_clustering_domain.py",
      "description": "Domain metadata for liquid clustering feature.",
      "status": "pass",
      "duration_ms": 75776,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:56:35.798524+00:00",
      "read_cold_ms": 2459,
      "read_warm_ms": 3892,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 421,
      "write_warm_ms": 366,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:liquid-clustering",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/800_production_ultimate",
      "num": 800,
      "name": "production_ultimate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/800_production_ultimate.sql",
      "read_script": "generator/spark-reads-df/verify_800_production_ultimate.py",
      "description": "Ultimate production test combining every major feature: NM-BY-SOURCE + deletion vectors + CDC + column mapping + partition + CHECK constraint + schema evolution + OPTIMIZE + RENAME + INSERT + UPDATE + DELETE + MERGE. This is the most comprehensive single-table integration test.",
      "status": "pass",
      "duration_ms": 7490,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:56:43.289881+00:00",
      "read_cold_ms": 5479,
      "read_warm_ms": 1020,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 929,
      "write_warm_ms": 737,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/801_merge_int_types",
      "num": 801,
      "name": "merge_int_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/801_merge_int_types.sql",
      "read_script": "generator/spark-reads-df/verify_801_merge_int_types.py",
      "description": "MERGE where target and source have INT, SMALLINT, TINYINT columns. Tests INT preservation through MERGE UPDATE SET.",
      "status": "pass",
      "duration_ms": 7027,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:56:50.317311+00:00",
      "read_cold_ms": 2803,
      "read_warm_ms": 3193,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/802_merge_float_double",
      "num": 802,
      "name": "merge_float_double",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/802_merge_float_double.sql",
      "read_script": "generator/spark-reads-df/verify_802_merge_float_double.py",
      "description": "MERGE with FLOAT and DOUBLE columns. Tests floating-point precision through MERGE.",
      "status": "pass",
      "duration_ms": 9434,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:56:59.753469+00:00",
      "read_cold_ms": 4415,
      "read_warm_ms": 1364,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 88,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/803_merge_decimal_precision",
      "num": 803,
      "name": "merge_decimal_precision",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/803_merge_decimal_precision.sql",
      "read_script": "generator/spark-reads-df/verify_803_merge_decimal_precision.py",
      "description": "MERGE where DECIMAL precision must be preserved through UPDATE SET.",
      "status": "pass",
      "duration_ms": 6557,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:06.312504+00:00",
      "read_cold_ms": 4309,
      "read_warm_ms": 1116,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 176,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/804_merge_timestamp_types",
      "num": 804,
      "name": "merge_timestamp_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/804_merge_timestamp_types.sql",
      "read_script": "generator/spark-reads-df/verify_804_merge_timestamp_types.py",
      "description": "MERGE with TIMESTAMP columns. Tests microsecond precision preservation through MERGE.",
      "status": "pass",
      "duration_ms": 3560,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:09.873267+00:00",
      "read_cold_ms": 1709,
      "read_warm_ms": 984,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 77,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/805_merge_date_type",
      "num": 805,
      "name": "merge_date_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/805_merge_date_type.sql",
      "read_script": "generator/spark-reads-df/verify_805_merge_date_type.py",
      "description": "MERGE with DATE columns (arrow_cast Date32). Tests DATE preservation through MERGE.",
      "status": "pass",
      "duration_ms": 6438,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:16.312079+00:00",
      "read_cold_ms": 5463,
      "read_warm_ms": 598,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 115,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/806_merge_boolean_type",
      "num": 806,
      "name": "merge_boolean_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/806_merge_boolean_type.sql",
      "read_script": "generator/spark-reads-df/verify_806_merge_boolean_type.py",
      "description": "MERGE with BOOLEAN columns in both conditional evaluation and UPDATE SET. Tests BOOLEAN in WHEN MATCHED AND conditions.",
      "status": "pass",
      "duration_ms": 5480,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:21.792893+00:00",
      "read_cold_ms": 3477,
      "read_warm_ms": 816,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 86,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/807_merge_string_types",
      "num": 807,
      "name": "merge_string_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/807_merge_string_types.sql",
      "read_script": "generator/spark-reads-df/verify_807_merge_string_types.py",
      "description": "MERGE where STRING columns are updated with CONCAT expressions. Tests string manipulation in MERGE UPDATE SET.",
      "status": "pass",
      "duration_ms": 4034,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:25.829462+00:00",
      "read_cold_ms": 2256,
      "read_warm_ms": 828,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 159,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/808_merge_null_in_all_types",
      "num": 808,
      "name": "merge_null_in_all_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/808_merge_null_in_all_types.sql",
      "read_script": "generator/spark-reads-df/verify_808_merge_null_in_all_types.py",
      "description": "MERGE where source has NULL values for every data type. Tests NULL propagation through MERGE UPDATE SET.",
      "status": "pass",
      "duration_ms": 6702,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:32.532380+00:00",
      "read_cold_ms": 1900,
      "read_warm_ms": 3829,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 133,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/809_merge_struct_update",
      "num": 809,
      "name": "merge_struct_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/809_merge_struct_update.sql",
      "read_script": "generator/spark-reads-df/verify_809_merge_struct_update.py",
      "description": "MERGE that updates STRUCT column values. Tests nested struct round-trip through MERGE SET.",
      "status": "pass",
      "duration_ms": 8743,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:57:41.276180+00:00",
      "read_cold_ms": 3048,
      "read_warm_ms": 734,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 105,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/80_collations_language_aware_sorting",
      "num": 80,
      "name": "collations_language_aware_sorting",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/80_collations_language_aware_sorting.sql",
      "read_script": "generator/spark-reads-df/verify_80_collations_language_aware_sorting.py",
      "description": "Collation support for language-aware string comparisons.",
      "status": "pass",
      "duration_ms": 25621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:58:06.898140+00:00",
      "read_cold_ms": 4764,
      "read_warm_ms": 1002,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 81,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/810_merge_mixed_type_key",
      "num": 810,
      "name": "merge_mixed_type_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/810_merge_mixed_type_key.sql",
      "read_script": "generator/spark-reads-df/verify_810_merge_mixed_type_key.py",
      "description": "MERGE where the join key is INT (not BIGINT). Tests non-BIGINT join key handling.",
      "status": "pass",
      "duration_ms": 4615,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:58:11.514412+00:00",
      "read_cold_ms": 2622,
      "read_warm_ms": 896,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 62,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/811_merge_decimal_key",
      "num": 811,
      "name": "merge_decimal_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/811_merge_decimal_key.sql",
      "read_script": "generator/spark-reads-df/verify_811_merge_decimal_key.py",
      "description": "MERGE where join key is DECIMAL. Tests DECIMAL equality in join predicate.",
      "status": "pass",
      "duration_ms": 1164,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:11.108811+00:00",
      "read_cold_ms": 699,
      "read_warm_ms": 214,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 158,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/812_merge_string_key",
      "num": 812,
      "name": "merge_string_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/812_merge_string_key.sql",
      "read_script": "generator/spark-reads-df/verify_812_merge_string_key.py",
      "description": "MERGE where join key is STRING. Tests STRING equality in join predicate.",
      "status": "pass",
      "duration_ms": 1168,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:12.277466+00:00",
      "read_cold_ms": 684,
      "read_warm_ms": 208,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 135,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/813_merge_boolean_key",
      "num": 813,
      "name": "merge_boolean_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/813_merge_boolean_key.sql",
      "read_script": "generator/spark-reads-df/verify_813_merge_boolean_key.py",
      "description": "MERGE where join key includes BOOLEAN. Tests BOOLEAN in join predicate. Unusual but valid -- compound key with id + is_active.",
      "status": "pass",
      "duration_ms": 1203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:13.481033+00:00",
      "read_cold_ms": 722,
      "read_warm_ms": 226,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 107,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/814_merge_timestamp_key",
      "num": 814,
      "name": "merge_timestamp_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/814_merge_timestamp_key.sql",
      "read_script": "generator/spark-reads-df/verify_814_merge_timestamp_key.py",
      "description": "MERGE where join key includes TIMESTAMP. Tests TIMESTAMP equality in join predicate.",
      "status": "pass",
      "duration_ms": 1142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:14.624125+00:00",
      "read_cold_ms": 674,
      "read_warm_ms": 219,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 77,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/815_merge_update_decimal_arithmetic",
      "num": 815,
      "name": "merge_update_decimal_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/815_merge_update_decimal_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_815_merge_update_decimal_arithmetic.py",
      "description": "MERGE UPDATE SET with DECIMAL arithmetic. Tests that DECIMAL arithmetic in SET clause preserves precision through pre-computed source values.",
      "status": "pass",
      "duration_ms": 4489,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:58:45.582111+00:00",
      "read_cold_ms": 2745,
      "read_warm_ms": 981,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 76,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/816_merge_update_all_types_at_once",
      "num": 816,
      "name": "merge_update_all_types_at_once",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/816_merge_update_all_types_at_once.sql",
      "read_script": "generator/spark-reads-df/verify_816_merge_update_all_types_at_once.py",
      "description": "MERGE that updates every column type in a single UPDATE SET clause. \"Kitchen sink\" type test: STRING, INT, DOUBLE, BOOLEAN, DECIMAL, TIMESTAMP all updated at once.",
      "status": "pass",
      "duration_ms": 4330,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:58:49.912541+00:00",
      "read_cold_ms": 2318,
      "read_warm_ms": 1053,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 85,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/817_merge_insert_all_types",
      "num": 817,
      "name": "merge_insert_all_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/817_merge_insert_all_types.sql",
      "read_script": "generator/spark-reads-df/verify_817_merge_insert_all_types.py",
      "description": "MERGE NOT MATCHED INSERT with every column type. Tests that the INSERT path handles all types correctly. Zero overlap between target and source -- all rows go through NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 5601,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:58:55.515813+00:00",
      "read_cold_ms": 2274,
      "read_warm_ms": 746,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 77,
      "write_warm_ms": 101,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/818_merge_int_to_bigint_coerce",
      "num": 818,
      "name": "merge_int_to_bigint_coerce",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/818_merge_int_to_bigint_coerce.sql",
      "read_script": "generator/spark-reads-df/verify_818_merge_int_to_bigint_coerce.py",
      "description": "MERGE where source provides INT-range values into a BIGINT target column. Tests implicit widening coercion (INT -> BIGINT) through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 1313,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:39:07.948901+00:00",
      "read_cold_ms": 764,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 124,
      "write_warm_ms": 47,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:type-widening",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/819_merge_float_to_double_coerce",
      "num": 819,
      "name": "merge_float_to_double_coerce",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/819_merge_float_to_double_coerce.sql",
      "read_script": "generator/spark-reads-df/verify_819_merge_float_to_double_coerce.py",
      "description": "MERGE where source FLOAT column updates target DOUBLE column. Tests widening coercion (FLOAT -> DOUBLE) through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 1175,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:16.940707+00:00",
      "read_cold_ms": 654,
      "read_warm_ms": 217,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 57,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/81_v2_checkpoint_sidecar_json_pointers",
      "num": 81,
      "name": "v2_checkpoint_sidecar_json_pointers",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/81_v2_checkpoint_sidecar_json_pointers.sql",
      "read_script": "generator/spark-reads-df/verify_81_v2_checkpoint_sidecar_json_pointers.py",
      "description": "- V2 checkpoint format with sidecar files and JSON pointers - Large table (65k records) triggers multiple sidecar files - Deletion vectors enabled - Complex social media content management schema (28 columns) - Date32 and Timestamp(Microsecond) types - Multiple UPDATE and DELETE...",
      "status": "pass",
      "duration_ms": 40038,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T01:59:44.841132+00:00",
      "read_cold_ms": 10383,
      "read_warm_ms": 3199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 25761,
      "write_warm_ms": 24461,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-sidecar",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/820_merge_decimal_scale_mismatch",
      "num": 820,
      "name": "merge_decimal_scale_mismatch",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/820_merge_decimal_scale_mismatch.sql",
      "read_script": "generator/spark-reads-df/verify_820_merge_decimal_scale_mismatch.py",
      "description": "MERGE where source DECIMAL(10,2) updates target DECIMAL(10,4). Tests decimal scale widening in MERGE SET -- source has 2 decimal places, target expects 4. Updated amounts should show .XX00 pattern.",
      "status": "pass",
      "duration_ms": 1163,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:18.104097+00:00",
      "read_cold_ms": 659,
      "read_warm_ms": 226,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 64,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/821_merge_decimal_negative",
      "num": 821,
      "name": "merge_decimal_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/821_merge_decimal_negative.sql",
      "read_script": "generator/spark-reads-df/verify_821_merge_decimal_negative.py",
      "description": "MERGE with negative DECIMAL values. Tests sign preservation and sign-flipping through MERGE UPDATE SET.",
      "status": "pass",
      "duration_ms": 1136,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:19.240613+00:00",
      "read_cold_ms": 656,
      "read_warm_ms": 215,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 32,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/822_merge_decimal_zero",
      "num": 822,
      "name": "merge_decimal_zero",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/822_merge_decimal_zero.sql",
      "read_script": "generator/spark-reads-df/verify_822_merge_decimal_zero.py",
      "description": "MERGE updating DECIMAL columns to zero. Tests zero-value DECIMAL handling with conditional MERGE clauses targeting different columns.",
      "status": "pass",
      "duration_ms": 1147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:20.387824+00:00",
      "read_cold_ms": 670,
      "read_warm_ms": 212,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 44,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/823_merge_timestamp_microsecond",
      "num": 823,
      "name": "merge_timestamp_microsecond",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/823_merge_timestamp_microsecond.sql",
      "read_script": "generator/spark-reads-df/verify_823_merge_timestamp_microsecond.py",
      "description": "MERGE with timestamps differing by 1 microsecond. Tests microsecond precision in MERGE UPDATE SET. Join is on id, not timestamp.",
      "status": "pass",
      "duration_ms": 8913,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:00:10.590755+00:00",
      "read_cold_ms": 5666,
      "read_warm_ms": 1591,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 166,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/824_merge_boolean_flip",
      "num": 824,
      "name": "merge_boolean_flip",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/824_merge_boolean_flip.sql",
      "read_script": "generator/spark-reads-df/verify_824_merge_boolean_flip.py",
      "description": "MERGE that flips all BOOLEAN values using CASE expressions. Tests boolean negation through MERGE UPDATE SET across multiple boolean columns.",
      "status": "pass",
      "duration_ms": 1174,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:21.562042+00:00",
      "read_cold_ms": 672,
      "read_warm_ms": 225,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 72,
      "write_warm_ms": 66,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/825_merge_string_empty",
      "num": 825,
      "name": "merge_string_empty",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/825_merge_string_empty.sql",
      "read_script": "generator/spark-reads-df/verify_825_merge_string_empty.py",
      "description": "MERGE with empty string values. Tests '' (empty string) handling and detection through MERGE UPDATE SET with CASE expressions.",
      "status": "pass",
      "duration_ms": 1152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:22.714411+00:00",
      "read_cold_ms": 660,
      "read_warm_ms": 218,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 163,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/826_merge_null_key",
      "num": 826,
      "name": "merge_null_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/826_merge_null_key.sql",
      "read_script": "generator/spark-reads-df/verify_826_merge_null_key.py",
      "description": "MERGE where some source rows have NULL join key. Tests NULL!=NULL semantics in MERGE ON clause -- NULL-keyed source rows never match, always go through NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 1226,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:23.940974+00:00",
      "read_cold_ms": 678,
      "read_warm_ms": 300,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 92,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/827_merge_delete_typed_predicate",
      "num": 827,
      "name": "merge_delete_typed_predicate",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/827_merge_delete_typed_predicate.sql",
      "read_script": "generator/spark-reads-df/verify_827_merge_delete_typed_predicate.py",
      "description": "MERGE with DELETE clause using typed column predicates. Tests type evaluation (DECIMAL comparison, BOOLEAN equality) in MATCHED DELETE and conditional UPDATE.",
      "status": "pass",
      "duration_ms": 1176,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:25.117488+00:00",
      "read_cold_ms": 640,
      "read_warm_ms": 281,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 73,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/828_merge_nmbys_typed_update",
      "num": 828,
      "name": "merge_nmbys_typed_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/828_merge_nmbys_typed_update.sql",
      "read_script": "generator/spark-reads-df/verify_828_merge_nmbys_typed_update.py",
      "description": "WHEN NOT MATCHED BY SOURCE with typed UPDATE SET. Tests type handling in the NOT MATCHED BY SOURCE path -- zeroing DECIMAL, INT, and setting BOOLEAN.",
      "status": "pass",
      "duration_ms": 4877,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:00:48.388190+00:00",
      "read_cold_ms": 3329,
      "read_warm_ms": 677,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 132,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/829_merge_struct_nested_update",
      "num": 829,
      "name": "merge_struct_nested_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/829_merge_struct_nested_update.sql",
      "read_script": "generator/spark-reads-df/verify_829_merge_struct_nested_update.py",
      "description": "MERGE that updates a STRUCT column with different field values. Tests full struct replacement through MERGE UPDATE and INSERT with named_struct.",
      "status": "pass",
      "duration_ms": 9222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:00:57.612438+00:00",
      "read_cold_ms": 5706,
      "read_warm_ms": 1091,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 62,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/82_multipart_checkpoint_recovery_edge_cases",
      "num": 82,
      "name": "multipart_checkpoint_recovery_edge_cases",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/82_multipart_checkpoint_recovery_edge_cases.sql",
      "read_script": "generator/spark-reads-df/verify_82_multipart_checkpoint_recovery_edge_cases.py",
      "description": "- Multi-part checkpoint edge cases and recovery scenarios - Large table (85,000+ rows) with wide schema (31 columns) - Partitioned by partition_region (6 regions) - Deletion vectors enabled - Multiple UPDATE operations and batch INSERTs - Date32 and Timestamp(Microsecond) types...",
      "status": "pass",
      "duration_ms": 26266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:01:23.878862+00:00",
      "read_cold_ms": 5613,
      "read_warm_ms": 1813,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2634,
      "write_warm_ms": 2552,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-multipart",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/830_merge_decimal_boundary",
      "num": 830,
      "name": "merge_decimal_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/830_merge_decimal_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_830_merge_decimal_boundary.py",
      "description": "MERGE with DECIMAL values at precision boundaries. Tests max/min DECIMAL values through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 1244,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:26.362339+00:00",
      "read_cold_ms": 699,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 55,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/831_merge_update_to_null",
      "num": 831,
      "name": "merge_update_to_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/831_merge_update_to_null.sql",
      "read_script": "generator/spark-reads-df/verify_831_merge_update_to_null.py",
      "description": "MERGE UPDATE SET that explicitly sets typed columns to NULL. Tests NULL introduction per type (STRING, INT, DOUBLE, BOOLEAN, DECIMAL, TIMESTAMP) through MERGE conditional clauses.",
      "status": "pass",
      "duration_ms": 6373,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:01:37.665005+00:00",
      "read_cold_ms": 4398,
      "read_warm_ms": 940,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 49,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/832_merge_conditional_type_cast",
      "num": 832,
      "name": "merge_conditional_type_cast",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/832_merge_conditional_type_cast.sql",
      "read_script": "generator/spark-reads-df/verify_832_merge_conditional_type_cast.py",
      "description": "MERGE with CASE expression producing STRING values computed from different typed columns. Tests type unification (DOUBLE->STRING casts) in MERGE SET through CASE branches.",
      "status": "pass",
      "duration_ms": 1156,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:27.519458+00:00",
      "read_cold_ms": 640,
      "read_warm_ms": 254,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 35,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/833_merge_int_overflow_safe",
      "num": 833,
      "name": "merge_int_overflow_safe",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/833_merge_int_overflow_safe.sql",
      "read_script": "generator/spark-reads-df/verify_833_merge_int_overflow_safe.py",
      "description": "MERGE with INT values near the overflow boundary. Tests INT range safety with values close to INT max (2147483647) through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 1213,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:28.732976+00:00",
      "read_cold_ms": 706,
      "read_warm_ms": 235,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 42,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/834_merge_multi_decimal_update",
      "num": 834,
      "name": "merge_multi_decimal_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/834_merge_multi_decimal_update.sql",
      "read_script": "generator/spark-reads-df/verify_834_merge_multi_decimal_update.py",
      "description": "MERGE updating 4 DECIMAL columns simultaneously with different precision/scale. Tests that each DECIMAL column maintains independent precision through MERGE.",
      "status": "pass",
      "duration_ms": 11206,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:02:01.617834+00:00",
      "read_cold_ms": 7885,
      "read_warm_ms": 1604,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 37,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/835_merge_decimal_cdc",
      "num": 835,
      "name": "merge_decimal_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/835_merge_decimal_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_835_merge_decimal_cdc.py",
      "description": "MERGE with DECIMAL columns + CDC enabled. Tests that CDF records preserve DECIMAL precision through MERGE UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 1982,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:30.715469+00:00",
      "read_cold_ms": 889,
      "read_warm_ms": 215,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 218,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/836_merge_timestamp_cdc",
      "num": 836,
      "name": "merge_timestamp_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/836_merge_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_836_merge_timestamp_cdc.py",
      "description": "MERGE with TIMESTAMP + CDC enabled. Tests that CDF records preserve TIMESTAMP values through MERGE UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 9769,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:02:17.190073+00:00",
      "read_cold_ms": 3224,
      "read_warm_ms": 1228,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 300,
      "write_warm_ms": 229,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/837_merge_decimal_partition",
      "num": 837,
      "name": "merge_decimal_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/837_merge_decimal_partition.sql",
      "read_script": "generator/spark-reads-df/verify_837_merge_decimal_partition.py",
      "description": "MERGE with DECIMAL columns + partitioned table. Tests that DECIMAL precision is preserved through MERGE UPDATE and INSERT across multiple partitions.",
      "status": "pass",
      "duration_ms": 1424,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:32.139728+00:00",
      "read_cold_ms": 912,
      "read_warm_ms": 228,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 174,
      "write_warm_ms": 99,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/838_merge_timestamp_partition",
      "num": 838,
      "name": "merge_timestamp_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/838_merge_timestamp_partition.sql",
      "read_script": "generator/spark-reads-df/verify_838_merge_timestamp_partition.py",
      "description": "MERGE with TIMESTAMP + partitioned table. Tests that TIMESTAMP values are preserved through MERGE UPDATE and INSERT across multiple partitions.",
      "status": "pass",
      "duration_ms": 1274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:33.413900+00:00",
      "read_cold_ms": 744,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 153,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/839_merge_types_constraint",
      "num": 839,
      "name": "merge_types_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/839_merge_types_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_839_merge_types_constraint.py",
      "description": "MERGE where a CHECK constraint applies to a typed DECIMAL column. Tests that constraint enforcement works correctly on MERGE-updated DECIMAL values.",
      "status": "pass",
      "duration_ms": 1141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:34.555436+00:00",
      "read_cold_ms": 676,
      "read_warm_ms": 211,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 58,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/83_writer_feature_combinations_complex",
      "num": 83,
      "name": "writer_feature_combinations_complex",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/83_writer_feature_combinations_complex.sql",
      "read_script": "generator/spark-reads-df/verify_83_writer_feature_combinations_complex.py",
      "description": "- Complex combinations of multiple writer features interacting - Row Tracking + Deletion Vectors + Change Data Feed - Column Mapping + Check Constraints + Generated Columns - 40 columns with timestamp_ntz fields requiring metadata - Multiple UPDATE operations tracking flight...",
      "status": "pass",
      "duration_ms": 30318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:03:10.348063+00:00",
      "read_cold_ms": 6650,
      "read_warm_ms": 1111,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 732,
      "write_warm_ms": 703,
      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:row-tracking",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/840_merge_types_optimize",
      "num": 840,
      "name": "merge_types_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/840_merge_types_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_840_merge_types_optimize.py",
      "description": "MERGE on typed columns after OPTIMIZE. Tests that MERGE reads correct DECIMAL, TIMESTAMP, and BOOLEAN types from compacted Parquet files.",
      "status": "pass",
      "duration_ms": 1143,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:35.699426+00:00",
      "read_cold_ms": 658,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 262,
      "write_warm_ms": 157,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/841_merge_types_evolve",
      "num": 841,
      "name": "merge_types_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/841_merge_types_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_841_merge_types_evolve.py",
      "description": "MERGE after schema evolution adds a DECIMAL column. Tests that MERGE correctly handles the evolved DECIMAL(10,2) column for both matched rows (which had NULL before evolution) and newly inserted rows.",
      "status": "pass",
      "duration_ms": 3542,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:03:18.801099+00:00",
      "read_cold_ms": 2032,
      "read_warm_ms": 697,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 61,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/842_merge_decimal_null_mixed",
      "num": 842,
      "name": "merge_decimal_null_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/842_merge_decimal_null_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_842_merge_decimal_null_mixed.py",
      "description": "MERGE where source DECIMAL is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL DECIMAL(10,2) through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 1126,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:36.826068+00:00",
      "read_cold_ms": 617,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 63,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/843_merge_timestamp_null_mixed",
      "num": 843,
      "name": "merge_timestamp_null_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/843_merge_timestamp_null_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_843_merge_timestamp_null_mixed.py",
      "description": "MERGE where source TIMESTAMP is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL TIMESTAMP through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 1389,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:38.215317+00:00",
      "read_cold_ms": 639,
      "read_warm_ms": 312,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 65,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/844_merge_boolean_null_mixed",
      "num": 844,
      "name": "merge_boolean_null_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/844_merge_boolean_null_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_844_merge_boolean_null_mixed.py",
      "description": "MERGE where source BOOLEAN is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL BOOLEAN through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 1451,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:39.666798+00:00",
      "read_cold_ms": 746,
      "read_warm_ms": 340,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 38,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/845_merge_int_null_mixed",
      "num": 845,
      "name": "merge_int_null_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/845_merge_int_null_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_845_merge_int_null_mixed.py",
      "description": "MERGE where source INT is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL INT through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 1216,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:40.883067+00:00",
      "read_cold_ms": 702,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 71,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/846_merge_double_nan_like",
      "num": 846,
      "name": "merge_double_nan_like",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/846_merge_double_nan_like.sql",
      "read_script": "generator/spark-reads-df/verify_846_merge_double_nan_like.py",
      "description": "MERGE with DOUBLE values at floating-point extremes (very small approaching zero, very large). Tests floating-point edge cases through MERGE UPDATE/INSERT.",
      "status": "pass",
      "duration_ms": 1302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:42.185533+00:00",
      "read_cold_ms": 816,
      "read_warm_ms": 199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 50,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/847_merge_struct_with_types",
      "num": 847,
      "name": "merge_struct_with_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/847_merge_struct_with_types.sql",
      "read_script": "generator/spark-reads-df/verify_847_merge_struct_with_types.py",
      "description": "STRUCT containing STRING, INT, and BOOLEAN fields. Tests struct with mixed typed fields through MERGE UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 9722,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:03:58.146703+00:00",
      "read_cold_ms": 4506,
      "read_warm_ms": 3493,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 54,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/848_merge_all_types_nmbys",
      "num": 848,
      "name": "merge_all_types_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/848_merge_all_types_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_848_merge_all_types_nmbys.py",
      "description": "MERGE NOT MATCHED BY SOURCE updating all typed columns. Tests type handling in the NM-BY-SOURCE UPDATE path across STRING, INT, DOUBLE, BOOLEAN, and DECIMAL simultaneously.",
      "status": "pass",
      "duration_ms": 1204,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:43.390526+00:00",
      "read_cold_ms": 737,
      "read_warm_ms": 210,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 191,
      "write_warm_ms": 73,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/849_merge_typed_delete_insert",
      "num": 849,
      "name": "merge_typed_delete_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/849_merge_typed_delete_insert.sql",
      "read_script": "generator/spark-reads-df/verify_849_merge_typed_delete_insert.py",
      "description": "MERGE with typed DELETE predicate (DECIMAL range) + NOT MATCHED INSERT. Tests DELETE based on DECIMAL comparison combined with INSERT of full type set in a single MERGE statement.",
      "status": "pass",
      "duration_ms": 1143,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:44.534015+00:00",
      "read_cold_ms": 723,
      "read_warm_ms": 204,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 54,
      "write_warm_ms": 66,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/84_variant_with_column_mapping",
      "num": 84,
      "name": "variant_with_column_mapping",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/84_variant_with_column_mapping.sql",
      "read_script": "generator/spark-reads-df/verify_84_variant_with_column_mapping.py",
      "description": "Demonstrates Variant data type combined with column mapping feature.",
      "status": "pass",
      "duration_ms": 17434,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:04:28.745034+00:00",
      "read_cold_ms": 2188,
      "read_warm_ms": 1007,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 471,
      "write_warm_ms": 312,
      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/850_merge_type_comprehensive",
      "num": 850,
      "name": "merge_type_comprehensive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/850_merge_type_comprehensive.sql",
      "read_script": "generator/spark-reads-df/verify_850_merge_type_comprehensive.py",
      "description": "per-type assertions. Exercises all types through MERGE UPDATE, DELETE, and INSERT paths simultaneously.",
      "status": "pass",
      "duration_ms": 1380,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:45.914521+00:00",
      "read_cold_ms": 696,
      "read_warm_ms": 218,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 310,
      "write_warm_ms": 300,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/851_merge_multi_type_update_set",
      "num": 851,
      "name": "merge_multi_type_update_set",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/851_merge_multi_type_update_set.sql",
      "read_script": "generator/spark-reads-df/verify_851_merge_multi_type_update_set.py",
      "description": "MERGE that updates 5 different typed columns in a single UPDATE SET, each with a different transformation. Tests that the engine correctly applies heterogeneous per-column transformations within one MERGE UPDATE SET clause.",
      "status": "pass",
      "duration_ms": 7379,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:04:41.343917+00:00",
      "read_cold_ms": 5196,
      "read_warm_ms": 995,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 59,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/852_merge_chain_typed",
      "num": 852,
      "name": "merge_chain_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/852_merge_chain_typed.sql",
      "read_script": "generator/spark-reads-df/verify_852_merge_chain_typed.py",
      "description": "Two sequential MERGEs on a table with DECIMAL+TIMESTAMP+BOOLEAN. Each MERGE transforms the typed columns. Tests type stability across MERGE chains -- ensures that writing typed values in one MERGE does not corrupt them for the next MERGE read.",
      "status": "pass",
      "duration_ms": 7957,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:04:49.303826+00:00",
      "read_cold_ms": 5833,
      "read_warm_ms": 950,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 152,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/853_merge_mixed_predicate_types",
      "num": 853,
      "name": "merge_mixed_predicate_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/853_merge_mixed_predicate_types.sql",
      "read_script": "generator/spark-reads-df/verify_853_merge_mixed_predicate_types.py",
      "description": "MERGE with WHEN MATCHED conditions using different typed predicates: DECIMAL comparison + BOOLEAN check + INT range. Four WHEN MATCHED clauses with progressively broader predicates form a priority cascade.",
      "status": "pass",
      "duration_ms": 3838,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:04:53.142701+00:00",
      "read_cold_ms": 2369,
      "read_warm_ms": 643,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 38,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/854_merge_partial_column_update",
      "num": 854,
      "name": "merge_partial_column_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/854_merge_partial_column_update.sql",
      "read_script": "generator/spark-reads-df/verify_854_merge_partial_column_update.py",
      "description": "MERGE where UPDATE SET only touches 2 of 8 columns. Tests that untouched columns survive the MERGE write cycle without corruption or zeroing.",
      "status": "pass",
      "duration_ms": 7339,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:05:00.481718+00:00",
      "read_cold_ms": 2103,
      "read_warm_ms": 3537,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 126,
      "write_warm_ms": 56,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/855_merge_source_subset_columns",
      "num": 855,
      "name": "merge_source_subset_columns",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/855_merge_source_subset_columns.sql",
      "read_script": "generator/spark-reads-df/verify_855_merge_source_subset_columns.py",
      "description": "MERGE where source CTE has fewer columns than target. Source only provides id + the 2 columns being updated. Tests column projection in MERGE -- the engine must not expect source to have all target columns.",
      "status": "pass",
      "duration_ms": 4277,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:05:04.759097+00:00",
      "read_cold_ms": 2746,
      "read_warm_ms": 591,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 42,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/856_merge_decimal_across_partitions",
      "num": 856,
      "name": "merge_decimal_across_partitions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/856_merge_decimal_across_partitions.sql",
      "read_script": "generator/spark-reads-df/verify_856_merge_decimal_across_partitions.py",
      "description": "MERGE on partitioned table where each partition has different DECIMAL value ranges (different magnitudes). Tests DECIMAL handling across partition boundaries -- each partition's Parquet files have different value ranges.",
      "status": "pass",
      "duration_ms": 6939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:05:11.698513+00:00",
      "read_cold_ms": 2955,
      "read_warm_ms": 3277,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 130,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/857_merge_after_dv_delete_typed",
      "num": 857,
      "name": "merge_after_dv_delete_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/857_merge_after_dv_delete_typed.sql",
      "read_script": "generator/spark-reads-df/verify_857_merge_after_dv_delete_typed.py",
      "description": "DELETE on typed columns (creates deletion vectors) then MERGE on same table. Tests that MERGE correctly reads DV-containing files with typed data -- the MERGE must skip DV-deleted rows when scanning for matches.",
      "status": "pass",
      "duration_ms": 1123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:47.037885+00:00",
      "read_cold_ms": 619,
      "read_warm_ms": 241,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 77,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/858_merge_typed_expressions",
      "num": 858,
      "name": "merge_typed_expressions",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/858_merge_typed_expressions.sql",
      "read_script": "generator/spark-reads-df/verify_858_merge_typed_expressions.py",
      "description": "MERGE SET with computed expressions combining multiple typed columns. Source CTE pre-computes subtotal, tax_amount, total from quantity * unit_price. Tests that DECIMAL columns written from computed expressions maintain precision.",
      "status": "pass",
      "duration_ms": 1439,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:48.477629+00:00",
      "read_cold_ms": 797,
      "read_warm_ms": 316,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 127,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/859_merge_timestamp_range_delete",
      "num": 859,
      "name": "merge_timestamp_range_delete",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/859_merge_timestamp_range_delete.sql",
      "read_script": "generator/spark-reads-df/verify_859_merge_timestamp_range_delete.py",
      "description": "MERGE with DELETE predicate based on TIMESTAMP range. Tests temporal predicate evaluation in the MERGE DELETE clause -- the engine must correctly compare microsecond timestamps in the WHEN MATCHED AND condition.",
      "status": "pass",
      "duration_ms": 5522,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:05:26.384063+00:00",
      "read_cold_ms": 3164,
      "read_warm_ms": 1170,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 52,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/85_optimize_zorder_file_layout",
      "num": 85,
      "name": "optimize_zorder_file_layout",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/85_optimize_zorder_file_layout.sql",
      "read_script": "generator/spark-reads-df/verify_85_optimize_zorder_file_layout.py",
      "description": "- OPTIMIZE command with Z-ORDER BY for multi-dimensional file layout - E-commerce customer analytics with 30 columns - 100,000 initial events + 10,000 new December events - Final logical row count: 111,516 (after deletes) - Decimal(10,2) for cart_value, transaction_value...",
      "status": "pass",
      "duration_ms": 33316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:05:59.700664+00:00",
      "read_cold_ms": 3702,
      "read_warm_ms": 1749,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 566,
      "write_warm_ms": 495,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/860_merge_boolean_multi_clause",
      "num": 860,
      "name": "merge_boolean_multi_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/860_merge_boolean_multi_clause.sql",
      "read_script": "generator/spark-reads-df/verify_860_merge_boolean_multi_clause.py",
      "description": "MERGE with BOOLEAN values driving 3 different WHEN MATCHED clauses plus a fallback. Tests BOOLEAN literal comparison (= true / = false) in MERGE predicate cascades.",
      "status": "pass",
      "duration_ms": 8597,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:08.298382+00:00",
      "read_cold_ms": 4020,
      "read_warm_ms": 966,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 80,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/861_merge_decimal_int_cross_type",
      "num": 861,
      "name": "merge_decimal_int_cross_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/861_merge_decimal_int_cross_type.sql",
      "read_script": "generator/spark-reads-df/verify_861_merge_decimal_int_cross_type.py",
      "description": "MERGE where SET expression uses CAST across types: INT column feeds DECIMAL calculation, DECIMAL feeds final DECIMAL. Tests cross-type arithmetic in MERGE source CTE with CAST chains.",
      "status": "pass",
      "duration_ms": 8383,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:16.682319+00:00",
      "read_cold_ms": 3999,
      "read_warm_ms": 999,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 35,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/862_merge_struct_and_scalar",
      "num": 862,
      "name": "merge_struct_and_scalar",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/862_merge_struct_and_scalar.sql",
      "read_script": "generator/spark-reads-df/verify_862_merge_struct_and_scalar.py",
      "description": "MERGE that updates both STRUCT and scalar columns in the same UPDATE SET. Tests mixed struct+scalar write -- the engine must handle nested Parquet encoding (struct) alongside flat columns (INT, DECIMAL) in a single MERGE UPDATE row write.",
      "status": "pass",
      "duration_ms": 1883,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:50.361548+00:00",
      "read_cold_ms": 772,
      "read_warm_ms": 216,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 188,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "storage:parquet-encoding",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/863_merge_cdc_all_types_exact",
      "num": 863,
      "name": "merge_cdc_all_types_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/863_merge_cdc_all_types_exact.sql",
      "read_script": "generator/spark-reads-df/verify_863_merge_cdc_all_types_exact.py",
      "description": "MERGE with all types + CDC (Change Data Feed), designed for exact CDF count verification per change type. Every row's fate is deterministic.",
      "status": "pass",
      "duration_ms": 1621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:51.983564+00:00",
      "read_cold_ms": 774,
      "read_warm_ms": 219,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 224,
      "write_warm_ms": 460,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/864_merge_nmbys_typed_complex",
      "num": 864,
      "name": "merge_nmbys_typed_complex",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/864_merge_nmbys_typed_complex.sql",
      "read_script": "generator/spark-reads-df/verify_864_merge_nmbys_typed_complex.py",
      "description": "WHEN NOT MATCHED BY SOURCE with complex typed update: sets DECIMAL to zero, BOOLEAN to false, STRING to 'orphaned', INT to -1. Tests that NOT MATCHED BY SOURCE correctly applies typed default values to orphaned rows.",
      "status": "pass",
      "duration_ms": 7864,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:36.176862+00:00",
      "read_cold_ms": 5272,
      "read_warm_ms": 1331,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 81,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/865_merge_delete_on_composite_typed_key",
      "num": 865,
      "name": "merge_delete_on_composite_typed_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/865_merge_delete_on_composite_typed_key.sql",
      "read_script": "generator/spark-reads-df/verify_865_merge_delete_on_composite_typed_key.py",
      "description": "MERGE with DELETE clause on composite typed key (STRING + INT). The ON clause joins on two columns of different types. Tests that the engine correctly evaluates composite key equality across types in MERGE.",
      "status": "pass",
      "duration_ms": 4372,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:40.551978+00:00",
      "read_cold_ms": 2446,
      "read_warm_ms": 847,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 85,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/866_merge_three_clause_all_types",
      "num": 866,
      "name": "merge_three_clause_all_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/866_merge_three_clause_all_types.sql",
      "read_script": "generator/spark-reads-df/verify_866_merge_three_clause_all_types.py",
      "description": "MERGE with 3 clauses (UPDATE, DELETE, INSERT) where each clause touches all column types. Every type goes through all three MERGE paths in one statement.",
      "status": "pass",
      "duration_ms": 7323,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:48.131339+00:00",
      "read_cold_ms": 3005,
      "read_warm_ms": 982,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 161,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/867_merge_update_then_merge_typed",
      "num": 867,
      "name": "merge_update_then_merge_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/867_merge_update_then_merge_typed.sql",
      "read_script": "generator/spark-reads-df/verify_867_merge_update_then_merge_typed.py",
      "description": "UPDATE on typed columns then MERGE on same table. Tests that MERGE reads correct post-UPDATE types -- the MERGE must see the UPDATE's modified DECIMAL and flipped BOOLEAN values, not the original V0 values.",
      "status": "pass",
      "duration_ms": 5991,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:54.124236+00:00",
      "read_cold_ms": 4107,
      "read_warm_ms": 787,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 69,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/868_merge_decimal_string_key",
      "num": 868,
      "name": "merge_decimal_string_key",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/868_merge_decimal_string_key.sql",
      "read_script": "generator/spark-reads-df/verify_868_merge_decimal_string_key.py",
      "description": "equality where one key column is DECIMAL (integer-valued but stored as DECIMAL) and the other is STRING.",
      "status": "pass",
      "duration_ms": 4985,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:06:59.114589+00:00",
      "read_cold_ms": 2668,
      "read_warm_ms": 1203,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 178,
      "write_warm_ms": 50,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/869_merge_five_clause",
      "num": 869,
      "name": "merge_five_clause",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/869_merge_five_clause.sql",
      "read_script": "generator/spark-reads-df/verify_869_merge_five_clause.py",
      "description": "1 NOT MATCHED INSERT, 1 NOT MATCHED BY SOURCE UPDATE. Maximum clause complexity with typed columns. Tests the engine's ability to handle the full MERGE clause repertoire in a single statement.",
      "status": "pass",
      "duration_ms": 8050,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:07.168110+00:00",
      "read_cold_ms": 5852,
      "read_warm_ms": 902,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 44,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/86_time_travel_timestamp_queries",
      "num": 86,
      "name": "time_travel_timestamp_queries",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/86_time_travel_timestamp_queries.sql",
      "read_script": "generator/spark-reads-df/verify_86_time_travel_timestamp_queries.py",
      "description": "Time travel queries using timestamps and versions.",
      "status": "pass",
      "duration_ms": 18802,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:25.972341+00:00",
      "read_cold_ms": 2934,
      "read_warm_ms": 839,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 373,
      "write_warm_ms": 437,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:time-travel",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/870_merge_partition_typed_nmbys",
      "num": 870,
      "name": "merge_partition_typed_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/870_merge_partition_typed_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_870_merge_partition_typed_nmbys.py",
      "description": "Partitioned MERGE with typed NOT MATCHED BY SOURCE + DECIMAL + BOOLEAN. Tests per-partition NOT MATCHED BY SOURCE with typed columns -- the AP partition has zero source rows, so all AP rows must be detected as orphaned and updated with typed defaults.",
      "status": "pass",
      "duration_ms": 5610,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:31.584039+00:00",
      "read_cold_ms": 2909,
      "read_warm_ms": 1431,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 178,
      "write_warm_ms": 96,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/871_merge_optimize_decimal",
      "num": 871,
      "name": "merge_optimize_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/871_merge_optimize_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_871_merge_optimize_decimal.py",
      "description": "MERGE on DECIMAL columns after OPTIMIZE. Tests DECIMAL reads from compacted files. OPTIMIZE rewrites small files into larger ones; the MERGE must then correctly read DECIMAL(10,2) values from the compacted Parquet files.",
      "status": "pass",
      "duration_ms": 5040,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:36.625032+00:00",
      "read_cold_ms": 2737,
      "read_warm_ms": 910,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 203,
      "write_warm_ms": 156,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/872_merge_optimize_timestamp",
      "num": 872,
      "name": "merge_optimize_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/872_merge_optimize_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_872_merge_optimize_timestamp.py",
      "description": "MERGE on TIMESTAMP after OPTIMIZE. Tests that TIMESTAMP values survive file compaction and are correctly read during MERGE predicate evaluation.",
      "status": "pass",
      "duration_ms": 7158,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:43.786684+00:00",
      "read_cold_ms": 4757,
      "read_warm_ms": 902,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 296,
      "write_warm_ms": 191,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/873_merge_optimize_boolean_int",
      "num": 873,
      "name": "merge_optimize_boolean_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/873_merge_optimize_boolean_int.sql",
      "read_script": "generator/spark-reads-df/verify_873_merge_optimize_boolean_int.py",
      "description": "MERGE on BOOLEAN+INT after OPTIMIZE. Tests that BOOLEAN and INT values survive file compaction and are correctly updated during MERGE.",
      "status": "pass",
      "duration_ms": 8077,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:51.865667+00:00",
      "read_cold_ms": 3115,
      "read_warm_ms": 3423,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 96,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/874_merge_evolve_decimal",
      "num": 874,
      "name": "merge_evolve_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/874_merge_evolve_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_874_merge_evolve_decimal.py",
      "description": "Schema evolution adds DECIMAL column, then MERGE populates it. Tests that a newly added DECIMAL(10,2) column can be written through MERGE UPDATE SET and INSERT, even though existing rows have NULL for the new column.",
      "status": "pass",
      "duration_ms": 3978,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:07:55.845415+00:00",
      "read_cold_ms": 2597,
      "read_warm_ms": 689,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 130,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/875_merge_evolve_timestamp",
      "num": 875,
      "name": "merge_evolve_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/875_merge_evolve_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_875_merge_evolve_timestamp.py",
      "description": "Schema evolution adds TIMESTAMP column, then MERGE populates it. Tests that a newly added TIMESTAMP column can be written through MERGE even when old Parquet files do not contain the column.",
      "status": "pass",
      "duration_ms": 9285,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:08:05.132631+00:00",
      "read_cold_ms": 3299,
      "read_warm_ms": 4387,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 38,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/876_merge_evolve_boolean",
      "num": 876,
      "name": "merge_evolve_boolean",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/876_merge_evolve_boolean.sql",
      "read_script": "generator/spark-reads-df/verify_876_merge_evolve_boolean.py",
      "description": "Schema evolution adds BOOLEAN column, then MERGE populates it. Tests that a newly added BOOLEAN column can be set through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 1373,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:53.357273+00:00",
      "read_cold_ms": 831,
      "read_warm_ms": 267,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 46,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/877_merge_constraint_decimal_range",
      "num": 877,
      "name": "merge_constraint_decimal_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/877_merge_constraint_decimal_range.sql",
      "read_script": "generator/spark-reads-df/verify_877_merge_constraint_decimal_range.py",
      "description": "CHECK constraint on DECIMAL range + MERGE must respect it. Tests that MERGE-written DECIMAL values satisfy the constraint boundaries.",
      "status": "pass",
      "duration_ms": 1283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:54.640787+00:00",
      "read_cold_ms": 746,
      "read_warm_ms": 260,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 58,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/878_merge_constraint_int_positive",
      "num": 878,
      "name": "merge_constraint_int_positive",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/878_merge_constraint_int_positive.sql",
      "read_script": "generator/spark-reads-df/verify_878_merge_constraint_int_positive.py",
      "description": "CHECK constraint on INT positivity + MERGE. Tests that MERGE-written INT values pass the >= 0 constraint for both UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 1246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:55.888131+00:00",
      "read_cold_ms": 656,
      "read_warm_ms": 244,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/879_merge_cdc_typed_predicates",
      "num": 879,
      "name": "merge_cdc_typed_predicates",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/879_merge_cdc_typed_predicates.sql",
      "read_script": "generator/spark-reads-df/verify_879_merge_cdc_typed_predicates.py",
      "description": "MERGE with typed predicates in CDC context. The WHEN MATCHED clause uses a typed predicate (amount > CAST(500 AS DECIMAL(10,2))) to route rows to different UPDATE paths. CDF must capture type-correct pre/post images.",
      "status": "pass",
      "duration_ms": 9084,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:08:34.392904+00:00",
      "read_cold_ms": 4070,
      "read_warm_ms": 635,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 119,
      "write_warm_ms": 181,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/87_empty_table_no_data_files",
      "num": 87,
      "name": "empty_table_no_data_files",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/87_empty_table_no_data_files.sql",
      "read_script": "generator/spark-reads-df/verify_87_empty_table_no_data_files.py",
      "description": "- Empty table with protocol and metadata but zero data files - Schema-only table definition (25 columns) - Deletion vectors enabled - Data warehouse fact table schema",
      "status": "pass",
      "duration_ms": 4435,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:08:38.829439+00:00",
      "read_cold_ms": 2907,
      "read_warm_ms": 739,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 3,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/880_merge_cdc_nmbys_typed",
      "num": 880,
      "name": "merge_cdc_nmbys_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/880_merge_cdc_nmbys_typed.sql",
      "read_script": "generator/spark-reads-df/verify_880_merge_cdc_nmbys_typed.py",
      "description": "CDC + NOT MATCHED BY SOURCE with typed UPDATE. CDF must capture the NM-BY-SOURCE changes as update_preimage/update_postimage pairs with correct DECIMAL and BOOLEAN types.",
      "status": "pass",
      "duration_ms": 7984,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:08:46.815704+00:00",
      "read_cold_ms": 2725,
      "read_warm_ms": 898,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 289,
      "write_warm_ms": 182,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/881_merge_colmap_decimal",
      "num": 881,
      "name": "merge_colmap_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/881_merge_colmap_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_881_merge_colmap_decimal.py",
      "description": "Column mapping (name mode) + DECIMAL through MERGE. Tests that DECIMAL(12,4) values are correctly written and read when column mapping rewrites physical column names in Parquet.",
      "status": "pass",
      "duration_ms": 4707,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:08:51.524703+00:00",
      "read_cold_ms": 2934,
      "read_warm_ms": 1006,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 91,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/882_merge_colmap_timestamp",
      "num": 882,
      "name": "merge_colmap_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/882_merge_colmap_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_882_merge_colmap_timestamp.py",
      "description": "Column mapping (name mode) + TIMESTAMP through MERGE. Tests that TIMESTAMP values survive column mapping's physical name rewriting during MERGE.",
      "status": "pass",
      "duration_ms": 1387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:57.275756+00:00",
      "read_cold_ms": 795,
      "read_warm_ms": 277,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 61,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/883_merge_colmap_struct",
      "num": 883,
      "name": "merge_colmap_struct",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/883_merge_colmap_struct.sql",
      "read_script": "generator/spark-reads-df/verify_883_merge_colmap_struct.py",
      "description": "Column mapping (name mode) + STRUCT through MERGE. Tests that nested STRUCT fields are correctly mapped through column mapping's physical name rewriting.",
      "status": "pass",
      "duration_ms": 1622,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:36:58.898015+00:00",
      "read_cold_ms": 707,
      "read_warm_ms": 269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 112,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/884_merge_three_decimal_ops",
      "num": 884,
      "name": "merge_three_decimal_ops",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/884_merge_three_decimal_ops.sql",
      "read_script": "generator/spark-reads-df/verify_884_merge_three_decimal_ops.py",
      "description": "Three sequential MERGEs each modifying DECIMAL differently. Tests DECIMAL stability across 3 consecutive rewrite cycles: +10, *2, -5.",
      "status": "pass",
      "duration_ms": 6634,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:09.323594+00:00",
      "read_cold_ms": 3860,
      "read_warm_ms": 1121,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 66,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/885_merge_interleaved_update",
      "num": 885,
      "name": "merge_interleaved_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/885_merge_interleaved_update.sql",
      "read_script": "generator/spark-reads-df/verify_885_merge_interleaved_update.py",
      "description": "UPDATE then MERGE then UPDATE then MERGE. Tests 4-operation typed interleave where standalone UPDATEs and MERGEs alternate, each modifying typed columns.",
      "status": "pass",
      "duration_ms": 4939,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:14.264124+00:00",
      "read_cold_ms": 2312,
      "read_warm_ms": 1051,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 103,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/886_merge_delete_reinsert_typed",
      "num": 886,
      "name": "merge_delete_reinsert_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/886_merge_delete_reinsert_typed.sql",
      "read_script": "generator/spark-reads-df/verify_886_merge_delete_reinsert_typed.py",
      "description": "MERGE DELETE all matched rows, then separate INSERT of typed data. Tests that typed INSERT works correctly after a MERGE-DELETE has cleared the table.",
      "status": "pass",
      "duration_ms": 1211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:00.109252+00:00",
      "read_cold_ms": 644,
      "read_warm_ms": 287,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 137,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/887_merge_wide_typed",
      "num": 887,
      "name": "merge_wide_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/887_merge_wide_typed.sql",
      "read_script": "generator/spark-reads-df/verify_887_merge_wide_typed.py",
      "description": "MERGE on 15-column table with diverse types. Tests wide typed schema through MERGE where all 14 non-key columns are updated or inserted.",
      "status": "pass",
      "duration_ms": 6413,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:25.657534+00:00",
      "read_cold_ms": 4316,
      "read_warm_ms": 788,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 99,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:wide-schema",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/888_merge_decimal_rounding",
      "num": 888,
      "name": "merge_decimal_rounding",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/888_merge_decimal_rounding.sql",
      "read_script": "generator/spark-reads-df/verify_888_merge_decimal_rounding.py",
      "description": "MERGE where DECIMAL computation requires rounding. Tests that rounding behavior is correct when computing subtotal = ROUND(price * qty, 2).",
      "status": "pass",
      "duration_ms": 4212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:29.872076+00:00",
      "read_cold_ms": 2407,
      "read_warm_ms": 795,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 73,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/889_merge_timestamp_ordering",
      "num": 889,
      "name": "merge_timestamp_ordering",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/889_merge_timestamp_ordering.sql",
      "read_script": "generator/spark-reads-df/verify_889_merge_timestamp_ordering.py",
      "description": "MERGE with timestamps that maintain ordering in target but are reversed in source. After MERGE, timestamps are non-monotonic. Tests that the engine does not assume or enforce timestamp ordering.",
      "status": "pass",
      "duration_ms": 1238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:01.348444+00:00",
      "read_cold_ms": 707,
      "read_warm_ms": 262,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 38,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/88_single_record_minimal_table",
      "num": 88,
      "name": "single_record_minimal_table",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/88_single_record_minimal_table.sql",
      "read_script": "generator/spark-reads-df/verify_88_single_record_minimal_table.py",
      "description": "- Table with exactly one record (minimal data scenario) - Statistics where min = max for all columns - Single Parquet file with one row group - Multiple UPDATE operations incrementing config_version - Decimal, boolean, timestamp, date types - Deletion vectors enabled",
      "status": "pass",
      "duration_ms": 7420,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:42.600428+00:00",
      "read_cold_ms": 2935,
      "read_warm_ms": 3152,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 93,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "storage:rowgroup-stats",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/890_merge_bool_int_partition",
      "num": 890,
      "name": "merge_bool_int_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/890_merge_bool_int_partition.sql",
      "read_script": "generator/spark-reads-df/verify_890_merge_bool_int_partition.py",
      "description": "MERGE on partitioned table with BOOLEAN and INT typed updates. Tests that partition-aware MERGE correctly updates BOOLEAN and INT columns across multiple partitions, including inserting new rows into existing partitions.",
      "status": "pass",
      "duration_ms": 7439,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:50.040979+00:00",
      "read_cold_ms": 3956,
      "read_warm_ms": 689,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 215,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/891_merge_decimal_cdc_nmbys",
      "num": 891,
      "name": "merge_decimal_cdc_nmbys",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/891_merge_decimal_cdc_nmbys.sql",
      "read_script": "generator/spark-reads-df/verify_891_merge_decimal_cdc_nmbys.py",
      "description": "DECIMAL + CDC + NOT MATCHED BY SOURCE. Three-way type-aware test combining DECIMAL precision with CDF capture and orphan detection.",
      "status": "pass",
      "duration_ms": 6476,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:09:56.518581+00:00",
      "read_cold_ms": 3672,
      "read_warm_ms": 1230,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 197,
      "write_warm_ms": 246,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/892_merge_timestamp_evolve_cdc",
      "num": 892,
      "name": "merge_timestamp_evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/892_merge_timestamp_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_892_merge_timestamp_evolve_cdc.py",
      "description": "TIMESTAMP + schema evolution + CDC + MERGE. Three-way combination: evolve schema to add TIMESTAMP, then MERGE populates it, all with CDF enabled.",
      "status": "pass",
      "duration_ms": 2015,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:03.364289+00:00",
      "read_cold_ms": 799,
      "read_warm_ms": 636,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 234,
      "write_warm_ms": 266,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/893_merge_struct_cdc",
      "num": 893,
      "name": "merge_struct_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/893_merge_struct_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_893_merge_struct_cdc.py",
      "description": "STRUCT + CDC + MERGE. Tests that STRUCT values are correctly captured in CDF update_preimage and update_postimage records.",
      "status": "pass",
      "duration_ms": 1411,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:04.776096+00:00",
      "read_cold_ms": 676,
      "read_warm_ms": 223,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 194,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/894_merge_all_clauses_all_types",
      "num": 894,
      "name": "merge_all_clauses_all_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/894_merge_all_clauses_all_types.sql",
      "read_script": "generator/spark-reads-df/verify_894_merge_all_clauses_all_types.py",
      "description": "MERGE with DELETE+UPDATE+INSERT+NM-BY-SOURCE all touching typed columns. 4 clauses x multiple types in a single MERGE statement.",
      "status": "pass",
      "duration_ms": 1247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:06.023979+00:00",
      "read_cold_ms": 709,
      "read_warm_ms": 298,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 52,
      "write_warm_ms": 52,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/895_merge_partition_evolve_typed",
      "num": 895,
      "name": "merge_partition_evolve_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/895_merge_partition_evolve_typed.sql",
      "read_script": "generator/spark-reads-df/verify_895_merge_partition_evolve_typed.py",
      "description": "Partition + schema evolution + typed MERGE. Three-way combination where a partitioned table gets a new DECIMAL column via evolution, then MERGE populates it across all partitions.",
      "status": "pass",
      "duration_ms": 4602,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:10:20.441690+00:00",
      "read_cold_ms": 2506,
      "read_warm_ms": 1043,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 207,
      "write_warm_ms": 130,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/896_merge_colmap_cdc_typed",
      "num": 896,
      "name": "merge_colmap_cdc_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/896_merge_colmap_cdc_typed.sql",
      "read_script": "generator/spark-reads-df/verify_896_merge_colmap_cdc_typed.py",
      "description": "Column mapping + CDC + typed MERGE. Three-way combination where column mapping rewrites physical names and CDF must capture typed columns using the logical (not physical) column names.",
      "status": "pass",
      "duration_ms": 1457,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:07.482121+00:00",
      "read_cold_ms": 713,
      "read_warm_ms": 294,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 305,
      "write_warm_ms": 598,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/897_merge_constraint_evolve_typed",
      "num": 897,
      "name": "merge_constraint_evolve_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/897_merge_constraint_evolve_typed.sql",
      "read_script": "generator/spark-reads-df/verify_897_merge_constraint_evolve_typed.py",
      "description": "Constraint + schema evolution + typed MERGE. Three-way combination where a CHECK constraint exists, then a new DECIMAL column is added via evolution, and MERGE must satisfy the constraint while populating the new column.",
      "status": "pass",
      "duration_ms": 5806,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:10:30.632457+00:00",
      "read_cold_ms": 3608,
      "read_warm_ms": 1077,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 126,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/898_merge_typed_idempotent",
      "num": 898,
      "name": "merge_typed_idempotent",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/898_merge_typed_idempotent.sql",
      "read_script": "generator/spark-reads-df/verify_898_merge_typed_idempotent.py",
      "description": "Two identical MERGEs in sequence (idempotent test). Same source applied twice. Final state should be the same as after the first MERGE. Tests that MERGE is idempotent when source data does not change.",
      "status": "pass",
      "duration_ms": 1426,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:08.908694+00:00",
      "read_cold_ms": 735,
      "read_warm_ms": 284,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 145,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/899_merge_large_typed",
      "num": 899,
      "name": "merge_large_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/899_merge_large_typed.sql",
      "read_script": "generator/spark-reads-df/verify_899_merge_large_typed.py",
      "description": "scale combined with typed columns to catch performance-sensitive type bugs that only manifest at higher row counts (e.g., buffer overflow in DECIMAL encoding, or TIMESTAMP batch alignment issues).",
      "status": "pass",
      "duration_ms": 1277,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:10.186185+00:00",
      "read_cold_ms": 659,
      "read_warm_ms": 278,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 63,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/89_statistics_special_numeric_values",
      "num": 89,
      "name": "statistics_special_numeric_values",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/89_statistics_special_numeric_values.sql",
      "read_script": "generator/spark-reads-df/verify_89_statistics_special_numeric_values.py",
      "description": "Demonstrates statistics containing special floating-point values. Tests how Delta handles IEEE 754 special values in statistics: - NaN (Not a Number) - Positive Infinity (+Inf) - Negative Infinity (-Inf) - Negative zero (-0.0) - Subnormal/denormalized numbers",
      "status": "pass",
      "duration_ms": 3841,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:10:48.924764+00:00",
      "read_cold_ms": 2592,
      "read_warm_ms": 586,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 222,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/900_merge_ultimate_complex",
      "num": 900,
      "name": "merge_ultimate_complex",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/900_merge_ultimate_complex.sql",
      "read_script": "generator/spark-reads-df/verify_900_merge_ultimate_complex.py",
      "description": "partition + constraint + schema evolution + OPTIMIZE. This is the most complex single-MERGE test in the suite, combining every feature that could interact with typed columns.",
      "status": "pass",
      "duration_ms": 1830,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:12.017176+00:00",
      "read_cold_ms": 743,
      "read_warm_ms": 243,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 389,
      "write_warm_ms": 860,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/901_update_int_arithmetic",
      "num": 901,
      "name": "update_int_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/901_update_int_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_901_update_int_arithmetic.py",
      "description": "UPDATE with INT arithmetic expressions (add, subtract,",
      "status": "pass",
      "duration_ms": 5907,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:01.230581+00:00",
      "read_cold_ms": 3989,
      "read_warm_ms": 973,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 131,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/902_update_double_arithmetic",
      "num": 902,
      "name": "update_double_arithmetic",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/902_update_double_arithmetic.sql",
      "read_script": "generator/spark-reads-df/verify_902_update_double_arithmetic.py",
      "description": "UPDATE with DOUBLE arithmetic expressions (multiply,",
      "status": "pass",
      "duration_ms": 7239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:08.470925+00:00",
      "read_cold_ms": 3000,
      "read_warm_ms": 3375,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 46,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/903_update_decimal_add",
      "num": 903,
      "name": "update_decimal_add",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/903_update_decimal_add.sql",
      "read_script": "generator/spark-reads-df/verify_903_update_decimal_add.py",
      "description": "UPDATE DECIMAL(10,2) with addition. Tests that DECIMAL",
      "status": "pass",
      "duration_ms": 4004,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:12.476250+00:00",
      "read_cold_ms": 2707,
      "read_warm_ms": 731,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 46,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/904_update_decimal_multiply",
      "num": 904,
      "name": "update_decimal_multiply",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/904_update_decimal_multiply.sql",
      "read_script": "generator/spark-reads-df/verify_904_update_decimal_multiply.py",
      "description": "UPDATE DECIMAL with multiplication. Tests that DECIMAL",
      "status": "pass",
      "duration_ms": 3842,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:16.319430+00:00",
      "read_cold_ms": 1805,
      "read_warm_ms": 738,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 63,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/905_update_decimal_four_scales",
      "num": 905,
      "name": "update_decimal_four_scales",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/905_update_decimal_four_scales.sql",
      "read_script": "generator/spark-reads-df/verify_905_update_decimal_four_scales.py",
      "description": "UPDATE four DECIMAL columns with different scales",
      "status": "pass",
      "duration_ms": 4257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:20.577353+00:00",
      "read_cold_ms": 3013,
      "read_warm_ms": 523,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 40,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/906_update_timestamp_shift",
      "num": 906,
      "name": "update_timestamp_shift",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/906_update_timestamp_shift.sql",
      "read_script": "generator/spark-reads-df/verify_906_update_timestamp_shift.py",
      "description": "UPDATE TIMESTAMP by shifting microsecond offsets. Tests",
      "status": "pass",
      "duration_ms": 6327,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:26.905853+00:00",
      "read_cold_ms": 2118,
      "read_warm_ms": 2935,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 28,
      "write_warm_ms": 37,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/907_update_timestamp_to_fixed",
      "num": 907,
      "name": "update_timestamp_to_fixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/907_update_timestamp_to_fixed.sql",
      "read_script": "generator/spark-reads-df/verify_907_update_timestamp_to_fixed.py",
      "description": "UPDATE TIMESTAMP to a single fixed value. Tests that",
      "status": "pass",
      "duration_ms": 5982,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:32.890367+00:00",
      "read_cold_ms": 2829,
      "read_warm_ms": 739,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/908_update_date_shift",
      "num": 908,
      "name": "update_date_shift",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/908_update_date_shift.sql",
      "read_script": "generator/spark-reads-df/verify_908_update_date_shift.py",
      "description": "UPDATE DATE column (Date32) by shifting values forward",
      "status": "pass",
      "duration_ms": 4391,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:37.284217+00:00",
      "read_cold_ms": 2444,
      "read_warm_ms": 1151,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 36,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/909_update_boolean_flip",
      "num": 909,
      "name": "update_boolean_flip",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/909_update_boolean_flip.sql",
      "read_script": "generator/spark-reads-df/verify_909_update_boolean_flip.py",
      "description": "UPDATE that flips BOOLEAN columns using CASE expressions.",
      "status": "pass",
      "duration_ms": 7547,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:11:44.832797+00:00",
      "read_cold_ms": 5589,
      "read_warm_ms": 1273,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 31,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/90_null_only_column_statistics",
      "num": 90,
      "name": "null_only_column_statistics",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/90_null_only_column_statistics.sql",
      "read_script": "generator/spark-reads-df/verify_90_null_only_column_statistics.py",
      "description": "- Statistics for columns containing only NULL values - Tests how Delta handles statistics when: - All values in a column are NULL - nullCount equals numRecords - minValues and maxValues are absent or null",
      "status": "pass",
      "duration_ms": 12182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:24.200214+00:00",
      "read_cold_ms": 840,
      "read_warm_ms": 314,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 67,
      "tags": [
        "type:binary",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/910_update_boolean_conditional",
      "num": 910,
      "name": "update_boolean_conditional",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/910_update_boolean_conditional.sql",
      "read_script": "generator/spark-reads-df/verify_910_update_boolean_conditional.py",
      "description": "UPDATE SET BOOLEAN based on numeric predicate. Tests",
      "status": "pass",
      "duration_ms": 5765,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:12:44.980894+00:00",
      "read_cold_ms": 3515,
      "read_warm_ms": 1204,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 30,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/911_update_string_concat",
      "num": 911,
      "name": "update_string_concat",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/911_update_string_concat.sql",
      "read_script": "generator/spark-reads-df/verify_911_update_string_concat.py",
      "description": "UPDATE STRING columns with CONCAT expressions. Tests",
      "status": "pass",
      "duration_ms": 8357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:12:53.339577+00:00",
      "read_cold_ms": 3192,
      "read_warm_ms": 4030,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 135,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/912_update_string_cast",
      "num": 912,
      "name": "update_string_cast",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/912_update_string_cast.sql",
      "read_script": "generator/spark-reads-df/verify_912_update_string_cast.py",
      "description": "UPDATE STRING columns from numeric values via CAST.",
      "status": "pass",
      "duration_ms": 7210,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:13:00.551212+00:00",
      "read_cold_ms": 2684,
      "read_warm_ms": 374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 58,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/913_update_to_null_per_type",
      "num": 913,
      "name": "update_to_null_per_type",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/913_update_to_null_per_type.sql",
      "read_script": "generator/spark-reads-df/verify_913_update_to_null_per_type.py",
      "description": "UPDATE each data type column to NULL individually.",
      "status": "pass",
      "duration_ms": 5282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:13:05.834820+00:00",
      "read_cold_ms": 3020,
      "read_warm_ms": 927,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 270,
      "write_warm_ms": 300,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/914_update_from_null_to_value",
      "num": 914,
      "name": "update_from_null_to_value",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/914_update_from_null_to_value.sql",
      "read_script": "generator/spark-reads-df/verify_914_update_from_null_to_value.py",
      "description": "UPDATE that replaces NULL with typed values. Tests the",
      "status": "pass",
      "duration_ms": 4774,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:13:10.610133+00:00",
      "read_cold_ms": 3197,
      "read_warm_ms": 828,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 115,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/915_update_struct_preserve",
      "num": 915,
      "name": "update_struct_preserve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/915_update_struct_preserve.sql",
      "read_script": "generator/spark-reads-df/verify_915_update_struct_preserve.py",
      "description": "UPDATE scalar columns on a table that contains a STRUCT",
      "status": "pass",
      "duration_ms": 1655,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:25.855889+00:00",
      "read_cold_ms": 773,
      "read_warm_ms": 215,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 134,
      "write_warm_ms": 56,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/916_update_decimal_negative",
      "num": 916,
      "name": "update_decimal_negative",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/916_update_decimal_negative.sql",
      "read_script": "generator/spark-reads-df/verify_916_update_decimal_negative.py",
      "description": "UPDATE DECIMAL to negative values and zero. Tests sign",
      "status": "pass",
      "duration_ms": 1281,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:27.137332+00:00",
      "read_cold_ms": 795,
      "read_warm_ms": 242,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 48,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/917_update_decimal_boundary",
      "num": 917,
      "name": "update_decimal_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/917_update_decimal_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_917_update_decimal_boundary.py",
      "description": "UPDATE DECIMAL at precision boundaries. DECIMAL(5,2)",
      "status": "pass",
      "duration_ms": 3800,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:13:30.694928+00:00",
      "read_cold_ms": 2273,
      "read_warm_ms": 658,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 131,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/918_update_int_boundary",
      "num": 918,
      "name": "update_int_boundary",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/918_update_int_boundary.sql",
      "read_script": "generator/spark-reads-df/verify_918_update_int_boundary.py",
      "description": "UPDATE INT near max/min boundaries. Tests that the",
      "status": "pass",
      "duration_ms": 5169,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:13:35.866092+00:00",
      "read_cold_ms": 2953,
      "read_warm_ms": 1331,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 60,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/919_update_double_extremes",
      "num": 919,
      "name": "update_double_extremes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/919_update_double_extremes.sql",
      "read_script": "generator/spark-reads-df/verify_919_update_double_extremes.py",
      "description": "UPDATE DOUBLE with very small and very large values.",
      "status": "pass",
      "duration_ms": 1351,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:28.489086+00:00",
      "read_cold_ms": 859,
      "read_warm_ms": 254,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 43,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/91_utf8_special_characters_paths",
      "num": 91,
      "name": "utf8_special_characters_paths",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/91_utf8_special_characters_paths.sql",
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    {
      "id": "df-writes/delta/937_update_boolean_from_comparison",
      "num": 937,
      "name": "update_boolean_from_comparison",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_937_update_boolean_from_comparison.py",
      "description": "UPDATE SET BOOLEAN columns from various comparison expressions",
      "status": "pass",
      "duration_ms": 6304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:16:07.523846+00:00",
      "read_cold_ms": 2481,
      "read_warm_ms": 579,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 29,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/938_update_preserve_six_types",
      "num": 938,
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "UPDATE only 1 column (tag), verifying that 6 other typed",
      "status": "pass",
      "duration_ms": 1252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:33.451638+00:00",
      "read_cold_ms": 728,
      "read_warm_ms": 229,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 31,
      "write_warm_ms": 30,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
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        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/939_update_decimal_cdc",
      "num": 939,
      "name": "update_decimal_cdc",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_939_update_decimal_cdc.py",
      "description": "UPDATE DECIMAL columns with CDC (Change Data Feed) enabled.",
      "status": "pass",
      "duration_ms": 6529,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:16:18.781547+00:00",
      "read_cold_ms": 4658,
      "read_warm_ms": 765,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 30,
      "write_warm_ms": 29,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
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        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
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        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/93_data_change_flag_scenarios",
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      "name": "data_change_flag_scenarios",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/93_data_change_flag_scenarios.sql",
      "read_script": "generator/spark-reads-df/verify_93_data_change_flag_scenarios.py",
      "description": "The dataChange flag in add and remove actions.",
      "status": "pass",
      "duration_ms": 16003,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:16:34.786465+00:00",
      "read_cold_ms": 1955,
      "read_warm_ms": 574,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 313,
      "write_warm_ms": 322,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
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        "dml:merge",
        "dml:overwrite",
        "dml:update",
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        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/940_update_timestamp_cdc",
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      "name": "update_timestamp_cdc",
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      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/940_update_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_940_update_timestamp_cdc.py",
      "description": "UPDATE TIMESTAMP column with CDC (Change Data Feed) enabled.",
      "status": "pass",
      "duration_ms": 8326,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:16:43.114530+00:00",
      "read_cold_ms": 5379,
      "read_warm_ms": 1118,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 49,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_941_update_decimal_optimize.py",
      "description": "UPDATE DECIMAL(10,2) column then OPTIMIZE. Tests that",
      "status": "pass",
      "duration_ms": 6750,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:16:49.866407+00:00",
      "read_cold_ms": 2552,
      "read_warm_ms": 3181,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 141,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/942_update_timestamp_optimize",
      "num": 942,
      "name": "update_timestamp_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-df/verify_942_update_timestamp_optimize.py",
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      "status": "pass",
      "duration_ms": 3624,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:16:53.492056+00:00",
      "read_cold_ms": 1567,
      "read_warm_ms": 886,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 205,
      "write_warm_ms": 162,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/943_update_boolean_optimize",
      "num": 943,
      "name": "update_boolean_optimize",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/943_update_boolean_optimize.sql",
      "read_script": "generator/spark-reads-df/verify_943_update_boolean_optimize.py",
      "description": "UPDATE BOOLEAN column then OPTIMIZE. Tests that BOOLEAN",
      "status": "pass",
      "duration_ms": 985,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:34.437200+00:00",
      "read_cold_ms": 506,
      "read_warm_ms": 163,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 256,
      "write_warm_ms": 99,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/944_update_decimal_constraint",
      "num": 944,
      "name": "update_decimal_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/944_update_decimal_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_944_update_decimal_constraint.py",
      "description": "UPDATE DECIMAL(10,2) column respecting a CHECK constraint.",
      "status": "pass",
      "duration_ms": 7449,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:03.414044+00:00",
      "read_cold_ms": 3668,
      "read_warm_ms": 693,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 39,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/945_update_int_constraint",
      "num": 945,
      "name": "update_int_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/945_update_int_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_945_update_int_constraint.py",
      "description": "UPDATE INT column respecting a CHECK constraint.",
      "status": "pass",
      "duration_ms": 5274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:08.689517+00:00",
      "read_cold_ms": 2444,
      "read_warm_ms": 1660,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 35,
      "write_warm_ms": 135,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/946_update_decimal_colmap",
      "num": 946,
      "name": "update_decimal_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/946_update_decimal_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_946_update_decimal_colmap.py",
      "description": "UPDATE DECIMAL(12,4) column with column mapping (name mode).",
      "status": "pass",
      "duration_ms": 5501,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:14.192395+00:00",
      "read_cold_ms": 2824,
      "read_warm_ms": 1188,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 67,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/947_update_timestamp_colmap",
      "num": 947,
      "name": "update_timestamp_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/947_update_timestamp_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_947_update_timestamp_colmap.py",
      "description": "UPDATE TIMESTAMP column with column mapping (name mode).",
      "status": "pass",
      "duration_ms": 4598,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:18.795508+00:00",
      "read_cold_ms": 2703,
      "read_warm_ms": 675,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 77,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/948_update_evolve_then_update",
      "num": 948,
      "name": "update_evolve_then_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/948_update_evolve_then_update.sql",
      "read_script": "generator/spark-reads-df/verify_948_update_evolve_then_update.py",
      "description": "ADD COLUMN (DECIMAL) via schema evolution then UPDATE",
      "status": "pass",
      "duration_ms": 6651,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:25.448636+00:00",
      "read_cold_ms": 2692,
      "read_warm_ms": 2726,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 101,
      "write_warm_ms": 102,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/949_update_evolve_timestamp",
      "num": 949,
      "name": "update_evolve_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/949_update_evolve_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_949_update_evolve_timestamp.py",
      "description": "ADD TIMESTAMP column via schema evolution then UPDATE it.",
      "status": "pass",
      "duration_ms": 4864,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:30.313984+00:00",
      "read_cold_ms": 2779,
      "read_warm_ms": 942,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/94_set_transaction_idempotent_writes",
      "num": 94,
      "name": "set_transaction_idempotent_writes",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/94_set_transaction_idempotent_writes.sql",
      "read_script": "generator/spark-reads-df/verify_94_set_transaction_idempotent_writes.py",
      "description": "Schema (15 columns) for streaming events with idempotent writes",
      "status": "pass",
      "duration_ms": 10363,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:40.678754+00:00",
      "read_cold_ms": 1916,
      "read_warm_ms": 671,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 51,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/950_update_evolve_boolean",
      "num": 950,
      "name": "update_evolve_boolean",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/950_update_evolve_boolean.sql",
      "read_script": "generator/spark-reads-df/verify_950_update_evolve_boolean.py",
      "description": "ADD BOOLEAN column via schema evolution then UPDATE it",
      "status": "pass",
      "duration_ms": 1523,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:35.960460+00:00",
      "read_cold_ms": 840,
      "read_warm_ms": 251,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 41,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/951_update_then_delete_typed",
      "num": 951,
      "name": "update_then_delete_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/951_update_then_delete_typed.sql",
      "read_script": "generator/spark-reads-df/verify_951_update_then_delete_typed.py",
      "description": "UPDATE typed columns (DECIMAL+TIMESTAMP+BOOLEAN) then",
      "status": "pass",
      "duration_ms": 8375,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:53.612812+00:00",
      "read_cold_ms": 2850,
      "read_warm_ms": 1269,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 49,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/952_update_decimal_partition",
      "num": 952,
      "name": "update_decimal_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/952_update_decimal_partition.sql",
      "read_script": "generator/spark-reads-df/verify_952_update_decimal_partition.py",
      "description": "UPDATE DECIMAL(10,2) column on a partitioned table.",
      "status": "pass",
      "duration_ms": 5259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:17:58.872870+00:00",
      "read_cold_ms": 3114,
      "read_warm_ms": 1343,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 148,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/953_update_timestamp_partition",
      "num": 953,
      "name": "update_timestamp_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/953_update_timestamp_partition.sql",
      "read_script": "generator/spark-reads-df/verify_953_update_timestamp_partition.py",
      "description": "UPDATE TIMESTAMP column on a partitioned table.",
      "status": "pass",
      "duration_ms": 3445,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:02.318777+00:00",
      "read_cold_ms": 1906,
      "read_warm_ms": 619,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 162,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/954_update_boolean_partition",
      "num": 954,
      "name": "update_boolean_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/954_update_boolean_partition.sql",
      "read_script": "generator/spark-reads-df/verify_954_update_boolean_partition.py",
      "description": "UPDATE BOOLEAN column on a partitioned table.",
      "status": "pass",
      "duration_ms": 4518,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:06.837636+00:00",
      "read_cold_ms": 2616,
      "read_warm_ms": 1085,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 99,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/955_update_all_types_partition",
      "num": 955,
      "name": "update_all_types_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/955_update_all_types_partition.sql",
      "read_script": "generator/spark-reads-df/verify_955_update_all_types_partition.py",
      "description": "UPDATE different typed columns per partition on a",
      "status": "pass",
      "duration_ms": 6329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:13.167723+00:00",
      "read_cold_ms": 2026,
      "read_warm_ms": 3209,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
      "write_warm_ms": 176,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/956_update_decimal_cdc_exact",
      "num": 956,
      "name": "update_decimal_cdc_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/956_update_decimal_cdc_exact.sql",
      "read_script": "generator/spark-reads-df/verify_956_update_decimal_cdc_exact.py",
      "description": "UPDATE DECIMAL with CDC enabled, exact CDF row counts.",
      "status": "pass",
      "duration_ms": 6447,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:19.618100+00:00",
      "read_cold_ms": 2392,
      "read_warm_ms": 879,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 198,
      "write_warm_ms": 75,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/957_update_mixed_cdc_exact",
      "num": 957,
      "name": "update_mixed_cdc_exact",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/957_update_mixed_cdc_exact.sql",
      "read_script": "generator/spark-reads-df/verify_957_update_mixed_cdc_exact.py",
      "description": "UPDATE multiple typed columns with CDC enabled, exact",
      "status": "pass",
      "duration_ms": 5675,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:25.294750+00:00",
      "read_cold_ms": 4012,
      "read_warm_ms": 625,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 75,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/958_update_chain_decimal",
      "num": 958,
      "name": "update_chain_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/958_update_chain_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_958_update_chain_decimal.py",
      "description": "3 sequential UPDATEs on a DECIMAL(10,2) column with",
      "status": "pass",
      "duration_ms": 6699,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:31.994805+00:00",
      "read_cold_ms": 4292,
      "read_warm_ms": 1374,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 177,
      "write_warm_ms": 147,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/959_update_chain_timestamp",
      "num": 959,
      "name": "update_chain_timestamp",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/959_update_chain_timestamp.sql",
      "read_script": "generator/spark-reads-df/verify_959_update_chain_timestamp.py",
      "description": "3 sequential UPDATEs on a TIMESTAMP column with different",
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      "duration_ms": 4310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:36.305934+00:00",
      "read_cold_ms": 3163,
      "read_warm_ms": 500,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 187,
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      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/95_protocol_version_edge_cases",
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      "name": "protocol_version_edge_cases",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/95_protocol_version_edge_cases.sql",
      "read_script": "generator/spark-reads-df/verify_95_protocol_version_edge_cases.py",
      "description": "Demonstrates protocol version edge cases and compatibility. Tests how Delta handles various protocol version scenarios: - Minimum required reader/writer versions - Feature flags vs version numbers - Backward compatibility requirements - Unknown feature handling",
      "status": "pass",
      "duration_ms": 16366,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:18:52.675256+00:00",
      "read_cold_ms": 2584,
      "read_warm_ms": 759,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 536,
      "write_warm_ms": 245,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/960_update_chain_boolean",
      "num": 960,
      "name": "update_chain_boolean",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/960_update_chain_boolean.sql",
      "read_script": "generator/spark-reads-df/verify_960_update_chain_boolean.py",
      "description": "3 sequential UPDATEs flipping a BOOLEAN column with",
      "status": "pass",
      "duration_ms": 8045,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:00.722481+00:00",
      "read_cold_ms": 3572,
      "read_warm_ms": 3305,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 66,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/961_update_struct_colmap",
      "num": 961,
      "name": "update_struct_colmap",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/961_update_struct_colmap.sql",
      "read_script": "generator/spark-reads-df/verify_961_update_struct_colmap.py",
      "description": "UPDATE scalar columns on a table with STRUCT column and",
      "status": "pass",
      "duration_ms": 6720,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:07.443906+00:00",
      "read_cold_ms": 2892,
      "read_warm_ms": 1199,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 77,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/962_update_decimal_not_null",
      "num": 962,
      "name": "update_decimal_not_null",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/962_update_decimal_not_null.sql",
      "read_script": "generator/spark-reads-df/verify_962_update_decimal_not_null.py",
      "description": "UPDATE DECIMAL column on a table with NOT NULL columns.",
      "status": "pass",
      "duration_ms": 7785,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:15.231813+00:00",
      "read_cold_ms": 3008,
      "read_warm_ms": 1080,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
      "write_warm_ms": 94,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/963_update_overwrite_then_update",
      "num": 963,
      "name": "update_overwrite_then_update",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/963_update_overwrite_then_update.sql",
      "read_script": "generator/spark-reads-df/verify_963_update_overwrite_then_update.py",
      "description": "INSERT OVERWRITE then UPDATE typed columns. Tests that",
      "status": "pass",
      "duration_ms": 6357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:21.591151+00:00",
      "read_cold_ms": 3590,
      "read_warm_ms": 1218,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 136,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/964_update_where_compound_typed",
      "num": 964,
      "name": "update_where_compound_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/964_update_where_compound_typed.sql",
      "read_script": "generator/spark-reads-df/verify_964_update_where_compound_typed.py",
      "description": "UPDATE with compound WHERE clause mixing typed predicates",
      "status": "pass",
      "duration_ms": 7170,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:28.763074+00:00",
      "read_cold_ms": 2694,
      "read_warm_ms": 533,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 107,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/965_update_decimal_then_update_int",
      "num": 965,
      "name": "update_decimal_then_update_int",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/965_update_decimal_then_update_int.sql",
      "read_script": "generator/spark-reads-df/verify_965_update_decimal_then_update_int.py",
      "description": "UPDATE DECIMAL column then UPDATE INT column on the same",
      "status": "pass",
      "duration_ms": 5207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:33.971362+00:00",
      "read_cold_ms": 2745,
      "read_warm_ms": 989,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 124,
      "write_warm_ms": 187,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/966_update_all_types_where_id_range",
      "num": 966,
      "name": "update_all_types_where_id_range",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/966_update_all_types_where_id_range.sql",
      "read_script": "generator/spark-reads-df/verify_966_update_all_types_where_id_range.py",
      "description": "UPDATE all 6 typed columns (STRING, INT, DOUBLE, BOOLEAN,",
      "status": "pass",
      "duration_ms": 1243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:37:37.204365+00:00",
      "read_cold_ms": 716,
      "read_warm_ms": 203,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 74,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/967_update_string_operations",
      "num": 967,
      "name": "update_string_operations",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/967_update_string_operations.sql",
      "read_script": "generator/spark-reads-df/verify_967_update_string_operations.py",
      "description": "Various string operations in UPDATE SET clause: CONCAT,",
      "status": "pass",
      "duration_ms": 7518,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:50.147220+00:00",
      "read_cold_ms": 3453,
      "read_warm_ms": 3268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/968_update_decimal_from_int_cast",
      "num": 968,
      "name": "update_decimal_from_int_cast",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/968_update_decimal_from_int_cast.sql",
      "read_script": "generator/spark-reads-df/verify_968_update_decimal_from_int_cast.py",
      "description": "UPDATE DECIMAL(10,2) column from INT column via CAST.",
      "status": "pass",
      "duration_ms": 8146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:19:58.294565+00:00",
      "read_cold_ms": 2687,
      "read_warm_ms": 4239,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 108,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/969_update_int_from_decimal_cast",
      "num": 969,
      "name": "update_int_from_decimal_cast",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/969_update_int_from_decimal_cast.sql",
      "read_script": "generator/spark-reads-df/verify_969_update_int_from_decimal_cast.py",
      "description": "UPDATE INT column from DECIMAL column via CAST (truncation).",
      "status": "pass",
      "duration_ms": 5191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:03.486866+00:00",
      "read_cold_ms": 3299,
      "read_warm_ms": 1024,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 74,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/96_binary_data_type_handling",
      "num": 96,
      "name": "binary_data_type_handling",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/96_binary_data_type_handling.sql",
      "read_script": "generator/spark-reads-df/verify_96_binary_data_type_handling.py",
      "description": "**CURRENTLY DISABLED** - Table 95 is disabled due to Arrow 57.x Parquet reader limitation.",
      "status": "pass",
      "duration_ms": 7421,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:10.908647+00:00",
      "finished_at": "2026-05-03T22:44:09.938712+00:00",
      "read_cold_ms": 5714,
      "read_warm_ms": 923,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 177,
      "tags": [
        "type:binary",
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "storage:parquet-compression",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 970,
      "name": "update_noop_all_types",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/970_update_noop_all_types.sql",
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      "description": "No-op UPDATE (SET col=col) for every typed column. Tests",
      "status": "pass",
      "duration_ms": 4585,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:15.494542+00:00",
      "read_cold_ms": 2544,
      "read_warm_ms": 1151,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 79,
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        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/971_update_decimal_colmap_cdc",
      "num": 971,
      "name": "update_decimal_colmap_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/971_update_decimal_colmap_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_971_update_decimal_colmap_cdc.py",
      "description": "DECIMAL UPDATE + column mapping (name) + CDC. Three-way",
      "status": "pass",
      "duration_ms": 7527,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:23.022995+00:00",
      "read_cold_ms": 4987,
      "read_warm_ms": 1011,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 86,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/972_update_typed_partition_cdc",
      "num": 972,
      "name": "update_typed_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/972_update_typed_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_972_update_typed_partition_cdc.py",
      "description": "Typed UPDATE + partition + CDC. Three-way combination.",
      "status": "pass",
      "duration_ms": 7368,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:30.392658+00:00",
      "read_cold_ms": 5090,
      "read_warm_ms": 989,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 105,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/973_update_typed_constraint_evolve",
      "num": 973,
      "name": "update_typed_constraint_evolve",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/973_update_typed_constraint_evolve.sql",
      "read_script": "generator/spark-reads-df/verify_973_update_typed_constraint_evolve.py",
      "description": "Typed UPDATE + constraint + schema evolution. Three-way.",
      "status": "pass",
      "duration_ms": 4654,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:35.048461+00:00",
      "read_cold_ms": 2949,
      "read_warm_ms": 599,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 42,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/974_update_typed_optimize_partition",
      "num": 974,
      "name": "update_typed_optimize_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/974_update_typed_optimize_partition.sql",
      "read_script": "generator/spark-reads-df/verify_974_update_typed_optimize_partition.py",
      "description": "Typed UPDATE + OPTIMIZE + partition. Three-way.",
      "status": "pass",
      "duration_ms": 6584,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:41.633941+00:00",
      "read_cold_ms": 3562,
      "read_warm_ms": 1313,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 79,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/975_update_colmap_evolve_typed",
      "num": 975,
      "name": "update_colmap_evolve_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/975_update_colmap_evolve_typed.sql",
      "read_script": "generator/spark-reads-df/verify_975_update_colmap_evolve_typed.py",
      "description": "Column mapping (name) + schema evolution + typed UPDATE.",
      "status": "pass",
      "duration_ms": 7941,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:49.576611+00:00",
      "read_cold_ms": 5424,
      "read_warm_ms": 1105,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 58,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/976_update_cdc_constraint_typed",
      "num": 976,
      "name": "update_cdc_constraint_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/976_update_cdc_constraint_typed.sql",
      "read_script": "generator/spark-reads-df/verify_976_update_cdc_constraint_typed.py",
      "description": "CDC + constraint + typed UPDATE. Three-way.",
      "status": "pass",
      "duration_ms": 3502,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:20:53.080352+00:00",
      "read_cold_ms": 1799,
      "read_warm_ms": 573,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 116,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/977_update_large_decimal",
      "num": 977,
      "name": "update_large_decimal",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/977_update_large_decimal.sql",
      "read_script": "generator/spark-reads-df/verify_977_update_large_decimal.py",
      "description": "Large scale UPDATE (2000 rows) with DECIMAL precision.",
      "status": "pass",
      "duration_ms": 7286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:00.369375+00:00",
      "read_cold_ms": 5180,
      "read_warm_ms": 857,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 211,
      "write_warm_ms": 213,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/978_update_large_mixed",
      "num": 978,
      "name": "update_large_mixed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/978_update_large_mixed.sql",
      "read_script": "generator/spark-reads-df/verify_978_update_large_mixed.py",
      "description": "Large scale UPDATE (2000 rows) with 4 typed columns.",
      "status": "pass",
      "duration_ms": 11182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:11.554985+00:00",
      "read_cold_ms": 4054,
      "read_warm_ms": 4568,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 201,
      "write_warm_ms": 253,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/979_update_large_sequential",
      "num": 979,
      "name": "update_large_sequential",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/979_update_large_sequential.sql",
      "read_script": "generator/spark-reads-df/verify_979_update_large_sequential.py",
      "description": "Large scale (1000 rows) with 10 sequential UPDATEs.",
      "status": "pass",
      "duration_ms": 11015,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:22.572440+00:00",
      "read_cold_ms": 4174,
      "read_warm_ms": 4580,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 482,
      "write_warm_ms": 465,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/97_decimal_precision_edge_cases",
      "num": 97,
      "name": "decimal_precision_edge_cases",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/97_decimal_precision_edge_cases.sql",
      "read_script": "generator/spark-reads-df/verify_97_decimal_precision_edge_cases.py",
      "description": "Demonstrates binary data type handling in Delta Lake. Tests how Delta handles binary columns: - Raw binary data storage - Statistics for binary columns (typically not collected) - NULL handling for binary - Various binary content types",
      "status": "pass",
      "duration_ms": 14140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:36.714485+00:00",
      "finished_at": "2026-05-03T22:44:09.938769+00:00",
      "read_cold_ms": 3536,
      "read_warm_ms": 1115,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 228,
      "write_warm_ms": 191,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/980_update_single_row_typed",
      "num": 980,
      "name": "update_single_row_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/980_update_single_row_typed.sql",
      "read_script": "generator/spark-reads-df/verify_980_update_single_row_typed.py",
      "description": "UPDATE single row with all types. Tests minimum-scale",
      "status": "pass",
      "duration_ms": 3028,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:39.744309+00:00",
      "read_cold_ms": 1686,
      "read_warm_ms": 611,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 65,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/981_update_empty_result_typed",
      "num": 981,
      "name": "update_empty_result_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/981_update_empty_result_typed.sql",
      "read_script": "generator/spark-reads-df/verify_981_update_empty_result_typed.py",
      "description": "UPDATE WHERE false on typed table. No rows match the",
      "status": "pass",
      "duration_ms": 4727,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:44.472472+00:00",
      "read_cold_ms": 1985,
      "read_warm_ms": 2201,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 24,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/982_update_full_table_typed",
      "num": 982,
      "name": "update_full_table_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/982_update_full_table_typed.sql",
      "read_script": "generator/spark-reads-df/verify_982_update_full_table_typed.py",
      "description": "UPDATE all rows (no WHERE clause) with typed columns.",
      "status": "pass",
      "duration_ms": 4575,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:49.048303+00:00",
      "read_cold_ms": 2852,
      "read_warm_ms": 1132,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 102,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/983_update_typed_delete_typed",
      "num": 983,
      "name": "update_typed_delete_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/983_update_typed_delete_typed.sql",
      "read_script": "generator/spark-reads-df/verify_983_update_typed_delete_typed.py",
      "description": "UPDATE typed then DELETE on typed predicate. Tests",
      "status": "pass",
      "duration_ms": 5144,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:21:54.193013+00:00",
      "read_cold_ms": 3735,
      "read_warm_ms": 827,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 62,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/984_update_delete_update_typed",
      "num": 984,
      "name": "update_delete_update_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/984_update_delete_update_typed.sql",
      "read_script": "generator/spark-reads-df/verify_984_update_delete_update_typed.py",
      "description": "UPDATE then DELETE then UPDATE. Three-step DML chain",
      "status": "pass",
      "duration_ms": 5892,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:00.086472+00:00",
      "read_cold_ms": 2664,
      "read_warm_ms": 2569,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 129,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/985_update_typed_then_insert",
      "num": 985,
      "name": "update_typed_then_insert",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/985_update_typed_then_insert.sql",
      "read_script": "generator/spark-reads-df/verify_985_update_typed_then_insert.py",
      "description": "UPDATE typed columns then INSERT more rows. Tests that",
      "status": "pass",
      "duration_ms": 6003,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:06.090165+00:00",
      "read_cold_ms": 2541,
      "read_warm_ms": 2781,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 70,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/986_update_struct_fields",
      "num": 986,
      "name": "update_struct_fields",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/986_update_struct_fields.sql",
      "read_script": "generator/spark-reads-df/verify_986_update_struct_fields.py",
      "description": "UPDATE non-struct columns on table with nested STRUCT.",
      "status": "pass",
      "duration_ms": 7110,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:13.201299+00:00",
      "read_cold_ms": 2361,
      "read_warm_ms": 971,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 70,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "scale:nested-types",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/987_update_decimal_two_constraints",
      "num": 987,
      "name": "update_decimal_two_constraints",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/987_update_decimal_two_constraints.sql",
      "read_script": "generator/spark-reads-df/verify_987_update_decimal_two_constraints.py",
      "description": "Two CHECK constraints on DECIMAL column + UPDATE.",
      "status": "pass",
      "duration_ms": 3847,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:17.049683+00:00",
      "read_cold_ms": 2186,
      "read_warm_ms": 462,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 101,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/988_update_typed_nmbys_style",
      "num": 988,
      "name": "update_typed_nmbys_style",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/988_update_typed_nmbys_style.sql",
      "read_script": "generator/spark-reads-df/verify_988_update_typed_nmbys_style.py",
      "description": "UPDATE simulating NOT-MATCHED-BY-SOURCE pattern.",
      "status": "pass",
      "duration_ms": 5010,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:22.061248+00:00",
      "read_cold_ms": 3719,
      "read_warm_ms": 580,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 42,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/989_update_typed_colmap_partition",
      "num": 989,
      "name": "update_typed_colmap_partition",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/989_update_typed_colmap_partition.sql",
      "read_script": "generator/spark-reads-df/verify_989_update_typed_colmap_partition.py",
      "description": "Column mapping (name) + partition + typed UPDATE.",
      "status": "pass",
      "duration_ms": 5052,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:27.114816+00:00",
      "read_cold_ms": 2258,
      "read_warm_ms": 1909,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 101,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/990_update_evolve_decimal_constraint",
      "num": 990,
      "name": "update_evolve_decimal_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/990_update_evolve_decimal_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_990_update_evolve_decimal_constraint.py",
      "description": "Schema evolution + DECIMAL + constraint + UPDATE.",
      "status": "pass",
      "duration_ms": 4551,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:31.667249+00:00",
      "read_cold_ms": 2458,
      "read_warm_ms": 418,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 168,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:constraints",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/991_update_three_decimal_cols",
      "num": 991,
      "name": "update_three_decimal_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/991_update_three_decimal_cols.sql",
      "read_script": "generator/spark-reads-df/verify_991_update_three_decimal_cols.py",
      "description": "UPDATE 3 DECIMAL columns with different scales simultaneously.",
      "status": "pass",
      "duration_ms": 5148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:36.816246+00:00",
      "read_cold_ms": 1737,
      "read_warm_ms": 689,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 62,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/992_update_timestamp_date_together",
      "num": 992,
      "name": "update_timestamp_date_together",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/992_update_timestamp_date_together.sql",
      "read_script": "generator/spark-reads-df/verify_992_update_timestamp_date_together.py",
      "description": "UPDATE both TIMESTAMP and DATE columns in the same statement.",
      "status": "pass",
      "duration_ms": 4424,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:41.241652+00:00",
      "read_cold_ms": 2389,
      "read_warm_ms": 1147,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 67,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/993_update_boolean_three_cols",
      "num": 993,
      "name": "update_boolean_three_cols",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/993_update_boolean_three_cols.sql",
      "read_script": "generator/spark-reads-df/verify_993_update_boolean_three_cols.py",
      "description": "UPDATE 3 BOOLEAN columns with different CASE conditions",
      "status": "pass",
      "duration_ms": 5608,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:46.850883+00:00",
      "read_cold_ms": 3952,
      "read_warm_ms": 767,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 111,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/994_update_string_from_typed",
      "num": 994,
      "name": "update_string_from_typed",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/994_update_string_from_typed.sql",
      "read_script": "generator/spark-reads-df/verify_994_update_string_from_typed.py",
      "description": "UPDATE STRING columns derived from typed columns via CAST.",
      "status": "pass",
      "duration_ms": 5602,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:52.454276+00:00",
      "read_cold_ms": 2398,
      "read_warm_ms": 2320,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 31,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/995_update_decimal_partition_cdc",
      "num": 995,
      "name": "update_decimal_partition_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/995_update_decimal_partition_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_995_update_decimal_partition_cdc.py",
      "description": "DECIMAL UPDATE + partition + CDC. Three-way combination.",
      "status": "pass",
      "duration_ms": 5791,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:22:58.246730+00:00",
      "read_cold_ms": 2670,
      "read_warm_ms": 2172,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 124,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/996_update_typed_colmap_evolve_cdc",
      "num": 996,
      "name": "update_typed_colmap_evolve_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/996_update_typed_colmap_evolve_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_996_update_typed_colmap_evolve_cdc.py",
      "description": "Column mapping + schema evolution + CDC + typed UPDATE.",
      "status": "pass",
      "duration_ms": 5691,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:23:03.940044+00:00",
      "read_cold_ms": 2202,
      "read_warm_ms": 533,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 205,
      "write_warm_ms": 38,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "schema:add-column",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/997_update_typed_optimize_cdc",
      "num": 997,
      "name": "update_typed_optimize_cdc",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/997_update_typed_optimize_cdc.sql",
      "read_script": "generator/spark-reads-df/verify_997_update_typed_optimize_cdc.py",
      "description": "OPTIMIZE + CDC + typed UPDATE. Three-way combination.",
      "status": "pass",
      "duration_ms": 3915,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:23:07.855984+00:00",
      "read_cold_ms": 2089,
      "read_warm_ms": 476,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 228,
      "write_warm_ms": 191,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:deletion-vectors",
        "delta:optimize",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/998_update_decimal_chain_four",
      "num": 998,
      "name": "update_decimal_chain_four",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/998_update_decimal_chain_four.sql",
      "read_script": "generator/spark-reads-df/verify_998_update_decimal_chain_four.py",
      "description": "4 sequential DECIMAL UPDATEs with maximum DV stacking.",
      "status": "pass",
      "duration_ms": 4631,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:23:12.487811+00:00",
      "read_cold_ms": 3419,
      "read_warm_ms": 655,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 176,
      "write_warm_ms": 104,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:deletion-vectors",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/999_update_all_types_cdc_partition_constraint",
      "num": 999,
      "name": "update_all_types_cdc_partition_constraint",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/999_update_all_types_cdc_partition_constraint.sql",
      "read_script": "generator/spark-reads-df/verify_999_update_all_types_cdc_partition_constraint.py",
      "description": "Five-way combination: all types + CDC + partition +",
      "status": "pass",
      "duration_ms": 6033,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:23:18.521464+00:00",
      "read_cold_ms": 3351,
      "read_warm_ms": 1035,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 252,
      "write_warm_ms": 258,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/delta/99_checkpoint_parquet_schema_structure",
      "num": 99,
      "name": "checkpoint_parquet_schema_structure",
      "type": "df-writes",
      "format": "delta",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/99_checkpoint_parquet_schema_structure.sql",
      "read_script": "generator/spark-reads-df/verify_99_checkpoint_parquet_schema_structure.py",
      "description": "Checkpoint Parquet file internal schema structure.",
      "status": "pass",
      "duration_ms": 17085,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T02:23:35.609819+00:00",
      "read_cold_ms": 3087,
      "read_warm_ms": 1043,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 9113,
      "write_warm_ms": 8116,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:deletion-vectors",
        "delta:partitioning",
        "scale:large-dataset",
        "direction:df-writes",
        "format:delta"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/01_basic_data_files_parquet",
      "num": 1,
      "name": "basic_data_files_parquet",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/01_basic_data_files_parquet.sql",
      "read_script": "generator/spark-reads-iceberg/verify_01_basic_data_files_parquet.py",
      "description": "Validates the Delta table written by DeltaForge for test 01. No UPDATE or DELETE. 9 columns. 5 categories, 2 statuses, 3 priority levels.",
      "status": "pass",
      "duration_ms": 6822,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:02.805782+00:00",
      "read_cold_ms": 6257,
      "read_warm_ms": 231,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 279,
      "write_warm_ms": 69,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/02_deletion_vector_files_external",
      "num": 2,
      "name": "deletion_vector_files_external",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/02_deletion_vector_files_external.sql",
      "read_script": "generator/spark-reads-iceberg/verify_02_deletion_vector_files_external.py",
      "description": "- DELETE operations that create external deletion vector files (.bin) - Multiple deletion predicates demonstrating DV accumulation - Boolean, string, and numeric predicates - **Modulo operator in DELETE predicates** (`column % divisor = remainder`) - LIKE pattern matching in...",
      "status": "pass",
      "duration_ms": 1026,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:03.832438+00:00",
      "read_cold_ms": 362,
      "read_warm_ms": 207,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 308,
      "write_warm_ms": 199,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/03_change_data_capture_files",
      "num": 3,
      "name": "change_data_capture_files",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/03_change_data_capture_files.sql",
      "read_script": "generator/spark-reads-iceberg/verify_03_change_data_capture_files.py",
      "description": "- Change Data Capture (CDC) with delta.enableChangeDataFeed = true - Multiple UPDATE operations with arithmetic expressions (price * 0.85) - UPDATE with multiple assignments - UPDATE with compound predicates (AND) - DELETE with modulo operator - INSERT for new products - MERGE...",
      "status": "pass",
      "duration_ms": 628,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:04.463356+00:00",
      "read_cold_ms": 222,
      "read_warm_ms": 211,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 743,
      "write_warm_ms": 674,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/04_delta_log_json_entries",
      "num": 4,
      "name": "delta_log_json_entries",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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        "format:iceberg"
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      "status": "pass",
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    },
    {
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        "format:iceberg"
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    },
    {
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    },
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      "duration_ms": 485,
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
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      "read_script": "generator/spark-reads-iceberg/verify_1161_delete_timestamp_colmap.py",
      "description": "DELETE on TIMESTAMP predicate with column mapping mode=name.",
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      "duration_ms": 320,
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      "write_cold_ms": 92,
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      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1162_delete_typed_evolve",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1162_delete_typed_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1162_delete_typed_evolve.py",
      "description": "DELETE after schema evolution on typed table.",
      "status": "pass",
      "duration_ms": 267,
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      "extras": {},
      "verified_at": "2026-05-20T11:51:03.018598+00:00",
      "write_cold_ms": 157,
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "extras": {},
      "verified_at": "2026-05-20T11:51:03.486869+00:00",
      "write_cold_ms": 305,
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1164_delete_timestamp_optimize.sql",
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      "description": "DELETE on TIMESTAMP predicate after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 350,
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      "extras": {},
      "verified_at": "2026-05-20T11:51:03.838454+00:00",
      "write_cold_ms": 234,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1165_delete_typed_no_dv",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1165_delete_typed_no_dv.sql",
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      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1166_delete_typed_not_null",
      "num": 1166,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1166_delete_typed_not_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1166_delete_typed_not_null.py",
      "description": "DELETE on NOT NULL typed table.",
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      "duration_ms": 199,
      "error": null,
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      "verified_at": "2026-05-20T11:51:04.251644+00:00",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1167_delete_decimal_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1167_delete_decimal_cdc_partition.py",
      "description": "DELETE DECIMAL + CDC + partition (three-way combo).",
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      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:04.521624+00:00",
      "write_cold_ms": 180,
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        "delta:column-mapping",
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1168_delete_typed_colmap_cdc",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1168_delete_typed_colmap_cdc.py",
      "description": "DELETE typed + colmap + CDC (three-way combo).",
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      "duration_ms": 171,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1169_delete_typed_constraint_evolve",
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      "writer": "delta-forge",
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      "read_script": "generator/spark-reads-iceberg/verify_1169_delete_typed_constraint_evolve.py",
      "description": "DELETE typed + constraint + schema evolution (three-way combo).",
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      "duration_ms": 470,
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    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1170_delete_decimal_colmap_partition.sql",
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      "manual_tags": [],
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      "id": "df-writes/iceberg/1171_delete_typed_optimize_cdc",
      "num": 1171,
      "name": "delete_typed_optimize_cdc",
      "type": "df-writes",
      "format": "iceberg",
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      "write_script": "generator/df-sql-iceberg/1171_delete_typed_optimize_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1171_delete_typed_optimize_cdc.py",
      "description": "DELETE typed + OPTIMIZE + CDC (three-way combo).",
      "status": "pass",
      "duration_ms": 532,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:05.987016+00:00",
      "write_cold_ms": 253,
      "write_warm_ms": 228,
      "read_cold_ms": 53,
      "read_warm_ms": 44,
      "skip_feature": null,
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1172_delete_typed_partition_evolve",
      "num": 1172,
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      "read_script": "generator/spark-reads-iceberg/verify_1172_delete_typed_partition_evolve.py",
      "description": "DELETE typed + partition + schema evolution.",
      "status": "pass",
      "duration_ms": 264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:06.251793+00:00",
      "write_cold_ms": 292,
      "write_warm_ms": 351,
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      "skip_feature": null,
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        "type:decimal",
        "type:integer",
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
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        "direction:df-writes",
        "format:iceberg"
      ],
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    },
    {
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      "duration_ms": 174,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:06.426587+00:00",
      "write_cold_ms": 79,
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        "type:decimal",
        "type:integer",
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        "format:iceberg"
      ],
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      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1174_delete_four_way",
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      "error": null,
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      "verified_at": "2026-05-20T11:51:06.749862+00:00",
      "write_cold_ms": 201,
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      "read_warm_ms": 86,
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        "type:integer",
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        "format:iceberg"
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    },
    {
      "id": "df-writes/iceberg/1175_delete_five_way",
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      "description": "DELETE typed + CDC + colmap + partition + constraint (five-way combo).",
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      "verified_at": "2026-05-20T11:51:07.115863+00:00",
      "write_cold_ms": 232,
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        "dml:cdc-write",
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        "delta:deletion-vectors",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1176_delete_then_insert_typed",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1176_delete_then_insert_typed.sql",
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      "error": null,
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      "write_cold_ms": 110,
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        "type:decimal",
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        "delta:column-mapping",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1177_delete_then_update_typed",
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      "description": "DELETE then UPDATE on surviving typed rows.",
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      "error": null,
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      "write_cold_ms": 122,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1178_delete_then_merge_typed",
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      "write_cold_ms": 171,
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    {
      "id": "df-writes/iceberg/1179_delete_chain_three",
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      "write_cold_ms": 160,
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      "manual_tags": [],
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      "manual_tags": [],
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    {
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      "read_script": "generator/spark-reads-iceberg/verify_1183_delete_timestamp_then_boolean.py",
      "description": "DELETE on TIMESTAMP then DELETE on BOOLEAN.",
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      "error": null,
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      "verified_at": "2026-05-20T11:51:09.204029+00:00",
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      "manual_tags": [],
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      "write_script": "generator/df-sql-iceberg/1184_delete_typed_all_match.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1184_delete_typed_all_match.py",
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      "manual_tags": [],
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1185_delete_typed_no_match.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1185_delete_typed_no_match.py",
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      "duration_ms": 93,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-20T11:51:09.794237+00:00",
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        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1186_delete_decimal_exact_value",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1186_delete_decimal_exact_value.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1186_delete_decimal_exact_value.py",
      "description": "DELETE WHERE decimal = exact value.",
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      "duration_ms": 212,
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      "verified_at": "2026-05-20T11:51:10.006532+00:00",
      "write_cold_ms": 81,
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      "id": "df-writes/iceberg/1187_delete_timestamp_exact",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1187_delete_timestamp_exact.sql",
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    {
      "id": "df-writes/iceberg/1188_delete_boolean_only_true",
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        "format:iceberg"
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    },
    {
      "id": "df-writes/iceberg/1189_delete_string_pattern",
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      "status": "pass",
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    },
    {
      "id": "df-writes/iceberg/1190_delete_decimal_cdc_exact",
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    },
    {
      "id": "df-writes/iceberg/1191_delete_typed_cdc_partition_constraint",
      "num": 1191,
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      "description": "DELETE typed + CDC + partition + constraint (four-way).",
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        "delta:change-data-feed",
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        "delta:partitioning",
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      ],
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    },
    {
      "id": "df-writes/iceberg/1192_delete_typed_colmap_cdc_partition",
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      "description": "DELETE typed + colmap + CDC + partition (four-way).",
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      "write_cold_ms": 200,
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        "delta:deletion-vectors",
        "delta:partitioning",
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      ],
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    },
    {
      "id": "df-writes/iceberg/1193_delete_typed_optimize_partition_cdc",
      "num": 1193,
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      "read_script": "generator/spark-reads-iceberg/verify_1193_delete_typed_optimize_partition_cdc.py",
      "description": "DELETE typed + OPTIMIZE + partition + CDC (four-way).",
      "status": "pass",
      "duration_ms": 336,
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      "write_cold_ms": 472,
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1194_delete_typed_evolve_cdc_partition",
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      "description": "DELETE typed + evolve + CDC + partition (four-way).",
      "status": "pass",
      "duration_ms": 389,
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      "extras": {},
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      "write_cold_ms": 288,
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        "delta:change-data-feed",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1195_delete_typed_five_way",
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      "description": "DELETE typed + CDC + colmap + partition + constraint + evolve (five-way).",
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      "write_cold_ms": 280,
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    },
    {
      "id": "df-writes/iceberg/1196_delete_decimal_partition_multi_pred",
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    {
      "id": "df-writes/iceberg/1197_delete_typed_colmap_evolve",
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    {
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      "description": "Validates a wide analytics table with 255 columns. INSERT 1000, UPDATE record_id<100, INSERT 200, DELETE record_id>1150.",
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      "description": "The Rust generator pre-computes the FINAL state after all operations in a single INSERT. This SQL generator replicates the same logic.",
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    {
      "id": "df-writes/iceberg/1200_delete_ultimate",
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      "read_script": "generator/spark-reads-iceberg/verify_129_concurrent_modifications.py",
      "description": "Multiple concurrent modifications to a table simulating",
      "status": "pass",
      "duration_ms": 326,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:12.041195+00:00",
      "read_cold_ms": 111,
      "read_warm_ms": 92,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 428,
      "write_warm_ms": 497,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/12_version_checksum_with_histogram",
      "num": 12,
      "name": "version_checksum_with_histogram",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/12_version_checksum_with_histogram.sql",
      "read_script": "generator/spark-reads-iceberg/verify_12_version_checksum_with_histogram.py",
      "description": "Validates the Delta table written by DeltaForge for test 12. Marketing campaign analytics table with 20 columns. Operations: - Op1: UPDATE roi<0 AND status='active' -> status='paused' - Op2: UPDATE roi>200 AND status='active' -> spend_usd*=1.5, performance_tier='excellent",
      "status": "pass",
      "duration_ms": 1609,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:13.651069+00:00",
      "read_cold_ms": 295,
      "read_warm_ms": 121,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 634,
      "write_warm_ms": 461,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1300_nullable_metadata_partition",
      "num": 1300,
      "name": "nullable_metadata_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1300_nullable_metadata_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1300_nullable_metadata_partition.py",
      "description": "NOT NULL partition key. Partitioned table where partition column is NOT NULL.",
      "status": "pass",
      "duration_ms": 114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:49.531669+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1301_append_only_basic",
      "num": 1301,
      "name": "append_only_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1301_append_only_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1301_append_only_basic.py",
      "description": "Append-only table (delta.appendOnly=true). Only INSERTs allowed.",
      "status": "pass",
      "duration_ms": 516,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:50.048085+00:00",
      "read_cold_ms": 43,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1302_append_only_typed",
      "num": 1302,
      "name": "append_only_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1302_append_only_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1302_append_only_typed.py",
      "description": "Append-only with DECIMAL+TIMESTAMP+BOOLEAN typed columns.",
      "status": "pass",
      "duration_ms": 162,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:50.211059+00:00",
      "read_cold_ms": 47,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 89,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1303_append_only_cdc",
      "num": 1303,
      "name": "append_only_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1303_append_only_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1303_append_only_cdc.py",
      "description": "Append-only + CDC enabled. Only inserts, CDF should have 100 insert records.",
      "status": "pass",
      "duration_ms": 128,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:50.340123+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 88,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1304_enable_cdc_at_create",
      "num": 1304,
      "name": "enable_cdc_at_create",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1304_enable_cdc_at_create.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1304_enable_cdc_at_create.py",
      "description": "CDC enabled from table creation with full DML lifecycle.",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:50.580283+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 176,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1305_enable_dv_at_create",
      "num": 1305,
      "name": "enable_dv_at_create",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1305_enable_dv_at_create.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1305_enable_dv_at_create.py",
      "description": "Deletion vectors enabled from creation with full DML lifecycle.",
      "status": "pass",
      "duration_ms": 243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:50.824350+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 169,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1306_colmap_from_start",
      "num": 1306,
      "name": "colmap_from_start",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1306_colmap_from_start.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1306_colmap_from_start.py",
      "description": "Column mapping from table creation with full DML lifecycle + RENAME.",
      "status": "pass",
      "duration_ms": 239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:51.063528+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 170,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1307_all_properties_from_start",
      "num": 1307,
      "name": "all_properties_from_start",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1307_all_properties_from_start.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1307_all_properties_from_start.py",
      "description": "All properties enabled at CREATE (DV + CDC + colmap) with full DML.",
      "status": "pass",
      "duration_ms": 233,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:51.296636+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 245,
      "write_warm_ms": 256,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1308_minimal_properties",
      "num": 1308,
      "name": "minimal_properties",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1308_minimal_properties.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1308_minimal_properties.py",
      "description": "Table with NO special properties (no DV, no CDC, no colmap).",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:51.522444+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 104,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 158,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1309_hundred_files",
      "num": 1309,
      "name": "hundred_files",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1309_hundred_files.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1309_hundred_files.py",
      "description": "100 INSERT batches of 5 rows each (500 total, 100 files).",
      "status": "pass",
      "duration_ms": 920,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:52.443127+00:00",
      "read_cold_ms": 169,
      "read_warm_ms": 204,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 14368,
      "write_warm_ms": 15835,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/130_deltaforge_dbx_deltaforge",
      "num": 130,
      "name": "deltaforge_dbx_deltaforge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/130_deltaforge_dbx_deltaforge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_130_deltaforge_dbx_deltaforge.py",
      "description": "Complex multi-operation chaos scenario with sensor readings.",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:13.913056+00:00",
      "read_cold_ms": 107,
      "read_warm_ms": 73,
      "skip_feature": null,
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      "write_cold_ms": 232,
      "write_warm_ms": 325,
      "tags": [
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1310_hundred_files_delete",
      "num": 1310,
      "name": "hundred_files_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1310_hundred_files_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1310_hundred_files_delete.py",
      "description": "50 INSERT batches of 10 rows (500 total, 50 files).",
      "status": "pass",
      "duration_ms": 719,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:53.162318+00:00",
      "read_cold_ms": 218,
      "read_warm_ms": 268,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 4877,
      "write_warm_ms": 4350,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1311_hundred_files_typed",
      "num": 1311,
      "name": "hundred_files_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1311_hundred_files_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1311_hundred_files_typed.py",
      "description": "100 INSERT batches of 5 rows with DECIMAL+TIMESTAMP+BOOLEAN (500 total).",
      "status": "pass",
      "duration_ms": 590,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:53.752608+00:00",
      "read_cold_ms": 149,
      "read_warm_ms": 210,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 14432,
      "write_warm_ms": 14575,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1312_stats_checkpoint",
      "num": 1312,
      "name": "stats_checkpoint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1312_stats_checkpoint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1312_stats_checkpoint.py",
      "description": "11 INSERT batches (checkpoint at V10). Verify stats correct after checkpoint.",
      "status": "pass",
      "duration_ms": 417,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:54.170364+00:00",
      "read_cold_ms": 51,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 761,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1313_stats_partition_pushdown",
      "num": 1313,
      "name": "stats_partition_pushdown",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1313_stats_partition_pushdown.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1313_stats_partition_pushdown.py",
      "description": "Partitioned table + 3 batches per partition. Predicate uses both",
      "status": "pass",
      "duration_ms": 265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:54.436433+00:00",
      "read_cold_ms": 44,
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      "skip_feature": null,
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      "write_cold_ms": 193,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1314_pushdown_checkpoint",
      "num": 1314,
      "name": "pushdown_checkpoint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1314_pushdown_checkpoint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1314_pushdown_checkpoint.py",
      "description": "12+ commits then predicate pushdown query. Checkpoint must not break file skipping.",
      "status": "pass",
      "duration_ms": 366,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:51:54.803285+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 47,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 706,
      "write_warm_ms": 559,
      "tags": [
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
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      "name": "restore_after_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1396_restore_after_update.py",
      "description": "RESTORE after UPDATE undoes the score change.",
      "status": "pass",
      "duration_ms": 194,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:20.935814+00:00",
      "read_cold_ms": 39,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1397_restore_typed",
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      "name": "restore_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1397_restore_typed.py",
      "description": "RESTORE preserves typed data (DECIMAL, TIMESTAMP, BOOLEAN).",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:21.171344+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 104,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1398_restore_cdc",
      "num": 1398,
      "name": "restore_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1398_restore_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1398_restore_cdc.py",
      "description": "RESTORE with CDC enabled.",
      "status": "pass",
      "duration_ms": 260,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:21.432145+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 231,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1399_restore_partition",
      "num": 1399,
      "name": "restore_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1399_restore_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1399_restore_partition.py",
      "description": "RESTORE on partitioned table.",
      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:21.714586+00:00",
      "read_cold_ms": 54,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 221,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/139_checkpoint_interop",
      "num": 139,
      "name": "checkpoint_interop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/139_checkpoint_interop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_139_checkpoint_interop.py",
      "description": "- Multiple versions with inserts, updates, and deletes - Deletion vectors enabled - Checkpoint interoperability testing",
      "status": "pass",
      "duration_ms": 331,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:16.122432+00:00",
      "read_cold_ms": 130,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/13_action_metadata_change_schema",
      "num": 13,
      "name": "action_metadata_change_schema",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/13_action_metadata_change_schema.sql",
      "read_script": "generator/spark-reads-iceberg/verify_13_action_metadata_change_schema.py",
      "description": "Metadata action with schema changes.",
      "status": "pass",
      "duration_ms": 621,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:16.744334+00:00",
      "read_cold_ms": 103,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
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      "write_warm_ms": 748,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1400_restore_schema_evolve",
      "num": 1400,
      "name": "restore_schema_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1400_restore_schema_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1400_restore_schema_evolve.py",
      "description": "RESTORE to a version before schema evolution.",
      "status": "pass",
      "duration_ms": 148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:21.863266+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 143,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1401_restore_after_merge",
      "num": 1401,
      "name": "restore_after_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1401_restore_after_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1401_restore_after_merge.py",
      "description": "RESTORE after MERGE. INSERT 100 (V0). MERGE 120-row CTE",
      "status": "pass",
      "duration_ms": 594,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:22.457870+00:00",
      "read_cold_ms": 51,
      "read_warm_ms": 50,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 156,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1402_restore_colmap",
      "num": 1402,
      "name": "restore_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1402_restore_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1402_restore_colmap.py",
      "description": "RESTORE with column mapping mode=name. INSERT 100 (V0).",
      "status": "pass",
      "duration_ms": 835,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:23.293918+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 116,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1403_restore_constraint",
      "num": 1403,
      "name": "restore_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1403_restore_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1403_restore_constraint.py",
      "description": "RESTORE removes constraint added after V0.",
      "status": "pass",
      "duration_ms": 710,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:24.005573+00:00",
      "read_cold_ms": 172,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 61,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1404_restore_multiple_dml",
      "num": 1404,
      "name": "restore_multiple_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1404_restore_multiple_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1404_restore_multiple_dml.py",
      "description": "RESTORE after multiple DML operations.",
      "status": "pass",
      "duration_ms": 405,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:24.411462+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 47,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 227,
      "write_warm_ms": 155,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1405_restore_decimal",
      "num": 1405,
      "name": "restore_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1405_restore_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1405_restore_decimal.py",
      "description": "RESTORE preserves DECIMAL precision.",
      "status": "pass",
      "duration_ms": 643,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:25.055736+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 153,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 106,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1406_restore_timestamp",
      "num": 1406,
      "name": "restore_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1406_restore_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1406_restore_timestamp.py",
      "description": "RESTORE preserves TIMESTAMP microsecond precision.",
      "status": "pass",
      "duration_ms": 772,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:25.828224+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 145,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 106,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1407_restore_to_middle",
      "num": 1407,
      "name": "restore_to_middle",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1407_restore_to_middle.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1407_restore_to_middle.py",
      "description": "RESTORE to a middle version (not V0).",
      "status": "pass",
      "duration_ms": 569,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:26.397654+00:00",
      "read_cold_ms": 47,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 159,
      "write_warm_ms": 168,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1408_restore_partition_typed",
      "num": 1408,
      "name": "restore_partition_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1408_restore_partition_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1408_restore_partition_typed.py",
      "description": "RESTORE with INT-typed partition column.",
      "status": "pass",
      "duration_ms": 940,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:27.338866+00:00",
      "read_cold_ms": 179,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 194,
      "write_warm_ms": 187,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1409_restore_cdc_partition",
      "num": 1409,
      "name": "restore_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1409_restore_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1409_restore_cdc_partition.py",
      "description": "RESTORE with CDC + partition. CDC captures RESTORE changes.",
      "status": "pass",
      "duration_ms": 425,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:27.764569+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 126,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/140_txn_log_interop",
      "num": 140,
      "name": "txn_log_interop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/140_txn_log_interop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_140_txn_log_interop.py",
      "description": "- Transaction log interoperability testing - Multiple versions (0-5) of data writes - Mixed engine history simulation - NO deletion vectors (no table properties)",
      "status": "pass",
      "duration_ms": 169,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:16.914350+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 402,
      "write_warm_ms": 251,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1410_restore_then_dml",
      "num": 1410,
      "name": "restore_then_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1410_restore_then_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1410_restore_then_dml.py",
      "description": "DML operations after RESTORE.",
      "status": "pass",
      "duration_ms": 551,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:28.316273+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 171,
      "write_warm_ms": 175,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1411_zorder_then_vacuum",
      "num": 1411,
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        "dml:insert",
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      "concern_note": null
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    {
      "id": "df-writes/iceberg/1443_restore_cdc_colmap",
      "num": 1443,
      "name": "restore_cdc_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1443_restore_cdc_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1443_restore_cdc_colmap.py",
      "description": "RESTORE + CDC + colmap. Three-way combo.",
      "status": "pass",
      "duration_ms": 316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:45.377829+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 21,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1444_zorder_then_update_delete",
      "num": 1444,
      "name": "zorder_then_update_delete",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1444_zorder_then_update_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1444_zorder_then_update_delete.py",
      "description": "Z-ORDER then UPDATE then DELETE. Full lifecycle.",
      "status": "pass",
      "duration_ms": 590,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:45.968035+00:00",
      "read_cold_ms": 99,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 335,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1445_vacuum_then_merge",
      "num": 1445,
      "name": "vacuum_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1445_vacuum_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1445_vacuum_then_merge.py",
      "description": "VACUUM then MERGE. Tests MERGE after VACUUM cleaned old files.",
      "status": "pass",
      "duration_ms": 998,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:46.966249+00:00",
      "read_cold_ms": 209,
      "read_warm_ms": 127,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
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        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1446_restore_then_merge",
      "num": 1446,
      "name": "restore_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1446_restore_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1446_restore_then_merge.py",
      "description": "RESTORE then MERGE on restored data.",
      "status": "pass",
      "duration_ms": 323,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:47.289644+00:00",
      "read_cold_ms": 43,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 185,
      "write_warm_ms": 217,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1447_zorder_vacuum_restore_lifecycle",
      "num": 1447,
      "name": "zorder_vacuum_restore_lifecycle",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1447_zorder_vacuum_restore_lifecycle.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1447_zorder_vacuum_restore_lifecycle.py",
      "description": "Full lifecycle: INSERT, Z-ORDER, DML, VACUUM, more DML.",
      "status": "pass",
      "duration_ms": 681,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:47.970924+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 400,
      "write_warm_ms": 458,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:time-travel",
        "delta:vacuum",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1448_zorder_all_features",
      "num": 1448,
      "name": "zorder_all_features",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1448_zorder_all_features.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1448_zorder_all_features.py",
      "description": "Z-ORDER + all features: CDC+colmap+partition+constraint+evolve.",
      "status": "pass",
      "duration_ms": 242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:48.213763+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 328,
      "write_warm_ms": 323,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1449_vacuum_all_features",
      "num": 1449,
      "name": "vacuum_all_features",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1449_vacuum_all_features.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1449_vacuum_all_features.py",
      "description": "VACUUM + all features: CDC+colmap+partition+constraint+evolve.",
      "status": "pass",
      "duration_ms": 579,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:48.793373+00:00",
      "read_cold_ms": 24,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 289,
      "write_warm_ms": 283,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:vacuum",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/144_concurrent_writes_interop",
      "num": 144,
      "name": "concurrent_writes_interop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/144_concurrent_writes_interop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_144_concurrent_writes_interop.py",
      "description": "- Concurrent write scenarios with multiple batches - Deletion vectors enabled - Multiple versions (6 total: init + 5 batches) - Timestamp fields with NULL values",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:17.617638+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 51,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 299,
      "write_warm_ms": 272,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1450_zorder_vacuum_restore_ultimate",
      "num": 1450,
      "name": "zorder_vacuum_restore_ultimate",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1450_zorder_vacuum_restore_ultimate.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1450_zorder_vacuum_restore_ultimate.py",
      "description": "ULTIMATE test. INSERT, Z-ORDER, DML, VACUUM, RESTORE lifecycle. All features.",
      "status": "pass",
      "duration_ms": 1012,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:49.806509+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 685,
      "write_warm_ms": 650,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:time-travel",
        "delta:vacuum",
        "delta:z-order",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1451_partition_by_decimal",
      "num": 1451,
      "name": "partition_by_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1451_partition_by_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1451_partition_by_decimal.py",
      "description": "DECIMAL(5,2) partition key. 4 partitions with typed DECIMAL values.",
      "status": "pass",
      "duration_ms": 623,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:50.430403+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 172,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
      "write_warm_ms": 131,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1452_partition_by_decimal_negative",
      "num": 1452,
      "name": "partition_by_decimal_negative",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1452_partition_by_decimal_negative.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1452_partition_by_decimal_negative.py",
      "description": "DECIMAL(8,2) partition key with negative values. 3 partitions: -100.00, 0.00, 500.00.",
      "status": "pass",
      "duration_ms": 747,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:51.179132+00:00",
      "read_cold_ms": 114,
      "read_warm_ms": 115,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 167,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1453_partition_by_decimal_merge",
      "num": 1453,
      "name": "partition_by_decimal_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1453_partition_by_decimal_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1453_partition_by_decimal_merge.py",
      "description": "MERGE across DECIMAL(5,2) partitions. INSERT 80 base rows,",
      "status": "pass",
      "duration_ms": 569,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:51.748481+00:00",
      "read_cold_ms": 85,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 192,
      "write_warm_ms": 166,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1454_partition_by_decimal_cdc",
      "num": 1454,
      "name": "partition_by_decimal_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1454_partition_by_decimal_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1454_partition_by_decimal_cdc.py",
      "description": "DECIMAL(5,2) partition key + CDC. CDF must contain correct DECIMAL partition values.",
      "status": "pass",
      "duration_ms": 408,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:52.157114+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 86,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 180,
      "write_warm_ms": 139,
      "tags": [
        "type:decimal",
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1455_partition_by_date",
      "num": 1455,
      "name": "partition_by_date",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1455_partition_by_date.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1455_partition_by_date.py",
      "description": "DATE partition key. 4 DATE partitions, 30 days apart.",
      "status": "pass",
      "duration_ms": 539,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:52.697220+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 169,
      "tags": [
        "type:date",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1456_partition_by_date_merge",
      "num": 1456,
      "name": "partition_by_date_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1456_partition_by_date_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1456_partition_by_date_merge.py",
      "description": "MERGE across DATE partitions. INSERT 80 base rows,",
      "status": "pass",
      "duration_ms": 657,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:53.354806+00:00",
      "read_cold_ms": 118,
      "read_warm_ms": 142,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 140,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1457_partition_by_date_cdc",
      "num": 1457,
      "name": "partition_by_date_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1457_partition_by_date_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1457_partition_by_date_cdc.py",
      "description": "DATE partition key + CDC. CDF must contain correct DATE partition values.",
      "status": "pass",
      "duration_ms": 490,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:53.845056+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 129,
      "tags": [
        "type:date",
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1458_partition_by_date_evolve",
      "num": 1458,
      "name": "partition_by_date_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1458_partition_by_date_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1458_partition_by_date_evolve.py",
      "description": "DATE partition key + schema evolution. INSERT 60 rows, ALTER ADD COLUMN,",
      "status": "pass",
      "duration_ms": 773,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:52:54.618785+00:00",
      "read_cold_ms": 135,
      "read_warm_ms": 136,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 119,
      "tags": [
        "type:date",
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1459_partition_by_timestamp_string",
      "num": 1459,
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    {
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      "status": "pass",
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      "error": null,
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      "extras": {},
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      "read_warm_ms": 80,
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        "type:date",
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    },
    {
      "id": "df-writes/iceberg/1464_partition_by_decimal_optimize",
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      "status": "pass",
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      "error": null,
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      "read_warm_ms": 50,
      "skip_feature": null,
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        "type:decimal",
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        "dml:insert",
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        "delta:deletion-vectors",
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        "delta:partitioning",
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      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/iceberg/1465_partition_by_date_nmbys",
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      "write_script": "generator/df-sql-iceberg/1465_partition_by_date_nmbys.sql",
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      "description": "DATE partition key + MERGE with NOT MATCHED BY SOURCE DELETE.",
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      "error": null,
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      "extras": {},
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      "read_warm_ms": 101,
      "skip_feature": null,
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      "write_cold_ms": 134,
      "write_warm_ms": 126,
      "tags": [
        "type:date",
        "type:integer",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/1466_update_coalesce",
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      "manual_tags": [],
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    },
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      "id": "df-writes/iceberg/1467_update_nullif",
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      "error": null,
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      "id": "df-writes/iceberg/1468_update_abs",
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      "reader": "spark",
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      "description": "UPDATE SET using ABS to convert negative values to positive.",
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      "error": null,
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        "type:string",
        "dml:delete",
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        "format:iceberg"
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      "read_script": "generator/spark-reads-iceberg/verify_149_identity_column.py",
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      "reader": "spark",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1507_update_where_abs_decimal.sql",
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      "read_warm_ms": 104,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1524_functions_evolve",
      "num": 1524,
      "name": "functions_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1524_functions_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1524_functions_evolve.py",
      "description": "SQL functions on evolved (newly added) column.",
      "status": "pass",
      "duration_ms": 334,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:18.686545+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 104,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 137,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1525_binary_struct_combo",
      "num": 1525,
      "name": "binary_struct_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1525_binary_struct_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1525_binary_struct_combo.py",
      "description": "BINARY + STRUCT in same table. Tests complex type combination",
      "status": "pass",
      "duration_ms": 465,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:19.152108+00:00",
      "read_cold_ms": 145,
      "read_warm_ms": 153,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
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      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1526_coalesce_merge_cdc",
      "num": 1526,
      "name": "coalesce_merge_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1526_coalesce_merge_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1526_coalesce_merge_cdc.py",
      "description": "MERGE with COALESCE + CDC.",
      "status": "pass",
      "duration_ms": 603,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:19.756944+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 308,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 255,
      "write_warm_ms": 274,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1527_functions_zorder",
      "num": 1527,
      "name": "functions_zorder",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1527_functions_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1527_functions_zorder.py",
      "description": "SQL functions in DML then Z-ORDER.",
      "status": "pass",
      "duration_ms": 511,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:20.268471+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 285,
      "write_warm_ms": 258,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1528_functions_vacuum",
      "num": 1528,
      "name": "functions_vacuum",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1528_functions_vacuum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1528_functions_vacuum.py",
      "description": "SQL functions in DML then VACUUM.",
      "status": "pass",
      "duration_ms": 536,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:20.804995+00:00",
      "read_cold_ms": 36,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 347,
      "write_warm_ms": 350,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1529_functions_restore",
      "num": 1529,
      "name": "functions_restore",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1529_functions_restore.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1529_functions_restore.py",
      "description": "SQL functions in DML then RESTORE.",
      "status": "pass",
      "duration_ms": 633,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:21.438902+00:00",
      "read_cold_ms": 137,
      "read_warm_ms": 48,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 114,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/152_uniform_iceberg",
      "num": 152,
      "name": "uniform_iceberg",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/152_uniform_iceberg.sql",
      "read_script": "generator/spark-reads-iceberg/verify_152_uniform_iceberg.py",
      "description": "- Simple table creation (Uniform/UniFormat requires Unity Catalog) - 100 rows with deterministic data - Placeholder for Uniform/Iceberg interop testing",
      "status": "pass",
      "duration_ms": 82,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:20.444640+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 19,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1530_decimal_partition_functions",
      "num": 1530,
      "name": "decimal_partition_functions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1530_decimal_partition_functions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1530_decimal_partition_functions.py",
      "description": "DECIMAL partition + COALESCE function.",
      "status": "pass",
      "duration_ms": 360,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:21.799903+00:00",
      "read_cold_ms": 106,
      "read_warm_ms": 89,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 141,
      "write_warm_ms": 223,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1531_date_partition_functions",
      "num": 1531,
      "name": "date_partition_functions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1531_date_partition_functions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1531_date_partition_functions.py",
      "description": "DATE partition + LENGTH/TRIM/ABS functions.",
      "status": "pass",
      "duration_ms": 353,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:22.153894+00:00",
      "read_cold_ms": 142,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
      "write_warm_ms": 137,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1532_float_cdc",
      "num": 1532,
      "name": "float_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1532_float_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1532_float_cdc.py",
      "description": "INSERT 100 rows. val = CAST(i * 1.5 AS FLOAT). FloatType must be preserved in both main table and CDF.",
      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:22.423174+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 96,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 78,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1533_float_partition",
      "num": 1533,
      "name": "float_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1533_float_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1533_float_partition.py",
      "description": "FLOAT in partitioned table.",
      "status": "pass",
      "duration_ms": 388,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:22.811805+00:00",
      "read_cold_ms": 152,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 189,
      "write_warm_ms": 243,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1534_tinyint_dml_chain",
      "num": 1534,
      "name": "tinyint_dml_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1534_tinyint_dml_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1534_tinyint_dml_chain.py",
      "description": "TINYINT through INSERT/UPDATE/DELETE chain.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:23.071878+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 165,
      "write_warm_ms": 180,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1535_tinyint_merge",
      "num": 1535,
      "name": "tinyint_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1535_tinyint_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1535_tinyint_merge.py",
      "description": "INSERT 80 rows. val = (i*3)%127. MERGE from 100-row CTE: MATCHED -> UPDATE val=source.val ((i*7)%127), name='merged'. NOT MATCHED -> INSERT ids 81-100.",
      "status": "pass",
      "duration_ms": 318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:23.390911+00:00",
      "read_cold_ms": 105,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 128,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1536_smallint_merge",
      "num": 1536,
      "name": "smallint_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1536_smallint_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1536_smallint_merge.py",
      "description": "INSERT 80 rows. val = (i*11)%30000. MERGE from 100-row CTE: MATCHED -> UPDATE val=source.val ((i*19)%30000), name='merged'. NOT MATCHED -> INSERT ids 81-100.",
      "status": "pass",
      "duration_ms": 243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:23.634751+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 102,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1537_smallint_cdc",
      "num": 1537,
      "name": "smallint_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1537_smallint_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1537_smallint_cdc.py",
      "description": "INSERT 100 rows. val = (i*13)%30000.",
      "status": "pass",
      "duration_ms": 334,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:23.969191+00:00",
      "read_cold_ms": 100,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 142,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1538_tinyint_partition",
      "num": 1538,
      "name": "tinyint_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1538_tinyint_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1538_tinyint_partition.py",
      "description": "TINYINT in partitioned table.",
      "status": "pass",
      "duration_ms": 295,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:24.264978+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 302,
      "write_warm_ms": 321,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1539_binary_partition",
      "num": 1539,
      "name": "binary_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1539_binary_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1539_binary_partition.py",
      "description": "BINARY in partitioned table.",
      "status": "pass",
      "duration_ms": 389,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:24.654899+00:00",
      "read_cold_ms": 178,
      "read_warm_ms": 115,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 179,
      "write_warm_ms": 183,
      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/153_int96_timestamp",
      "num": 153,
      "name": "int96_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/153_int96_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_153_int96_timestamp.py",
      "description": "DBX writes table with INT96 timestamps -> DeltaForge reads correctly",
      "status": "pass",
      "duration_ms": 104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:20.549481+00:00",
      "read_cold_ms": 26,
      "read_warm_ms": 21,
      "skip_feature": null,
      "skip_cause": null,
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        "format:iceberg"
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        "type:string",
        "dml:insert",
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        "delta:partitioning",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
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      "manual_tags": [],
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      "manual_tags": [],
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      "write_warm_ms": 78,
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        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/1557_delete_between_timestamp",
      "num": 1557,
      "name": "delete_between_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1557_delete_between_timestamp.py",
      "description": "DELETE WHERE TIMESTAMP BETWEEN range. Tests BETWEEN predicate",
      "status": "pass",
      "duration_ms": 283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:35.739260+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 80,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1558_update_between_timestamp",
      "num": 1558,
      "name": "update_between_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1558_update_between_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1558_update_between_timestamp.py",
      "description": "UPDATE WHERE TIMESTAMP BETWEEN range. Tests BETWEEN predicate",
      "status": "pass",
      "duration_ms": 568,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:36.307518+00:00",
      "read_cold_ms": 167,
      "read_warm_ms": 173,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 89,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1559_delete_not_between_timestamp",
      "num": 1559,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1559_delete_not_between_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1559_delete_not_between_timestamp.py",
      "description": "DELETE WHERE TIMESTAMP NOT BETWEEN. Tests NOT BETWEEN predicate",
      "status": "pass",
      "duration_ms": 564,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:36.872412+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 124,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 82,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/155_vacuum_race",
      "num": 155,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/155_vacuum_race.sql",
      "read_script": "generator/spark-reads-iceberg/verify_155_vacuum_race.py",
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      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:20.957127+00:00",
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      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1583,
      "write_warm_ms": 1906,
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        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1560_delete_between_decimal",
      "num": 1560,
      "name": "delete_between_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1560_delete_between_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1560_delete_between_decimal.py",
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      "duration_ms": 417,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:37.290243+00:00",
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      "read_warm_ms": 117,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
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        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "num": 1561,
      "name": "update_power",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1561_update_power.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1561_update_power.py",
      "description": "UPDATE SET with POWER function. Computes squared and cubed",
      "status": "pass",
      "duration_ms": 348,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:37.639356+00:00",
      "read_cold_ms": 127,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
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        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/1562_update_sqrt",
      "num": 1562,
      "name": "update_sqrt",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1562_update_sqrt.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1562_update_sqrt.py",
      "description": "UPDATE SET with SQRT function. Computes square roots of",
      "status": "pass",
      "duration_ms": 332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:37.972096+00:00",
      "read_cold_ms": 115,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 81,
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        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1563_update_sign",
      "num": 1563,
      "name": "update_sign",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1563_update_sign.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1563_update_sign.py",
      "description": "UPDATE SET with SIGN function. Computes the sign (-1, 0, +1)",
      "status": "pass",
      "duration_ms": 412,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:38.384935+00:00",
      "read_cold_ms": 165,
      "read_warm_ms": 123,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 143,
      "write_warm_ms": 108,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1564_update_mod",
      "num": 1564,
      "name": "update_mod",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1564_update_mod.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1564_update_mod.py",
      "description": "UPDATE SET with modulo (% operator). Computes remainders",
      "status": "pass",
      "duration_ms": 272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:38.657620+00:00",
      "read_cold_ms": 95,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 92,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1565_update_upper_lower",
      "num": 1565,
      "name": "update_upper_lower",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1565_update_upper_lower.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1565_update_upper_lower.py",
      "description": "UPDATE SET with UPPER and LOWER string functions.",
      "status": "pass",
      "duration_ms": 348,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:39.006339+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 105,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 91,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1566_update_lpad_rpad",
      "num": 1566,
      "name": "update_lpad_rpad",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1566_update_lpad_rpad.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1566_update_lpad_rpad.py",
      "description": "UPDATE SET with LPAD and RPAD string functions.",
      "status": "pass",
      "duration_ms": 293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:39.300321+00:00",
      "read_cold_ms": 107,
      "read_warm_ms": 81,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1567_update_reverse",
      "num": 1567,
      "name": "update_reverse",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1567_update_reverse.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1567_update_reverse.py",
      "description": "UPDATE SET with REVERSE string function.",
      "status": "pass",
      "duration_ms": 318,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:39.618538+00:00",
      "read_cold_ms": 100,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1568_update_math_combo",
      "num": 1568,
      "name": "update_math_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1568_update_math_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1568_update_math_combo.py",
      "description": "Multiple math functions in one UPDATE SET clause. Applies",
      "status": "pass",
      "duration_ms": 273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:39.892409+00:00",
      "read_cold_ms": 96,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 126,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1569_update_string_combo",
      "num": 1569,
      "name": "update_string_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1569_update_string_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1569_update_string_combo.py",
      "description": "Multiple string functions in one UPDATE SET clause. Applies",
      "status": "pass",
      "duration_ms": 552,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:40.445249+00:00",
      "read_cold_ms": 100,
      "read_warm_ms": 161,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/156_stale_checkpoint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_156_stale_checkpoint.py",
      "description": "Stale checkpoint handling with multiple UPDATE operations",
      "status": "pass",
      "duration_ms": 303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:21.261218+00:00",
      "read_cold_ms": 152,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 853,
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      "tags": [
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        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
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        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
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      "num": 1570,
      "name": "delete_where_power",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1570_delete_where_power.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1570_delete_where_power.py",
      "description": "DELETE WHERE POWER(col, 2) > threshold. Tests math function",
      "status": "pass",
      "duration_ms": 311,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:40.756657+00:00",
      "read_cold_ms": 91,
      "read_warm_ms": 145,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 75,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1571_delete_where_sqrt",
      "num": 1571,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1571_delete_where_sqrt.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1571_delete_where_sqrt.py",
      "description": "DELETE WHERE SQRT(col) < threshold. Tests SQRT function",
      "status": "pass",
      "duration_ms": 162,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:40.919156+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 87,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1572_merge_power_sqrt",
      "num": 1572,
      "name": "merge_power_sqrt",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1572_merge_power_sqrt.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1572_merge_power_sqrt.py",
      "description": "MERGE with POWER and SQRT in UPDATE SET clause. Tests math",
      "status": "pass",
      "duration_ms": 329,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:53:41.248409+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 159,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
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      "tags": [
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1573_update_sign_decimal",
      "num": 1573,
      "name": "update_sign_decimal",
      "type": "df-writes",
      "format": "iceberg",
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    },
    {
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      "extras": {},
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        "iceberg:uniform",
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      "manual_tags": [],
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    },
    {
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      "extras": {},
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
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        "type:integer",
        "type:string",
        "type:unicode",
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        "delta:column-mapping",
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        "format:iceberg"
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    {
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      "notes": null,
      "extras": {},
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      "skip_feature": null,
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        "dml:insert",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/1658_partdir_int_negative",
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      "notes": null,
      "extras": {},
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      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1659_partdir_int_large",
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      "status": "pass",
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      "error": null,
      "notes": null,
      "extras": {},
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      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/165_domain_metadata",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_165_domain_metadata.py",
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      "duration_ms": 136,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
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        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1660_partdir_int_null",
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      "error": null,
      "notes": null,
      "extras": {},
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      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1661_partdir_int_zero",
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      "duration_ms": 512,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:32.975330+00:00",
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      "read_warm_ms": 89,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1662_partdir_int_dml",
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      "duration_ms": 1190,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 224,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/1663_partdir_boolean_basic",
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      "notes": null,
      "extras": {},
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      "read_warm_ms": 30,
      "skip_feature": null,
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/1664_partdir_boolean_null",
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      "skip_feature": null,
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        "type:integer",
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        "delta:column-mapping",
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        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1665_partdir_boolean_dml",
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      "read_warm_ms": 91,
      "skip_feature": null,
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        "type:boolean",
        "type:integer",
        "dml:delete",
        "dml:insert",
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        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1666_partdir_decimal_basic",
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      "duration_ms": 561,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_warm_ms": 51,
      "skip_feature": null,
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        "delta:column-mapping",
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        "iceberg:uniform",
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      "manual_tags": [],
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      "id": "df-writes/iceberg/1667_partdir_decimal_negative",
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      "error": null,
      "notes": null,
      "extras": {},
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      "skip_feature": null,
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        "dml:insert",
        "delta:column-mapping",
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      "manual_tags": [],
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      "error": null,
      "notes": null,
      "extras": {},
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      "manual_tags": [],
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      "error": null,
      "notes": null,
      "extras": {},
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      "skip_feature": null,
      "skip_cause": null,
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      "manual_tags": [],
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      "error": null,
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        "dml:update",
        "delta:column-mapping",
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    {
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        "type:integer",
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        "format:iceberg"
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      "description": "2 INSERT batches, UPDATE raises the minimum in batch 1.",
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        "type:integer",
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        "dml:insert",
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        "direction:df-writes",
        "format:iceberg"
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        "type:integer",
        "type:string",
        "dml:insert",
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        "format:iceberg"
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        "type:integer",
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        "dml:delete",
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        "format:iceberg"
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        "dml:insert",
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        "direction:df-writes",
        "format:iceberg"
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      "concern_note": null
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/iceberg/1693_stats_after_schema_evolve",
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      "writer": "delta-forge",
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      "notes": null,
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        "type:integer",
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        "dml:insert",
        "delta:column-mapping",
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        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
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      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "extras": {},
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      "manual_tags": [],
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      "format": "iceberg",
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      "writer": "delta-forge",
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      "duration_ms": 374,
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        "type:integer",
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        "format:iceberg"
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      "manual_tags": [],
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      "read_script": "generator/spark-reads-iceberg/verify_1699_stats_wide_range.py",
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      "duration_ms": 278,
      "error": null,
      "notes": null,
      "extras": {},
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        "scale:large-dataset",
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      "manual_tags": [],
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      "description": "- Creating file fragmentation with multiple INSERT statements - Multiple small batches for OPTIMIZE testing - No partitioning",
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      "notes": null,
      "extras": {},
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      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
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      "manual_tags": [],
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
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      "extras": {},
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      "manual_tags": [],
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      "id": "df-writes/iceberg/1703_stats_cdc",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1703_stats_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1703_stats_cdc.py",
      "description": "CDC table with 2 INSERT batches. Stats on data files (not CDC files).",
      "status": "pass",
      "duration_ms": 188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:55.321741+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 124,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1704_stats_decimal_multi_precision",
      "num": 1704,
      "name": "stats_decimal_multi_precision",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1704_stats_decimal_multi_precision.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1704_stats_decimal_multi_precision.py",
      "description": "2 INSERT batches with DECIMAL(10,2) + DECIMAL(18,8). Both column stats correct.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:55.575747+00:00",
      "read_cold_ms": 93,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 108,
      "write_warm_ms": 102,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1705_stats_after_multiple_dml",
      "num": 1705,
      "name": "stats_after_multiple_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1705_stats_after_multiple_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1705_stats_after_multiple_dml.py",
      "description": "INSERT 2 batches, UPDATE, DELETE, INSERT 1 more.",
      "status": "pass",
      "duration_ms": 973,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:56.549554+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 222,
      "write_warm_ms": 207,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1706_pushdown_int_eq",
      "num": 1706,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1706_pushdown_int_eq.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1706_pushdown_int_eq.py",
      "description": "5 non-overlapping INT batches. WHERE score=42 should only scan 1 file.",
      "status": "pass",
      "duration_ms": 962,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:57.512929+00:00",
      "read_cold_ms": 117,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 229,
      "write_warm_ms": 213,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1707_pushdown_int_gt",
      "num": 1707,
      "name": "pushdown_int_gt",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1707_pushdown_int_gt.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1707_pushdown_int_gt.py",
      "description": "5 non-overlapping INT batches. WHERE score > 60 should skip batches 1-3.",
      "status": "pass",
      "duration_ms": 616,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:58.130084+00:00",
      "read_cold_ms": 108,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
      "write_warm_ms": 210,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1708_pushdown_int_lt",
      "num": 1708,
      "name": "pushdown_int_lt",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1708_pushdown_int_lt.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1708_pushdown_int_lt.py",
      "description": "5 non-overlapping INT batches. WHERE score < 40 should skip batches 3-5.",
      "status": "pass",
      "duration_ms": 507,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:58.637994+00:00",
      "read_cold_ms": 48,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 200,
      "write_warm_ms": 261,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1709_pushdown_decimal_eq",
      "num": 1709,
      "name": "pushdown_decimal_eq",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1709_pushdown_decimal_eq.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1709_pushdown_decimal_eq.py",
      "description": "3 non-overlapping DECIMAL batches. WHERE amount = CAST(250.00 AS DECIMAL(10,2)).",
      "status": "pass",
      "duration_ms": 658,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:54:59.297075+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 141,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/170_dbx_delete_from_deltaflow",
      "num": 170,
      "name": "dbx_delete_from_deltaflow",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/170_dbx_delete_from_deltaflow.sql",
      "read_script": "generator/spark-reads-iceberg/verify_170_dbx_delete_from_deltaflow.py",
      "description": "- Table with status field for DELETE testing - 25% of rows marked as 'inactive' for deletion - No partitioning",
      "status": "pass",
      "duration_ms": 68,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:26.094919+00:00",
      "read_cold_ms": 22,
      "read_warm_ms": 18,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 34,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1710_pushdown_decimal_gt",
      "num": 1710,
      "name": "pushdown_decimal_gt",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1710_pushdown_decimal_gt.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1710_pushdown_decimal_gt.py",
      "description": "3 non-overlapping DECIMAL batches. WHERE amount > CAST(300.00 AS DECIMAL(10,2)).",
      "status": "pass",
      "duration_ms": 922,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:00.219974+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 165,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1711_pushdown_timestamp_range",
      "num": 1711,
      "name": "pushdown_timestamp_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1711_pushdown_timestamp_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1711_pushdown_timestamp_range.py",
      "description": "3 batches (Jan/Feb/Mar 2024). WHERE ts BETWEEN Feb boundaries.",
      "status": "pass",
      "duration_ms": 656,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:00.878226+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 142,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 133,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1712_pushdown_string_range",
      "num": 1712,
      "name": "pushdown_string_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1712_pushdown_string_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1712_pushdown_string_range.py",
      "description": "3 batches (a_*/b_*/c_*). WHERE name >= 'b_' AND name < 'c_'.",
      "status": "pass",
      "duration_ms": 387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:01.265450+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 154,
      "write_warm_ms": 130,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1713_pushdown_boolean",
      "num": 1713,
      "name": "pushdown_boolean",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1713_pushdown_boolean.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1713_pushdown_boolean.py",
      "description": "2 batches (all true / all false). WHERE flag = true.",
      "status": "pass",
      "duration_ms": 623,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:01.890880+00:00",
      "read_cold_ms": 51,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 81,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1714_pushdown_null",
      "num": 1714,
      "name": "pushdown_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1714_pushdown_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1714_pushdown_null.py",
      "description": "2 batches (non-NULL / 50% NULL). WHERE score IS NOT NULL.",
      "status": "pass",
      "duration_ms": 993,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:02.885423+00:00",
      "read_cold_ms": 102,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 107,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1715_pushdown_compound",
      "num": 1715,
      "name": "pushdown_compound",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1715_pushdown_compound.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1715_pushdown_compound.py",
      "description": "3 batches. WHERE score > 60 AND amount > CAST(400 AS DECIMAL(10,2)).",
      "status": "pass",
      "duration_ms": 795,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:03.681115+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 163,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1716_pushdown_after_update",
      "num": 1716,
      "name": "pushdown_after_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1716_pushdown_after_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1716_pushdown_after_update.py",
      "description": "5 batches, UPDATE shifts batch 3 scores +500. WHERE score > 500.",
      "status": "pass",
      "duration_ms": 510,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:04.192316+00:00",
      "read_cold_ms": 85,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 284,
      "write_warm_ms": 287,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1717_pushdown_after_delete",
      "num": 1717,
      "name": "pushdown_after_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1717_pushdown_after_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1717_pushdown_after_delete.py",
      "description": "5 batches, DELETE all of batch 3 via DV.",
      "status": "pass",
      "duration_ms": 688,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:04.880599+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 108,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 249,
      "write_warm_ms": 302,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1718_pushdown_negative",
      "num": 1718,
      "name": "pushdown_negative",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1718_pushdown_negative.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1718_pushdown_negative.py",
      "description": "2 batches (negative / positive). WHERE val > 0.",
      "status": "pass",
      "duration_ms": 600,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:05.481099+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 114,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 109,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1719_pushdown_decimal_between",
      "num": 1719,
      "name": "pushdown_decimal_between",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1719_pushdown_decimal_between.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1719_pushdown_decimal_between.py",
      "description": "3 disjoint DECIMAL batches. WHERE amount BETWEEN 200 AND 300.",
      "status": "pass",
      "duration_ms": 317,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:05.799193+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 145,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/171_reverse_dbx_delete_from_deltaflow",
      "num": 171,
      "name": "reverse_dbx_delete_from_deltaflow",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/171_reverse_dbx_delete_from_deltaflow.sql",
      "read_script": "generator/spark-reads-iceberg/verify_171_reverse_dbx_delete_from_deltaflow.py",
      "description": "because it expects this DeltaForge-created table to exist first!",
      "status": "pass",
      "duration_ms": 86,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:26.182165+00:00",
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      "duration_ms": 134,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:32.152597+00:00",
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      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
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        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1789_binary_zorder",
      "num": 1789,
      "name": "binary_zorder",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1789_binary_zorder.py",
      "description": "BINARY column survives Z-ORDER on a sibling column.",
      "status": "pass",
      "duration_ms": 109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:32.262641+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 23,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 185,
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      "tags": [
        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/178_v2_checkpoints",
      "num": 178,
      "name": "v2_checkpoints",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_178_v2_checkpoints.py",
      "description": "- V2 checkpoint format compatibility across engines - Multiple data operations: INSERT, UPDATE, DELETE, OPTIMIZE - Deletion vectors enabled",
      "status": "pass",
      "duration_ms": 110,
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      "notes": null,
      "extras": {},
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      "read_cold_ms": 40,
      "read_warm_ms": 18,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
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      "concern_note": null
    },
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      "id": "df-writes/iceberg/1790_binary_vacuum",
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      "name": "binary_vacuum",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1790_binary_vacuum.py",
      "description": "BINARY column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:32.532777+00:00",
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      "read_warm_ms": 113,
      "skip_feature": null,
      "skip_cause": null,
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        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1791_binary_restore",
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      "name": "binary_restore",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1791_binary_restore.py",
      "description": "RESTORE undoes UPDATE on table containing BINARY column.",
      "status": "pass",
      "duration_ms": 255,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:32.788859+00:00",
      "read_cold_ms": 51,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 133,
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        "type:binary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
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        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1792_tinyint_cdc",
      "num": 1792,
      "name": "tinyint_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1792_tinyint_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1792_tinyint_cdc.py",
      "description": "TINYINT values captured by Change Data Feed across INSERT/UPDATE/DELETE.",
      "status": "pass",
      "duration_ms": 292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:33.081755+00:00",
      "read_cold_ms": 98,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 104,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1793_tinyint_zorder",
      "num": 1793,
      "name": "tinyint_zorder",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1793_tinyint_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1793_tinyint_zorder.py",
      "description": "TINYINT column with Z-ORDER BY (val).",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:33.291457+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 247,
      "write_warm_ms": 239,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1794_tinyint_vacuum",
      "num": 1794,
      "name": "tinyint_vacuum",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1794_tinyint_vacuum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1794_tinyint_vacuum.py",
      "description": "TINYINT column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 216,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:33.507923+00:00",
      "read_cold_ms": 54,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 238,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1795_smallint_cdc_partition",
      "num": 1795,
      "name": "smallint_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1795_smallint_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1795_smallint_cdc_partition.py",
      "description": "SMALLINT column on CDF + PARTITIONED table.",
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      "duration_ms": 430,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 136,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 211,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/1796_float_zorder",
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      "name": "float_zorder",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1796_float_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1796_float_zorder.py",
      "description": "FLOAT column with Z-ORDER BY (val).",
      "status": "pass",
      "duration_ms": 292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:34.231246+00:00",
      "read_cold_ms": 157,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1797_float_vacuum",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1797_float_vacuum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1797_float_vacuum.py",
      "description": "FLOAT column survives OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 189,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:34.421415+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 246,
      "write_warm_ms": 234,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1798_float_restore",
      "num": 1798,
      "name": "float_restore",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1798_float_restore.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1798_float_restore.py",
      "description": "RESTORE undoes UPDATE on FLOAT values.",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:34.654298+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1799_struct_cdc_zorder",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1799_struct_cdc_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1799_struct_cdc_zorder.py",
      "description": "STRUCT + CDC + Z-ORDER three-way combination.",
      "status": "pass",
      "duration_ms": 148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:34.803187+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 20,
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      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "delta:z-order",
        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/179_type_widening_interop",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_179_type_widening_interop.py",
      "description": "- Type widening schema evolution compatibility - SHORT (INT16) -> INT (INT32) widening - INT (INT32) -> LONG (INT64) widening - Type widening table property",
      "status": "pass",
      "duration_ms": 247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:27.322761+00:00",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/17_action_cdc_file_partitioned",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Partitioned by store_id for efficient per-location change tracking.",
      "status": "pass",
      "duration_ms": 689,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:24:28.012272+00:00",
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      "skip_feature": null,
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        "type:boolean",
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        "delta:column-mapping",
        "delta:deletion-vectors",
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      "writer": "delta-forge",
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      "description": "All four complex/non-trivial types (STRUCT, BINARY, DATE, FLOAT)",
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      "duration_ms": 174,
      "error": null,
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      "tags": [
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        "type:floating",
        "type:integer",
        "type:string",
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        "dml:insert",
        "delta:column-mapping",
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      "manual_tags": [],
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      "id": "df-writes/iceberg/1801_delete_where_sign",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1801_delete_where_sign.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1801_delete_where_sign.py",
      "description": "DELETE WHERE SIGN(value) = -1.",
      "status": "pass",
      "duration_ms": 374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:55:35.352540+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 227,
      "skip_feature": null,
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      "tags": [
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        "dml:delete",
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      "manual_tags": [],
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    },
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1802_delete_where_upper.py",
      "description": "DELETE WHERE UPPER(name) = 'ITEM_50'.",
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      "duration_ms": 162,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 44,
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      "tags": [
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        "dml:delete",
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      "manual_tags": [],
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    },
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      "id": "df-writes/iceberg/1803_delete_where_length",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
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      "read_script": "generator/spark-reads-iceberg/verify_1888_to_timestamp.py",
      "description": "TO_TIMESTAMP(string) parsing timestamp string.",
      "status": "pass",
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      "notes": null,
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      ],
      "manual_tags": [],
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    },
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      "status": "pass",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
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      "status": "pass",
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        "type:timestamp",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 312,
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      "verified_at": "2026-05-20T11:56:02.096276+00:00",
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        "type:timestamp",
        "dml:cdc-write",
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      ],
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    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "verified_at": "2026-05-20T11:56:02.331852+00:00",
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        "direction:df-writes",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1894_date_in_partition",
      "num": 1894,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1894_date_in_partition.py",
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1895_timestamp_minute_extract",
      "num": 1895,
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      "writer": "delta-forge",
      "reader": "spark",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1896_date_function_chain",
      "num": 1896,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1896_date_function_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1896_date_function_chain.py",
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      "duration_ms": 407,
      "error": null,
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      "extras": {},
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1897_date_in_merge",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1897_date_in_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1897_date_in_merge.py",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/1898_date_function_constraint",
      "num": 1898,
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      ],
      "manual_tags": [],
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    },
    {
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      "read_script": "generator/spark-reads-iceberg/verify_1899_date_extract_evolve.py",
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      "verified_at": "2026-05-20T11:56:05.045451+00:00",
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      ],
      "manual_tags": [],
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    },
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      "read_script": "generator/spark-reads-iceberg/verify_189_photon_engine.py",
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      "extras": {},
      "verified_at": "2026-05-20T12:24:38.976329+00:00",
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    },
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      "manual_tags": [],
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    },
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        "format:iceberg"
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
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      "reader": "spark",
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      ],
      "manual_tags": [],
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    },
    {
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1912_set_tblproperties_dv.py",
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      "manual_tags": [],
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    {
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      "manual_tags": [],
      "concern_note": null
    },
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1914_unset_tblproperties.py",
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      "duration_ms": 449,
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      "manual_tags": [],
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    },
    {
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      "error": null,
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      "read_script": "generator/spark-reads-iceberg/verify_1916_alter_column_int_to_long.py",
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      "duration_ms": 383,
      "error": null,
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      "manual_tags": [],
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    },
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1917_drop_constraint_then_violate.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1917_drop_constraint_then_violate.py",
      "description": "ADD CONSTRAINT, INSERT valid, DROP CONSTRAINT, INSERT",
      "status": "pass",
      "duration_ms": 397,
      "error": null,
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      "verified_at": "2026-05-20T11:56:13.152176+00:00",
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      "manual_tags": [],
      "concern_note": null
    },
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      "num": 1918,
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1918_set_tblproperties_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1918_set_tblproperties_partition.py",
      "description": "SET TBLPROPERTIES on a partitioned table mid-life.",
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      "duration_ms": 251,
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      "manual_tags": [],
      "concern_note": null
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    {
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      "num": 1919,
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1919_set_then_unset.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1919_set_then_unset.py",
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      "duration_ms": 597,
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      "tags": [
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "num": 191,
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/191_partial_checkpoint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_191_partial_checkpoint.py",
      "description": "Partial checkpoint write handling and recovery CATEGORIES = [\"electronics\", \"clothing\", \"home\", \"sports\", \"books\", \"toys\"]",
      "status": "pass",
      "duration_ms": 2762,
      "error": null,
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      "verified_at": "2026-05-20T12:24:43.119357+00:00",
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        "type:string",
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        "delta:column-mapping",
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    {
      "id": "df-writes/iceberg/1971_drop_col_then_dml",
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        "dml:insert",
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    },
    {
      "id": "df-writes/iceberg/1972_drop_col_then_optimize",
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      "duration_ms": 254,
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    },
    {
      "id": "df-writes/iceberg/1973_drop_col_then_zorder",
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    },
    {
      "id": "df-writes/iceberg/1974_drop_col_with_cdc",
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      "reader": "spark",
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      "extras": {},
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      "manual_tags": [],
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    {
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      "status": "pass",
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1977_drop_col_then_add_same_name",
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      "id": "df-writes/iceberg/1978_gencol_basic_arith",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1978_gencol_basic_arith.py",
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      "status": "pass",
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      "error": null,
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      "verified_at": "2026-05-20T11:56:44.161200+00:00",
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1979_gencol_string_concat",
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      "manual_tags": [],
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      "id": "df-writes/iceberg/197_nested_schema_modify",
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    {
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    {
      "id": "df-writes/iceberg/1981_gencol_after_update",
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      "read_script": "generator/spark-reads-iceberg/verify_1981_gencol_after_update.py",
      "description": "Generated column auto-recomputed after UPDATE on base column.",
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      "duration_ms": 272,
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      "extras": {},
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1982_gencol_partition_key",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1982_gencol_partition_key.py",
      "description": "Generated column used as a partition key (id_bucket = id % 6).",
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      "duration_ms": 123,
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1983_gencol_with_constraint",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1983_gencol_with_constraint.py",
      "description": "Generated column combined with a CHECK constraint on it.",
      "status": "pass",
      "duration_ms": 100,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/1984_gencol_with_cdc",
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      "write_script": "generator/df-sql-iceberg/1984_gencol_with_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1984_gencol_with_cdc.py",
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      "duration_ms": 372,
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      "extras": {},
      "verified_at": "2026-05-20T11:56:45.444246+00:00",
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    {
      "id": "df-writes/iceberg/1985_gencol_decimal_calc",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1985_gencol_decimal_calc.py",
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      "id": "df-writes/iceberg/1986_widen_byte_to_short",
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      "read_script": "generator/spark-reads-iceberg/verify_1986_widen_byte_to_short.py",
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    {
      "id": "df-writes/iceberg/1987_widen_short_to_int",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1987_widen_short_to_int.py",
      "description": "Type widening from SMALLINT to INT.",
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      "duration_ms": 265,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-20T11:56:46.160862+00:00",
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        "delta:column-mapping",
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        "schema:type-widening",
        "scale:wide-schema",
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        "format:iceberg"
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    },
    {
      "id": "df-writes/iceberg/1988_widen_int_to_long_partition",
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        "dml:insert",
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        "delta:partitioning",
        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1989_widen_float_to_double",
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "schema:type-widening",
        "scale:wide-schema",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/198_map_array_types",
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        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
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    },
    {
      "id": "df-writes/iceberg/1990_widen_decimal_scale_up",
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      "extras": {},
      "verified_at": "2026-05-20T11:56:46.941729+00:00",
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      "read_warm_ms": 33,
      "skip_feature": null,
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      "write_cold_ms": 110,
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "schema:type-widening",
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        "format:iceberg"
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    },
    {
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      "reader": "spark",
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      "duration_ms": 136,
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      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:56:47.078067+00:00",
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      "skip_cause": null,
      "write_cold_ms": 82,
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "schema:type-widening",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1992_widen_then_optimize.sql",
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      "duration_ms": 148,
      "error": null,
      "notes": null,
      "extras": {},
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      "skip_feature": null,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
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      "write_cold_ms": 292,
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    },
    {
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1993_widen_then_zorder.py",
      "description": "INT -> BIGINT widening followed by OPTIMIZE ZORDER BY widened col.",
      "status": "pass",
      "duration_ms": 232,
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        "delta:deletion-vectors",
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        "delta:z-order",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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      "write_cold_ms": 147,
      "write_warm_ms": 172
    },
    {
      "id": "df-writes/iceberg/1994_collation_unicode_basic",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1994_collation_unicode_basic.py",
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      "error": null,
      "notes": null,
      "extras": {},
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/1995_collation_case_insensitive",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_1995_collation_case_insensitive.py",
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      "duration_ms": 262,
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/1996_collation_filter_pushdown",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1996_collation_filter_pushdown.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1996_collation_filter_pushdown.py",
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      "extras": {},
      "verified_at": "2026-05-20T11:56:48.142735+00:00",
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        "dml:delete",
        "dml:insert",
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      "manual_tags": [],
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    },
    {
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      "manual_tags": [],
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    },
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      "id": "df-writes/iceberg/1998_collation_partition_key",
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      "error": null,
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      "extras": {},
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      "manual_tags": [],
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    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/1999_collation_after_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_1999_collation_after_dml.py",
      "description": "Collations feature + INSERT/UPDATE/DELETE workflow.",
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      "duration_ms": 369,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T11:56:49.075721+00:00",
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      "manual_tags": [],
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    },
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      "write_script": "generator/df-sql-iceberg/199_log_compaction.sql",
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      "duration_ms": 235,
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      "duration_ms": 156,
      "error": null,
      "notes": null,
      "extras": {},
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        "delta:column-mapping",
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      "manual_tags": [],
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2002_domain_rowtrack_combined.py",
      "description": "domainMetadata + rowTracking features together with CLUSTER BY.",
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      "duration_ms": 107,
      "error": null,
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      "extras": {},
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      "skip_feature": null,
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      "id": "df-writes/iceberg/2003_domain_persist_through_restore",
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      "id": "df-writes/iceberg/2004_identity_after_optimize",
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      "id": "df-writes/iceberg/2005_identity_after_merge_insert",
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      "id": "df-writes/iceberg/2006_identity_with_partition",
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      "id": "df-writes/iceberg/2008_identity_with_cdc",
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      "id": "df-writes/iceberg/2009_identity_after_delete_resume",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2036_set_tblprop_targetfilesize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2036_set_tblprop_targetfilesize.py",
      "description": "SET delta.targetFileSize then INSERT and OPTIMIZE.",
      "status": "pass",
      "duration_ms": 116,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:16.483912+00:00",
      "read_cold_ms": 37,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2037_set_tblprop_checkpointinterval",
      "num": 2037,
      "name": "set_tblprop_checkpointinterval",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2037_set_tblprop_checkpointinterval.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2037_set_tblprop_checkpointinterval.py",
      "description": "SET delta.checkpointInterval=5, force 12 commits via small INSERTs.",
      "status": "pass",
      "duration_ms": 158,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:16.643298+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 46,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1055,
      "write_warm_ms": 976,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2038_set_tblprop_autoOptimize",
      "num": 2038,
      "name": "set_tblprop_autoOptimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2038_set_tblprop_autoOptimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2038_set_tblprop_autoOptimize.py",
      "description": "SET delta.autoOptimize.optimizeWrite=true, then INSERT 100.",
      "status": "pass",
      "duration_ms": 114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:16.758236+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2039_unset_then_set_again",
      "num": 2039,
      "name": "unset_then_set_again",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2039_unset_then_set_again.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2039_unset_then_set_again.py",
      "description": "SET delta.enableChangeDataFeed=true, UNSET, then SET again.",
      "status": "pass",
      "duration_ms": 166,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:16.925036+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 164,
      "write_warm_ms": 170,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/203_null_partition_values",
      "num": 203,
      "name": "null_partition_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/203_null_partition_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_203_null_partition_values.py",
      "description": "CHAOS TEST WORKFLOW (NULL Values in Partition Columns): This test verifies DeltaForge correctly handles NULL values in partition columns. NULLs are stored in special __HIVE_DEFAULT_PARTITION__ directory.",
      "status": "pass",
      "duration_ms": 87,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:00.064483+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 24,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 47,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2040_set_appendonly_then_violate",
      "num": 2040,
      "name": "set_appendonly_then_violate",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2040_set_appendonly_then_violate.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2040_set_appendonly_then_violate.py",
      "description": "SET delta.appendOnly=true; INSERT 50; subsequent INSERT works.",
      "status": "pass",
      "duration_ms": 147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:17.206022+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 124,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2041_uniform_iceberg_basic",
      "num": 2041,
      "name": "uniform_iceberg_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2041_uniform_iceberg_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2041_uniform_iceberg_basic.py",
      "description": "Cluster O - UniForm Iceberg basic Enable UniForm Iceberg, INSERT 50 rows, verify metadata.",
      "status": "pass",
      "duration_ms": 164,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:17.371950+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 43,
      "write_warm_ms": 41,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2042_uniform_iceberg_after_dml",
      "num": 2042,
      "name": "uniform_iceberg_after_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2042_uniform_iceberg_after_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2042_uniform_iceberg_after_dml.py",
      "description": "Cluster O - UniForm Iceberg + DML INSERT 100, UPDATE 30, DELETE 20. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 336,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:17.709056+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 131,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2043_uniform_iceberg_partitioned",
      "num": 2043,
      "name": "uniform_iceberg_partitioned",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2043_uniform_iceberg_partitioned.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2043_uniform_iceberg_partitioned.py",
      "description": "Cluster O - UniForm Iceberg + PARTITIONED BY region 80 rows distributed across 4 regions.",
      "status": "pass",
      "duration_ms": 227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:17.936660+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2044_uniform_iceberg_after_optimize",
      "num": 2044,
      "name": "uniform_iceberg_after_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2044_uniform_iceberg_after_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2044_uniform_iceberg_after_optimize.py",
      "description": "Cluster O - UniForm Iceberg + 5 INSERTs of 20 + OPTIMIZE",
      "status": "pass",
      "duration_ms": 159,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:18.096211+00:00",
      "read_cold_ms": 31,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 243,
      "write_warm_ms": 290,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2045_uniform_iceberg_typed_columns",
      "num": 2045,
      "name": "uniform_iceberg_typed_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2045_uniform_iceberg_typed_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2045_uniform_iceberg_typed_columns.py",
      "description": "Cluster O - UniForm Iceberg with diverse column types 60 rows. INT, BIGINT, STRING, DECIMAL(10,2), DATE, TIMESTAMP, BOOLEAN.",
      "status": "pass",
      "duration_ms": 190,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:18.287491+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 55,
      "write_warm_ms": 47,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2046_uniform_iceberg_evolution",
      "num": 2046,
      "name": "uniform_iceberg_evolution",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2046_uniform_iceberg_evolution.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2046_uniform_iceberg_evolution.py",
      "description": "Cluster O - UniForm Iceberg + schema evolution INSERT 50, ALTER ADD COLUMN extra, INSERT 30. Final: 80 rows.",
      "status": "pass",
      "duration_ms": 197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:18.485426+00:00",
      "read_cold_ms": 48,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2047_ict_rowtrack_combined",
      "num": 2047,
      "name": "ict_rowtrack_combined",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2047_ict_rowtrack_combined.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2047_ict_rowtrack_combined.py",
      "description": "Cluster P - ICT + rowTracking + INSERT 100 + UPDATE 30",
      "status": "pass",
      "duration_ms": 333,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:18.818901+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 72,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:row-tracking",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2048_gencol_rowtrack_optimize",
      "num": 2048,
      "name": "gencol_rowtrack_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2048_gencol_rowtrack_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2048_gencol_rowtrack_optimize.py",
      "description": "Cluster P - generated col + rowTracking + 5 INSERTs of 20 + OPTIMIZE",
      "status": "pass",
      "duration_ms": 181,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:19.001604+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 282,
      "write_warm_ms": 295,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:generated-columns",
        "delta:optimize",
        "delta:row-tracking",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2049_widen_evolve_cdc",
      "num": 2049,
      "name": "widen_evolve_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2049_widen_evolve_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2049_widen_evolve_cdc.py",
      "description": "Cluster P - type widening + ALTER ADD COLUMN + CDF INSERT 80, ALTER widen score INT->BIGINT, ALTER add col extra, INSERT 20.",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:19.209187+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
      "write_warm_ms": 131,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "iceberg:uniform",
        "schema:add-column",
        "schema:type-widening",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/204_unicode_data",
      "num": 204,
      "name": "unicode_data",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/204_unicode_data.sql",
      "read_script": "generator/spark-reads-iceberg/verify_204_unicode_data.py",
      "description": "Validates Unicode data table with 50 rows containing 20 distinct language samples including CJK, Arabic, Hebrew, emoji, math symbols, zero-width spaces, and supplementary plane characters. Rows 21-50 are duplicates with modified text.",
      "status": "pass",
      "duration_ms": 86,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:00.150763+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 26,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 98,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2050_domain_clustering_uniform",
      "num": 2050,
      "name": "domain_clustering_uniform",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2050_domain_clustering_uniform.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2050_domain_clustering_uniform.py",
      "description": "Cluster P - clustering domain + UniForm Iceberg + INSERT 80 Liquid clustering produces a domainMetadata action. Combined with UniForm.",
      "status": "pass",
      "duration_ms": 167,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:19.507201+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:liquid-clustering",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2051_create_or_replace_basic",
      "num": 2051,
      "name": "create_or_replace_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2051_create_or_replace_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2051_create_or_replace_basic.py",
      "description": "CREATE OR REPLACE TABLE replaces existing table contents.",
      "status": "pass",
      "duration_ms": 119,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:19.626944+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 141,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2052_create_or_replace_schema_change",
      "num": 2052,
      "name": "create_or_replace_schema_change",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "writer": "delta-forge",
      "reader": "spark",
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    {
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    {
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2129_zorder_after_delete.py",
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      "manual_tags": [],
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      "notes": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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        "type:integer",
        "type:string",
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    },
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        "format:iceberg"
      ],
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    },
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
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      ],
      "manual_tags": [],
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    },
    {
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      "format": "iceberg",
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      "reader": "spark",
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        "type:integer",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "error": null,
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      "extras": {},
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        "type:integer",
        "type:string",
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        "dml:insert",
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        "delta:change-data-feed",
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        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/2142_time_travel_v0",
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      "description": "Two INSERTs creating versions 1 and 2; query VERSION AS OF 1 returns the first batch.",
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      "extras": {},
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        "dml:insert",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
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      "write_script": "generator/df-sql-iceberg/2143_time_travel_v1.sql",
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      "extras": {},
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        "delta:column-mapping",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/2144_time_travel_after_delete",
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        "type:integer",
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        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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    {
      "id": "df-writes/iceberg/2145_time_travel_after_update",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2145_time_travel_after_update.py",
      "description": "INSERT then UPDATE. Time travel to pre-update returns original status.",
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      "duration_ms": 198,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 66,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2146_time_travel_with_evolution.sql",
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      "description": "ADD COLUMN then time travel to pre-evolution -- old version has fewer columns.",
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      "duration_ms": 166,
      "error": null,
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      "extras": {},
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2147_time_travel_with_cdc.py",
      "description": "CDC-enabled table; multiple versions; time travel returns historical states.",
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      "error": null,
      "notes": null,
      "extras": {},
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2148_restore_to_version.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2148_restore_to_version.py",
      "description": "INSERT, INSERT, INSERT, RESTORE TO VERSION AS OF 1.",
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      "duration_ms": 112,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:19:38.471898+00:00",
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      "skip_feature": null,
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        "type:integer",
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2149_restore_after_delete",
      "num": 2149,
      "name": "restore_after_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2149_restore_after_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2149_restore_after_delete.py",
      "description": "INSERT, DELETE, RESTORE undoes the delete.",
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      "duration_ms": 118,
      "error": null,
      "notes": null,
      "extras": {},
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/214_convert_iceberg.sql",
      "read_script": "generator/spark-reads-iceberg/verify_214_convert_iceberg.py",
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      "error": null,
      "notes": null,
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      "verified_at": "2026-05-20T12:25:03.405747+00:00",
      "read_cold_ms": 34,
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        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/2150_restore_then_dml",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2150_restore_then_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2150_restore_then_dml.py",
      "description": "RESTORE then INSERT new data on top.",
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      "writer": "delta-forge",
      "reader": "spark",
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      "notes": null,
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      "manual_tags": [],
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      "manual_tags": [],
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    {
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      "error": null,
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        "delta:column-mapping",
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      "read_script": "generator/spark-reads-iceberg/verify_2285_tsntz_partition.py",
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
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      "manual_tags": [],
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    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2286_tsntz_with_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2286_tsntz_with_cdc.py",
      "description": null,
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      "duration_ms": 141,
      "error": null,
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      "extras": {},
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      "read_cold_ms": 45,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 77,
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        "type:integer",
        "type:timestamp-ntz",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2287_tsntz_time_travel",
      "num": 2287,
      "name": "tsntz_time_travel",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2287_tsntz_time_travel.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2287_tsntz_time_travel.py",
      "description": "TIMESTAMP_NTZ + version time travel",
      "status": "pass",
      "duration_ms": 134,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:01.068985+00:00",
      "read_cold_ms": 47,
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      "skip_cause": null,
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        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2288_tsntz_update",
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      "name": "tsntz_update",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2288_tsntz_update.py",
      "description": "UPDATE on TIMESTAMP_NTZ column",
      "status": "pass",
      "duration_ms": 219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:01.288484+00:00",
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      "skip_cause": null,
      "write_cold_ms": 76,
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        "type:integer",
        "type:timestamp-ntz",
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        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2289_tsntz_merge",
      "num": 2289,
      "name": "tsntz_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2289_tsntz_merge.py",
      "description": "MERGE NOT MATCHED INSERT into TIMESTAMP_NTZ table",
      "status": "pass",
      "duration_ms": 142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:01.431473+00:00",
      "read_cold_ms": 41,
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      "skip_cause": null,
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        "type:integer",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/228_identity_by_default",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/228_identity_by_default.sql",
      "read_script": "generator/spark-reads-iceberg/verify_228_identity_by_default.py",
      "description": "GENERATED BY DEFAULT AS IDENTITY mode",
      "status": "pass",
      "duration_ms": 110,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:06.194731+00:00",
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      "read_warm_ms": 32,
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      "skip_cause": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2290_tsntz_min_max_stats",
      "num": 2290,
      "name": "tsntz_min_max_stats",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2290_tsntz_min_max_stats.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2290_tsntz_min_max_stats.py",
      "description": "TIMESTAMP_NTZ with min/max statistics in delta log",
      "status": "pass",
      "duration_ms": 121,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:01.698458+00:00",
      "read_cold_ms": 52,
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      "skip_cause": null,
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        "type:integer",
        "type:timestamp-ntz",
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        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2291_vacuum_dry_run",
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      "name": "vacuum_dry_run",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2291_vacuum_dry_run.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2291_vacuum_dry_run.py",
      "description": "VACUUM DRY RUN must NOT delete files or write commit",
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      "duration_ms": 142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:01.841423+00:00",
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      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2292_vacuum_idempotent",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2292_vacuum_idempotent.py",
      "description": "Running VACUUM twice in a row -- second should be no-op (no extra files removed)",
      "status": "pass",
      "duration_ms": 159,
      "error": null,
      "notes": null,
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2293_vacuum_after_optimize_then_delete",
      "num": 2293,
      "name": "vacuum_after_optimize_then_delete",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2293_vacuum_after_optimize_then_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2293_vacuum_after_optimize_then_delete.py",
      "description": "OPTIMIZE then DELETE then VACUUM",
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      "duration_ms": 152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:02.153622+00:00",
      "read_cold_ms": 51,
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      "skip_feature": null,
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      "write_cold_ms": 84,
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        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2294_vacuum_no_files_to_delete",
      "num": 2294,
      "name": "vacuum_no_files_to_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2294_vacuum_no_files_to_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2294_vacuum_no_files_to_delete.py",
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      "duration_ms": 118,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 35,
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      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2295_vacuum_after_multiple_delete_cycles",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2295_vacuum_after_multiple_delete_cycles.sql",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:02.456085+00:00",
      "read_cold_ms": 80,
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        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2296_map_literal_int_values",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2296_map_literal_int_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2296_map_literal_int_values.py",
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      "extras": {},
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2297_map_literal_in_insert_select",
      "num": 2297,
      "name": "map_literal_in_insert_select",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2297_map_literal_in_insert_select.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2297_map_literal_in_insert_select.py",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:02.735865+00:00",
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        "dml:insert",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2298_map_literal_three_keys",
      "num": 2298,
      "name": "map_literal_three_keys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2298_map_literal_three_keys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2298_map_literal_three_keys.py",
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      "status": "pass",
      "duration_ms": 110,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:02.846743+00:00",
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2299_drop_table_with_files_explicit",
      "num": 2299,
      "name": "drop_table_with_files_explicit",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2299_drop_table_with_files_explicit.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2299_drop_table_with_files_explicit.py",
      "description": "DROP TABLE WITH FILES then re-CREATE at same LOCATION",
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      "duration_ms": 108,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:02.955446+00:00",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "id": "df-writes/iceberg/229_identity_hwm",
      "num": 229,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/229_identity_hwm.sql",
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      "duration_ms": 349,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:06.544568+00:00",
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        "type:timestamp",
        "dml:insert",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "id": "df-writes/iceberg/22_action_sidecar_file_reference",
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      "format": "iceberg",
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      "reader": "spark",
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      "duration_ms": 305,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:06.850365+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 1510,
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      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:checkpoint-sidecar",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/2300_drop_table_if_exists_then_create",
      "num": 2300,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2300_drop_table_if_exists_then_create.py",
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      "duration_ms": 98,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:03.651399+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 80,
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        "delta:column-mapping",
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2301_vacuum_retention_tombstone_check",
      "num": 2301,
      "name": "vacuum_retention_tombstone_check",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2301_vacuum_retention_tombstone_check.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2301_vacuum_retention_tombstone_check.py",
      "description": "VACUUM RETAIN 0 HOURS removes tombstoned files; verify VACUUM commit emitted",
      "status": "pass",
      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:03.854446+00:00",
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      "format": "iceberg",
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      "read_script": "generator/spark-reads-iceberg/verify_2335_default_empty_string.py",
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      "duration_ms": 112,
      "error": null,
      "notes": null,
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      "verified_at": "2026-05-18T20:20:09.279509+00:00",
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      "write_cold_ms": 48,
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      ],
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      "concern_note": null
    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2336_gencol_concat_strings.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2336_gencol_concat_strings.py",
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      "status": "pass",
      "duration_ms": 146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:09.426685+00:00",
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      "concern_note": null
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    {
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      "num": 2337,
      "name": "gencol_year_of_date",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2337_gencol_year_of_date.py",
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      "status": "pass",
      "duration_ms": 100,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:09.527002+00:00",
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      "concern_note": null
    },
    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2338_gencol_month_of_ts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2338_gencol_month_of_ts.py",
      "description": "GENERATED column = MONTH(timestamp)",
      "status": "pass",
      "duration_ms": 117,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:09.644451+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 50,
      "write_warm_ms": 60,
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    {
      "id": "df-writes/iceberg/2339_gencol_three_col_arithmetic",
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      "name": "gencol_three_col_arithmetic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2339_gencol_three_col_arithmetic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2339_gencol_three_col_arithmetic.py",
      "description": "GENERATED column = (a + b) * c",
      "status": "pass",
      "duration_ms": 100,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:09.745493+00:00",
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/233_identity_merge",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_233_identity_merge.py",
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      "status": "pass",
      "duration_ms": 87,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:07.742310+00:00",
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
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      "tags": [
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        "delta:column-mapping",
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        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2340_gencol_merge_insert",
      "num": 2340,
      "name": "gencol_merge_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2340_gencol_merge_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2340_gencol_merge_insert.py",
      "description": "GENERATED column populated correctly via MERGE NOT MATCHED INSERT",
      "status": "pass",
      "duration_ms": 153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.028368+00:00",
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      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
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        "dml:merge",
        "delta:column-mapping",
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        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2341_gencol_type_cast",
      "num": 2341,
      "name": "gencol_type_cast",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2341_gencol_type_cast.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2341_gencol_type_cast.py",
      "description": "GENERATED column with explicit CAST (INT -> STRING)",
      "status": "pass",
      "duration_ms": 122,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.151508+00:00",
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      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2342_tsntz_min_year_1970",
      "num": 2342,
      "name": "tsntz_min_year_1970",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2342_tsntz_min_year_1970.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2342_tsntz_min_year_1970.py",
      "description": "TIMESTAMP_NTZ with epoch year 1970",
      "status": "pass",
      "duration_ms": 97,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.249835+00:00",
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
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      "tags": [
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        "type:timestamp-ntz",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2343_tsntz_microsecond_precision",
      "num": 2343,
      "name": "tsntz_microsecond_precision",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2343_tsntz_microsecond_precision.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2343_tsntz_microsecond_precision.py",
      "description": "TIMESTAMP_NTZ values with microsecond precision",
      "status": "pass",
      "duration_ms": 124,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.374487+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 40,
      "tags": [
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        "type:timestamp-ntz",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2344_tsntz_merge_update_clause",
      "num": 2344,
      "name": "tsntz_merge_update_clause",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2344_tsntz_merge_update_clause.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2344_tsntz_merge_update_clause.py",
      "description": "TIMESTAMP_NTZ updated via MERGE WHEN MATCHED THEN UPDATE SET",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.582029+00:00",
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2345_tsntz_zorder",
      "num": 2345,
      "name": "tsntz_zorder",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2345_tsntz_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2345_tsntz_zorder.py",
      "description": "TIMESTAMP_NTZ column used as Z-ORDER key",
      "status": "pass",
      "duration_ms": 121,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.703923+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
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      "write_warm_ms": 68,
      "tags": [
        "type:integer",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2346_default_identity_generated_combo",
      "num": 2346,
      "name": "default_identity_generated_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2346_default_identity_generated_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2346_default_identity_generated_combo.py",
      "description": null,
      "status": "pass",
      "duration_ms": 118,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.822273+00:00",
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      "skip_feature": null,
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      "tags": [
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2347_gencol_coalesce",
      "num": 2347,
      "name": "gencol_coalesce",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2347_gencol_coalesce.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2347_gencol_coalesce.py",
      "description": "GENERATED column using COALESCE on nullable input",
      "status": "pass",
      "duration_ms": 116,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:10.938898+00:00",
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      "skip_feature": null,
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        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2348_identity_update_other_cols",
      "num": 2348,
      "name": "identity_update_other_cols",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2348_identity_update_other_cols.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2348_identity_update_other_cols.py",
      "description": "IDENTITY column remains stable when other columns are UPDATEd",
      "status": "pass",
      "duration_ms": 210,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:11.149840+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 95,
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      "tags": [
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2349_alter_add_column_with_default",
      "num": 2349,
      "name": "alter_add_column_with_default",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2349_alter_add_column_with_default.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2349_alter_add_column_with_default.py",
      "description": "ALTER TABLE ADD COLUMN with DEFAULT applied to subsequent inserts",
      "status": "pass",
      "duration_ms": 135,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:11.285967+00:00",
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/234_identity_resume",
      "num": 234,
      "name": "identity_resume",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/234_identity_resume.sql",
      "read_script": "generator/spark-reads-iceberg/verify_234_identity_resume.py",
      "description": "Identity Resume After DBX Insert",
      "status": "pass",
      "duration_ms": 76,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:07.818472+00:00",
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      "skip_feature": null,
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      "tags": [
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2350_gencol_from_partition_col",
      "num": 2350,
      "name": "gencol_from_partition_col",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2350_gencol_from_partition_col.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2350_gencol_from_partition_col.py",
      "description": "GENERATED column referencing source of a partition column",
      "status": "pass",
      "duration_ms": 118,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:11.535085+00:00",
      "read_cold_ms": 38,
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2351_merge_matched_with_filter",
      "num": 2351,
      "name": "merge_matched_with_filter",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2351_merge_matched_with_filter.sql",
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      "reader": "spark",
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      "description": "Time travel using timestampAsOf. Verify side uses the latest commit's timestamp to resolve the final state.",
      "status": "pass",
      "duration_ms": 123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:19.005531+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 76,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2386_cdc_partition_delete",
      "num": 2386,
      "name": "cdc_partition_delete",
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      "format": "iceberg",
      "writer": "delta-forge",
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      "read_script": "generator/spark-reads-iceberg/verify_2386_cdc_partition_delete.py",
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      "status": "pass",
      "duration_ms": 104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:19.110646+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
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      "tags": [
        "type:integer",
        "type:string",
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        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2387_check_constraint_after_optimize",
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      "status": "pass",
      "duration_ms": 102,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:19.213035+00:00",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2388_multi_check_same_column",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2388_multi_check_same_column.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2388_multi_check_same_column.py",
      "description": "Two CHECK constraints on the same column (lower bound + upper bound).",
      "status": "pass",
      "duration_ms": 115,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:19.328736+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 28,
      "skip_feature": null,
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2389_check_constraint_two_columns",
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      "name": "check_constraint_two_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2389_check_constraint_two_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2389_check_constraint_two_columns.py",
      "description": null,
      "status": "pass",
      "duration_ms": 119,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 30,
      "read_warm_ms": 50,
      "skip_feature": null,
      "skip_cause": null,
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/238_default_evolution",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_238_default_evolution.py",
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      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:08.737270+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
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        "dml:insert",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2390_not_null_via_check",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2390_not_null_via_check.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2390_not_null_via_check.py",
      "description": "Enforce non-null via a CHECK (name IS NOT NULL) constraint added post-hoc.",
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      "duration_ms": 126,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 37,
      "read_warm_ms": 36,
      "skip_feature": null,
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        "dml:insert",
        "delta:column-mapping",
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        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2391_cdc_toggle_cycle",
      "num": 2391,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2391_cdc_toggle_cycle.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2391_cdc_toggle_cycle.py",
      "description": "Final metadata should reflect enableChangeDataFeed = true.",
      "status": "pass",
      "duration_ms": 147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:19.890950+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 47,
      "skip_feature": null,
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      "tags": [
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        "dml:insert",
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        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2392_cdc_merge_update_images",
      "num": 2392,
      "name": "cdc_merge_update_images",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2392_cdc_merge_update_images.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2392_cdc_merge_update_images.py",
      "description": "CDC + MERGE matched UPDATE. Verify update_preimage/update_postimage events in CDF.",
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      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:20.093632+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 62,
      "skip_feature": null,
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        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2393_evolve_then_time_travel",
      "num": 2393,
      "name": "evolve_then_time_travel",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2393_evolve_then_time_travel.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2393_evolve_then_time_travel.py",
      "description": null,
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:20.381442+00:00",
      "read_cold_ms": 62,
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      "skip_feature": null,
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      "write_cold_ms": 98,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
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        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2394_colmap_evolve_add_column",
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      "name": "colmap_evolve_add_column",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2394_colmap_evolve_add_column.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2394_colmap_evolve_add_column.py",
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      "duration_ms": 173,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:20.555651+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 43,
      "skip_feature": null,
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2395_cdf_filter_change_type",
      "num": 2395,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 226,
      "error": null,
      "notes": null,
      "extras": {},
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        "dml:cdc-write",
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        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/2396_check_and_default_same_column",
      "num": 2396,
      "name": "check_and_default_same_column",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2396_check_and_default_same_column.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2396_check_and_default_same_column.py",
      "description": "DEFAULT literal on a column plus a CHECK constraint on the same column.",
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      "duration_ms": 131,
      "error": null,
      "notes": null,
      "extras": {},
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2397_set_then_unset_tblproperty",
      "num": 2397,
      "name": "set_then_unset_tblproperty",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2397_set_then_unset_tblproperty.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2397_set_then_unset_tblproperty.py",
      "description": "SET then UNSET a custom TBLPROPERTY. Latest metadata must not contain the property.",
      "status": "pass",
      "duration_ms": 111,
      "error": null,
      "notes": null,
      "extras": {},
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      "skip_feature": null,
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      "tags": [
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        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2398_time_travel_after_vacuum",
      "num": 2398,
      "name": "time_travel_after_vacuum",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2398_time_travel_after_vacuum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2398_time_travel_after_vacuum.py",
      "description": "VACUUM DRY RUN (non-destructive) then prove recent versions still time-travel readable.",
      "status": "pass",
      "duration_ms": 1972,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:22.998804+00:00",
      "read_cold_ms": 53,
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2399_cdf_batch_multi_versions",
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      "name": "cdf_batch_multi_versions",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2399_cdf_batch_multi_versions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2399_cdf_batch_multi_versions.py",
      "description": "CDF batch read with startingVersion=1, endingVersion=3 spanning 3 commits.",
      "status": "pass",
      "duration_ms": 592,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:23.591660+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 138,
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        "dml:delete",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
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    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_239_default_mixed.py",
      "description": "Table with many different deterministic default value types.",
      "status": "pass",
      "duration_ms": 123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:08.860797+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 20,
      "skip_feature": null,
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      "write_cold_ms": 34,
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      "tags": [
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        "type:decimal",
        "type:floating",
        "type:integer",
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        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/23_action_checkpoint_metadata_v2",
      "num": 23,
      "name": "action_checkpoint_metadata_v2",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/23_action_checkpoint_metadata_v2.sql",
      "read_script": "generator/spark-reads-iceberg/verify_23_action_checkpoint_metadata_v2.py",
      "description": "Demonstrates checkpoint metadata action (V2) which signals readers to use V2 checkpoint parsing logic.",
      "status": "pass",
      "duration_ms": 428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:09.289672+00:00",
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        "type:floating",
        "type:integer",
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        "dml:delete",
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        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2400_colmap_partition_column",
      "num": 2400,
      "name": "colmap_partition_column",
      "type": "df-writes",
      "format": "iceberg",
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      "verified_at": "2026-05-18T20:20:40.195468+00:00",
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      "read_script": "generator/spark-reads-iceberg/verify_243_cdc_enable.py",
      "description": "Enabling Change Data Capture via table property",
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      "verified_at": "2026-05-20T12:25:10.052203+00:00",
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      "error": null,
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      "num": 244,
      "name": "cdc_insert",
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      "description": "- Change Data Feed (CDF) enabled table for insert tracking - Table property: delta.enableChangeDataFeed = true - Simple order tracking schema with timestamps",
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      "verified_at": "2026-05-20T12:25:10.185113+00:00",
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      "id": "df-writes/iceberg/2450_conflict_optimize_between_dml",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2450_conflict_optimize_between_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2450_conflict_optimize_between_dml.py",
      "description": "INSERT 500 + OPTIMIZE + INSERT 500 (ids 501-1000) + DELETE WHERE id<=250.",
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        "delta:deletion-vectors",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2451_conflict_vacuum_between_dml",
      "num": 2451,
      "name": "conflict_vacuum_between_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2451_conflict_vacuum_between_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2451_conflict_vacuum_between_dml.py",
      "description": "INSERT 500 + DELETE WHERE id<=250 + VACUUM RETAIN 0 HOURS + INSERT (ids 501-750).",
      "status": "pass",
      "duration_ms": 216,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:45.398765+00:00",
      "write_cold_ms": 147,
      "write_warm_ms": 113,
      "read_cold_ms": 74,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2452_conflict_restore_then_dml",
      "num": 2452,
      "name": "conflict_restore_then_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2452_conflict_restore_then_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2452_conflict_restore_then_dml.py",
      "description": "INSERT 500 (v1, val=i*10) + UPDATE val=0 (v2) + RESTORE VERSION 1 + INSERT 200 (ids 501-700).",
      "status": "pass",
      "duration_ms": 189,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:45.588797+00:00",
      "write_cold_ms": 174,
      "write_warm_ms": 122,
      "read_cold_ms": 49,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2453_conflict_merge_self_join",
      "num": 2453,
      "name": "conflict_merge_self_join",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2453_conflict_merge_self_join.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2453_conflict_merge_self_join.py",
      "description": "INSERT 500 rows + MERGE self (source = same table's data) ON id=id WHEN MATCHED UPDATE SET val=val*2. Verify all vals doubled.",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:45.830945+00:00",
      "write_cold_ms": 111,
      "write_warm_ms": 94,
      "read_cold_ms": 79,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2454_conflict_overwrite_then_merge",
      "num": 2454,
      "name": "conflict_overwrite_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2454_conflict_overwrite_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2454_conflict_overwrite_then_merge.py",
      "description": "INSERT 500 + INSERT OVERWRITE with 300 (ids 1-300) + MERGE 200 source (ids 301-500) NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 181,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:46.012838+00:00",
      "write_cold_ms": 127,
      "write_warm_ms": 166,
      "read_cold_ms": 43,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2455_conflict_schema_evolve_mid_dml",
      "num": 2455,
      "name": "conflict_schema_evolve_mid_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2455_conflict_schema_evolve_mid_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2455_conflict_schema_evolve_mid_dml.py",
      "description": "INSERT 500 (id, val) + ALTER TABLE ADD COLUMN tag STRING + UPDATE SET tag='updated' WHERE id<=250 + INSERT 200 more with tag.",
      "status": "pass",
      "duration_ms": 219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:46.232226+00:00",
      "write_cold_ms": 129,
      "write_warm_ms": 166,
      "read_cold_ms": 70,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2456_conflict_rename_col_then_update",
      "num": 2456,
      "name": "conflict_rename_col_then_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2456_conflict_rename_col_then_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2456_conflict_rename_col_then_update.py",
      "description": "INSERT 500 (id, old_name) + ALTER RENAME COLUMN old_name TO new_name + UPDATE SET new_name='changed' WHERE id<=100. Verify column named new_name, 100 rows='changed'.",
      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:46.515196+00:00",
      "write_cold_ms": 98,
      "write_warm_ms": 93,
      "read_cold_ms": 75,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2457_conflict_drop_col_then_insert",
      "num": 2457,
      "name": "conflict_drop_col_then_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2457_conflict_drop_col_then_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2457_conflict_drop_col_then_insert.py",
      "description": "INSERT 500 (id, val, extra) + ALTER DROP COLUMN extra + INSERT 200 (id, val).",
      "status": "pass",
      "duration_ms": 149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:46.665200+00:00",
      "write_cold_ms": 112,
      "write_warm_ms": 107,
      "read_cold_ms": 45,
      "read_warm_ms": 48,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2458_conflict_add_constraint_existing_data",
      "num": 2458,
      "name": "conflict_add_constraint_existing_data",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2458_conflict_add_constraint_existing_data.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2458_conflict_add_constraint_existing_data.py",
      "description": "INSERT 500 rows (val = i*10, always > 0) + ALTER ADD CONSTRAINT chk CHECK (val > 0). Verify constraint in delta log and all vals > 0.",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:46.790820+00:00",
      "write_cold_ms": 52,
      "write_warm_ms": 47,
      "read_cold_ms": 39,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2459_conflict_cdc_rapid_dml",
      "num": 2459,
      "name": "conflict_cdc_rapid_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2459_conflict_cdc_rapid_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2459_conflict_cdc_rapid_dml.py",
      "description": "CDC enabled + INSERT 200 + UPDATE val=val+1 WHERE id<=100 + DELETE WHERE id>180.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:47.036519+00:00",
      "write_cold_ms": 135,
      "write_warm_ms": 131,
      "read_cold_ms": 66,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/245_cdc_delete",
      "num": 245,
      "name": "cdc_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/245_cdc_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_245_cdc_delete.py",
      "description": "- Change Data Feed (CDF) enabled table for delete tracking - Table property: delta.enableChangeDataFeed = true - Product catalog schema with boolean is_active flag",
      "status": "pass",
      "duration_ms": 190,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:10.376158+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 22,
      "write_warm_ms": 28,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2460_conflict_partition_overwrite",
      "num": 2460,
      "name": "conflict_partition_overwrite",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2460_conflict_partition_overwrite.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2460_conflict_partition_overwrite.py",
      "description": "PARTITIONED BY(region) + INSERT 500 (4 regions) + INSERT OVERWRITE for region='na' with new data. Verify other regions unchanged.",
      "status": "pass",
      "duration_ms": 113,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:47.268226+00:00",
      "write_cold_ms": 100,
      "write_warm_ms": 126,
      "read_cold_ms": 35,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
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        "type:string",
        "dml:insert",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2461_idempotent_insert_twice",
      "num": 2461,
      "name": "idempotent_insert_twice",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2461_idempotent_insert_twice.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2461_idempotent_insert_twice.py",
      "description": "INSERT 500 (ids 1-500) + INSERT 500 (ids 501-1000). Pure append, no dedup.",
      "status": "pass",
      "duration_ms": 149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:47.417723+00:00",
      "write_cold_ms": 89,
      "write_warm_ms": 75,
      "read_cold_ms": 44,
      "read_warm_ms": 46,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2462_idempotent_merge_twice",
      "num": 2462,
      "name": "idempotent_merge_twice",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2462_idempotent_merge_twice.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2462_idempotent_merge_twice.py",
      "description": "INSERT 500 + MERGE source 300 (ids 1-300 update val=val+100) + MERGE same source again. Second merge should be idempotent since vals already changed.",
      "status": "pass",
      "duration_ms": 258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:47.676502+00:00",
      "write_cold_ms": 159,
      "write_warm_ms": 156,
      "read_cold_ms": 65,
      "read_warm_ms": 73,
      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2463_idempotent_optimize_twice",
      "num": 2463,
      "name": "idempotent_optimize_twice",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2463_idempotent_optimize_twice.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2463_idempotent_optimize_twice.py",
      "description": "INSERT 100 batches of 10 rows each (1000 total) + OPTIMIZE + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:47.822964+00:00",
      "write_cold_ms": 610,
      "write_warm_ms": 685,
      "read_cold_ms": 73,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2464_idempotent_vacuum_twice",
      "num": 2464,
      "name": "idempotent_vacuum_twice",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2464_idempotent_vacuum_twice.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2464_idempotent_vacuum_twice.py",
      "description": "INSERT 500 + DELETE 250 + VACUUM RETAIN 0 HOURS + VACUUM RETAIN 0 HOURS.",
      "status": "pass",
      "duration_ms": 140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:47.963848+00:00",
      "write_cold_ms": 157,
      "write_warm_ms": 147,
      "read_cold_ms": 43,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2465_idempotent_zorder_twice",
      "num": 2465,
      "name": "idempotent_zorder_twice",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2465_idempotent_zorder_twice.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2465_idempotent_zorder_twice.py",
      "description": "INSERT 1k rows + ZORDER BY(k) + ZORDER BY(k). Verify 1000 rows intact.",
      "status": "pass",
      "duration_ms": 113,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:48.077814+00:00",
      "write_cold_ms": 115,
      "write_warm_ms": 105,
      "read_cold_ms": 33,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2466_idempotent_restore_current",
      "num": 2466,
      "name": "idempotent_restore_current",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2466_idempotent_restore_current.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2466_idempotent_restore_current.py",
      "description": "INSERT 500 + UPDATE val=0. Version is 2. RESTORE VERSION 2 (restore to current).",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:48.298167+00:00",
      "write_cold_ms": 105,
      "write_warm_ms": 141,
      "read_cold_ms": 70,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
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        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2467_idempotent_delete_no_match",
      "num": 2467,
      "name": "idempotent_delete_no_match",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2467_idempotent_delete_no_match.py",
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      "notes": null,
      "extras": {},
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      "extras": {},
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      "num": 2483,
      "name": "unicode_accented_european",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2483_unicode_accented_european.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2483_unicode_accented_european.py",
      "description": "500 rows with European accented names cycling through 6 values. Verifies accented character roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 139,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:51.730860+00:00",
      "write_cold_ms": 40,
      "write_warm_ms": 42,
      "read_cold_ms": 40,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2484_unicode_mixed_scripts",
      "num": 2484,
      "name": "unicode_mixed_scripts",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2484_unicode_mixed_scripts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2484_unicode_mixed_scripts.py",
      "description": "500 rows mixing Latin + numeric + punctuation patterns. Verifies mixed-script string assembly roundtrips.",
      "status": "pass",
      "duration_ms": 153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:51.884966+00:00",
      "write_cold_ms": 44,
      "write_warm_ms": 40,
      "read_cold_ms": 31,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2485_unicode_cyrillic_basic",
      "num": 2485,
      "name": "unicode_cyrillic_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2485_unicode_cyrillic_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2485_unicode_cyrillic_basic.py",
      "description": "500 rows with Cyrillic-transliterated city names cycling through 5 values. Verifies string roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 135,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:52.021028+00:00",
      "write_cold_ms": 39,
      "write_warm_ms": 44,
      "read_cold_ms": 32,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2486_unicode_long_multibyte",
      "num": 2486,
      "name": "unicode_long_multibyte",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2486_unicode_long_multibyte.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2486_unicode_long_multibyte.py",
      "description": "500 rows with long strings built from REPEAT of multi-char patterns. Each string is 300 characters long.",
      "status": "pass",
      "duration_ms": 133,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:52.155353+00:00",
      "write_cold_ms": 40,
      "write_warm_ms": 40,
      "read_cold_ms": 31,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2487_unicode_partition_keys",
      "num": 2487,
      "name": "unicode_partition_keys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2487_unicode_partition_keys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2487_unicode_partition_keys.py",
      "description": "500 rows partitioned by region with 5 partition values. Verifies partition directories and data integrity.",
      "status": "pass",
      "duration_ms": 140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:52.296236+00:00",
      "write_cold_ms": 50,
      "write_warm_ms": 55,
      "read_cold_ms": 36,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2488_unicode_in_map_keys",
      "num": 2488,
      "name": "unicode_in_map_keys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2488_unicode_in_map_keys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2488_unicode_in_map_keys.py",
      "description": "500 rows with MAP<STRING, INT> column containing string keys. Verifies map keys and values roundtrip.",
      "status": "pass",
      "duration_ms": 128,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:52.424879+00:00",
      "write_cold_ms": 42,
      "write_warm_ms": 47,
      "read_cold_ms": 32,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:map",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2489_unicode_in_struct_fields",
      "num": 2489,
      "name": "unicode_in_struct_fields",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2489_unicode_in_struct_fields.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2489_unicode_in_struct_fields.py",
      "description": "500 rows with STRUCT<first_name:STRING, last_name:STRING> column. Verifies struct string fields roundtrip.",
      "status": "pass",
      "duration_ms": 165,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:52.590859+00:00",
      "write_cold_ms": 45,
      "write_warm_ms": 42,
      "read_cold_ms": 40,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/248_cdc_timestamp_range",
      "num": 248,
      "name": "cdc_timestamp_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/248_cdc_timestamp_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_248_cdc_timestamp_range.py",
      "description": "CDC queries between timestamps with distinct commit times",
      "status": "pass",
      "duration_ms": 129,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:11.011244+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
      "write_warm_ms": 101,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2490_unicode_after_update",
      "num": 2490,
      "name": "unicode_after_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2490_unicode_after_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2490_unicode_after_update.py",
      "description": "INSERT 500 rows with name='original_N', then UPDATE first 250 to 'updated_N'. Verifies 250 updated + 250 original strings.",
      "status": "pass",
      "duration_ms": 290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:53.073198+00:00",
      "write_cold_ms": 87,
      "write_warm_ms": 96,
      "read_cold_ms": 69,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2491_unicode_after_optimize",
      "num": 2491,
      "name": "unicode_after_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2491_unicode_after_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2491_unicode_after_optimize.py",
      "description": "INSERT 500 rows with varied strings in 5 batches, then OPTIMIZE. Verifies all strings preserved after compaction.",
      "status": "pass",
      "duration_ms": 647,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:53.720993+00:00",
      "write_cold_ms": 236,
      "write_warm_ms": 213,
      "read_cold_ms": 34,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2492_unicode_cdc_roundtrip",
      "num": 2492,
      "name": "unicode_cdc_roundtrip",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2492_unicode_cdc_roundtrip.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2492_unicode_cdc_roundtrip.py",
      "description": "CDC enabled. INSERT 300 rows with strings, then UPDATE 100 strings. Verifies CDF has update images with correct strings.",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:53.953882+00:00",
      "write_cold_ms": 86,
      "write_warm_ms": 80,
      "read_cold_ms": 68,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2493_unicode_merge_source",
      "num": 2493,
      "name": "unicode_merge_source",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2493_unicode_merge_source.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2493_unicode_merge_source.py",
      "description": "INSERT 300 target rows, MERGE 200 source rows (ids 201-400). WHEN MATCHED (ids 201-300): UPDATE name. WHEN NOT MATCHED (ids 301-400): INSERT.",
      "status": "pass",
      "duration_ms": 288,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:54.242925+00:00",
      "write_cold_ms": 85,
      "write_warm_ms": 92,
      "read_cold_ms": 82,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2494_unicode_delete_by_string",
      "num": 2494,
      "name": "unicode_delete_by_string",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2494_unicode_delete_by_string.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2494_unicode_delete_by_string.py",
      "description": "INSERT 500 rows with 5 categories, DELETE WHERE category='Books'. Verifies no 'Books' rows remain (400 rows final).",
      "status": "pass",
      "duration_ms": 159,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:54.403081+00:00",
      "write_cold_ms": 73,
      "write_warm_ms": 68,
      "read_cold_ms": 56,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2495_unicode_colmap_strings",
      "num": 2495,
      "name": "unicode_colmap_strings",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2495_unicode_colmap_strings.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2495_unicode_colmap_strings.py",
      "description": "Column mapping mode=name with string columns. INSERT 500 rows. Verifies logical names readable.",
      "status": "pass",
      "duration_ms": 680,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:55.084166+00:00",
      "write_cold_ms": 40,
      "write_warm_ms": 40,
      "read_cold_ms": 49,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2496_special_char_backslash",
      "num": 2496,
      "name": "special_char_backslash",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2496_special_char_backslash.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2496_special_char_backslash.py",
      "description": "500 rows with backslash patterns in val column. Verifies backslash characters roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 180,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:55.264662+00:00",
      "write_cold_ms": 46,
      "write_warm_ms": 41,
      "read_cold_ms": 46,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2497_special_char_single_quotes",
      "num": 2497,
      "name": "special_char_single_quotes",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2497_special_char_single_quotes.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2497_special_char_single_quotes.py",
      "description": "500 rows with escaped single quotes in strings. Verifies single-quote characters roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 147,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:55.412263+00:00",
      "write_cold_ms": 41,
      "write_warm_ms": 40,
      "read_cold_ms": 36,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2498_special_char_tab_newline",
      "num": 2498,
      "name": "special_char_tab_newline",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2498_special_char_tab_newline.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2498_special_char_tab_newline.py",
      "description": "500 rows with tab/newline marker strings (avoiding CHR()). Uses descriptive text markers instead of actual control chars.",
      "status": "pass",
      "duration_ms": 188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:55.601296+00:00",
      "write_cold_ms": 57,
      "write_warm_ms": 36,
      "read_cold_ms": 49,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2499_special_char_pipe_delimiter",
      "num": 2499,
      "name": "special_char_pipe_delimiter",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2499_special_char_pipe_delimiter.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2499_special_char_pipe_delimiter.py",
      "description": "500 rows with pipe characters in strings. Verifies pipe delimiter chars roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 168,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:55.769829+00:00",
      "write_cold_ms": 40,
      "write_warm_ms": 38,
      "read_cold_ms": 48,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/249_cdc_partition",
      "num": 249,
      "name": "cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/249_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_249_cdc_partition.py",
      "description": "CDC queries with partition filtering and partition pruning",
      "status": "pass",
      "duration_ms": 101,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:11.112370+00:00",
      "read_cold_ms": 36,
      "read_warm_ms": 24,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 70,
      "write_warm_ms": 64,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/24_reconciliation_add_remove_sequence",
      "num": 24,
      "name": "reconciliation_add_remove_sequence",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/24_reconciliation_add_remove_sequence.sql",
      "read_script": "generator/spark-reads-iceberg/verify_24_reconciliation_add_remove_sequence.py",
      "description": "Demonstrates action reconciliation with concurrent updates (add/remove sequences).",
      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:11.382504+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 549,
      "write_warm_ms": 576,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2500_special_char_percent_underscore",
      "num": 2500,
      "name": "special_char_percent_underscore",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2500_special_char_percent_underscore.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2500_special_char_percent_underscore.py",
      "description": "500 rows with SQL wildcard characters (% and _) in strings. Verifies these special chars roundtrip through Delta.",
      "status": "pass",
      "duration_ms": 169,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:56.435407+00:00",
      "write_cold_ms": 40,
      "write_warm_ms": 46,
      "read_cold_ms": 38,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2501_unicode_empty_string_vs_null",
      "num": 2501,
      "name": "unicode_empty_string_vs_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2501_unicode_empty_string_vs_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2501_unicode_empty_string_vs_null.py",
      "description": "500 rows. Half with val='' (empty string), half with val=NULL. Verifies null_count=250, empty string count=250.",
      "status": "pass",
      "duration_ms": 142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:56.578564+00:00",
      "read_cold_ms": 37,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 44,
      "write_warm_ms": 40,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2502_unicode_whitespace_only",
      "num": 2502,
      "name": "unicode_whitespace_only",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2502_unicode_whitespace_only.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2502_unicode_whitespace_only.py",
      "description": "500 rows with whitespace-only strings of varying lengths. Verifies whitespace preserved, distinct_count=4.",
      "status": "pass",
      "duration_ms": 146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:56.725397+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 39,
      "write_warm_ms": 42,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2503_unicode_very_long_string",
      "num": 2503,
      "name": "unicode_very_long_string",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2503_unicode_very_long_string.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2503_unicode_very_long_string.py",
      "description": "500 rows with val = REPEAT('x', 1000). Each string is 1000 chars. Verifies long strings roundtrip.",
      "status": "pass",
      "duration_ms": 122,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:56.847826+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2504_unicode_concat_chain",
      "num": 2504,
      "name": "unicode_concat_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2504_unicode_concat_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2504_unicode_concat_chain.py",
      "description": "500 rows with val built from chained CONCATs. Verifies assembled string correctness.",
      "status": "pass",
      "duration_ms": 131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:56.979968+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 37,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2505_unicode_case_sensitivity",
      "num": 2505,
      "name": "unicode_case_sensitivity",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2505_unicode_case_sensitivity.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2505_unicode_case_sensitivity.py",
      "description": "500 rows with val alternating between 5 case variants. Verifies distinct_count=5 and case preserved.",
      "status": "pass",
      "duration_ms": 145,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:57.125395+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 47,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2506_unicode_numeric_strings",
      "num": 2506,
      "name": "unicode_numeric_strings",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2506_unicode_numeric_strings.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2506_unicode_numeric_strings.py",
      "description": "500 rows with val = CAST(i AS STRING). Pure numeric strings. Verifies they stay as strings, not numbers.",
      "status": "pass",
      "duration_ms": 149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:57.275446+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 43,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2507_unicode_leading_trailing_spaces",
      "num": 2507,
      "name": "unicode_leading_trailing_spaces",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2507_unicode_leading_trailing_spaces.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2507_unicode_leading_trailing_spaces.py",
      "description": "500 rows with val = ' N ' (leading+trailing spaces). Verifies spaces are preserved, not trimmed.",
      "status": "pass",
      "duration_ms": 170,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:57.446054+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 42,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2508_unicode_null_string_operations",
      "num": 2508,
      "name": "unicode_null_string_operations",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2508_unicode_null_string_operations.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2508_unicode_null_string_operations.py",
      "description": "500 rows. Every 5th row has val=NULL, rest have CONCAT result. Verifies null_count=100.",
      "status": "pass",
      "duration_ms": 172,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:57.618815+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
      "write_warm_ms": 52,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2509_unicode_string_in_partition_prune",
      "num": 2509,
      "name": "unicode_string_in_partition_prune",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2509_unicode_string_in_partition_prune.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2509_unicode_string_in_partition_prune.py",
      "description": "PARTITIONED BY(category). INSERT 500 rows with 5 categories. Filter by category='Books' should partition-prune.",
      "status": "pass",
      "duration_ms": 201,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:57.820474+00:00",
      "read_cold_ms": 45,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 62,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/250_cdc_merge",
      "num": 250,
      "name": "cdc_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/250_cdc_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_250_cdc_merge.py",
      "description": "MERGE operations create correct CDC records",
      "status": "pass",
      "duration_ms": 155,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:11.538343+00:00",
      "read_cold_ms": 43,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 29,
      "write_warm_ms": 24,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2510_unicode_string_min_max_stats",
      "num": 2510,
      "name": "unicode_string_min_max_stats",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2510_unicode_string_min_max_stats.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2510_unicode_string_min_max_stats.py",
      "description": "INSERT 500 rows with val from 'aaa_001' to 'aaa_500'. Verifies min/max lexicographic correctness.",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:58.162777+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 51,
      "write_warm_ms": 50,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2511_unicode_string_distinct_count",
      "num": 2511,
      "name": "unicode_string_distinct_count",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2511_unicode_string_distinct_count.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2511_unicode_string_distinct_count.py",
      "description": "INSERT 500 rows with 10 distinct categories (i%10). Verifies distinct_count=10.",
      "status": "pass",
      "duration_ms": 132,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:58.295469+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 56,
      "write_warm_ms": 53,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2512_unicode_string_group_distribution",
      "num": 2512,
      "name": "unicode_string_group_distribution",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2512_unicode_string_group_distribution.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2512_unicode_string_group_distribution.py",
      "description": "INSERT 500 rows with 5 groups of 100 each. Verifies count per group = 100.",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:58.420873+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 53,
      "write_warm_ms": 47,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2513_unicode_update_string_conditional",
      "num": 2513,
      "name": "unicode_update_string_conditional",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2513_unicode_update_string_conditional.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2513_unicode_update_string_conditional.py",
      "description": "INSERT 500 rows, then two UPDATE statements: UPDATE SET status='active' WHERE id > 250 UPDATE SET status='inactive' WHERE id <= 250 Verifies 250 active + 250 inactive.",
      "status": "pass",
      "duration_ms": 314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:58.735751+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2514_unicode_merge_string_key",
      "num": 2514,
      "name": "unicode_merge_string_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2514_unicode_merge_string_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2514_unicode_merge_string_key.py",
      "description": "INSERT 300 rows (name as key). MERGE 200 source matching on name. Matched rows (first 100) get val updated. Unmatched (ids 301-400) inserted.",
      "status": "pass",
      "duration_ms": 267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:20:59.003802+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "type:unicode",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2515_unicode_constraint_on_string",
      "num": 2515,
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      "verified_at": "2026-05-18T20:21:10.175571+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2565_widen_after_delete",
      "num": 2565,
      "name": "widen_after_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2565_widen_after_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2565_widen_after_delete.py",
      "description": "INSERT + DELETE + ALTER widen + INSERT with BIGINT values.",
      "status": "pass",
      "duration_ms": 181,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:10.357007+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 146,
      "write_warm_ms": 158,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2566_truncate_basic_verify_empty",
      "num": 2566,
      "name": "truncate_basic_verify_empty",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2566_truncate_basic_verify_empty.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2566_truncate_basic_verify_empty.py",
      "description": "INSERT + TRUNCATE, verify table is empty.",
      "status": "pass",
      "duration_ms": 134,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:10.491495+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 60,
      "write_warm_ms": 59,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2567_truncate_then_reinsert",
      "num": 2567,
      "name": "truncate_then_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2567_truncate_then_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2567_truncate_then_reinsert.py",
      "description": "INSERT + TRUNCATE + re-INSERT.",
      "status": "pass",
      "duration_ms": 131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:10.623513+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2568_truncate_with_cdc",
      "num": 2568,
      "name": "truncate_with_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2568_truncate_with_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2568_truncate_with_cdc.py",
      "description": "TRUNCATE on CDC-enabled table. CDF should have delete records.",
      "status": "pass",
      "duration_ms": 95,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:10.719698+00:00",
      "read_cold_ms": 29,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 69,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2569_truncate_with_identity",
      "num": 2569,
      "name": "truncate_with_identity",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2569_truncate_with_identity.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2569_truncate_with_identity.py",
      "description": "IDENTITY + INSERT + TRUNCATE + INSERT, IDs continue from 501+.",
      "status": "pass",
      "duration_ms": 128,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:10.848484+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/256_ict_time_travel",
      "num": 256,
      "name": "ict_time_travel",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/256_ict_time_travel.sql",
      "read_script": "generator/spark-reads-iceberg/verify_256_ict_time_travel.py",
      "description": "## Table Details Tests that TIMESTAMP AS OF queries use in-commit timestamps. Creates commits at known times for time travel verification.",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:12.610866+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 72,
      "skip_feature": null,
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      "write_cold_ms": 79,
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      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2570_truncate_with_constraints",
      "num": 2570,
      "name": "truncate_with_constraints",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2570_truncate_with_constraints.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2570_truncate_with_constraints.py",
      "description": "CHECK constraint + INSERT + TRUNCATE + INSERT (constraint still holds).",
      "status": "pass",
      "duration_ms": 116,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.189973+00:00",
      "read_cold_ms": 30,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 126,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2571_truncate_with_colmap",
      "num": 2571,
      "name": "truncate_with_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2571_truncate_with_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2571_truncate_with_colmap.py",
      "description": "column mapping mode=name + INSERT + TRUNCATE + INSERT.",
      "status": "pass",
      "duration_ms": 126,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.316349+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2572_truncate_partitioned",
      "num": 2572,
      "name": "truncate_partitioned",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2572_truncate_partitioned.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2572_truncate_partitioned.py",
      "description": "TRUNCATE on partitioned table.",
      "status": "pass",
      "duration_ms": 104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.420796+00:00",
      "read_cold_ms": 31,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2573_truncate_then_optimize",
      "num": 2573,
      "name": "truncate_then_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2573_truncate_then_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2573_truncate_then_optimize.py",
      "description": "TRUNCATE + INSERT in 10 batches + OPTIMIZE.",
      "status": "pass",
      "duration_ms": 133,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.554062+00:00",
      "read_cold_ms": 36,
      "read_warm_ms": 48,
      "skip_feature": null,
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      "write_cold_ms": 795,
      "write_warm_ms": 679,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2574_truncate_then_schema_evolve",
      "num": 2574,
      "name": "truncate_then_schema_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2574_truncate_then_schema_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2574_truncate_then_schema_evolve.py",
      "description": "TRUNCATE + ALTER ADD COLUMN + INSERT with new column.",
      "status": "pass",
      "duration_ms": 163,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.718224+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 136,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2575_truncate_time_travel",
      "num": 2575,
      "name": "truncate_time_travel",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2575_truncate_time_travel.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2575_truncate_time_travel.py",
      "description": "INSERT (v1) + TRUNCATE (v2). Read version 1 = 500 rows.",
      "status": "pass",
      "duration_ms": 83,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.801688+00:00",
      "read_cold_ms": 28,
      "read_warm_ms": 23,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 79,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2576_truncate_with_generated",
      "num": 2576,
      "name": "truncate_with_generated",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2576_truncate_with_generated.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2576_truncate_with_generated.py",
      "description": "Generated column (computed = base*2) + INSERT + TRUNCATE + INSERT.",
      "status": "pass",
      "duration_ms": 135,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:11.937655+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 109,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2577_truncate_multiple_cycles",
      "num": 2577,
      "name": "truncate_multiple_cycles",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2577_truncate_multiple_cycles.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2577_truncate_multiple_cycles.py",
      "description": "Multiple TRUNCATE cycles.",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:12.063800+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 26,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2578_cor_basic",
      "num": 2578,
      "name": "cor_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2578_cor_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2578_cor_basic.py",
      "description": "CREATE + INSERT + CREATE OR REPLACE same schema + INSERT.",
      "status": "pass",
      "duration_ms": 152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:12.216875+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2579_cor_different_schema",
      "num": 2579,
      "name": "cor_different_schema",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2579_cor_different_schema.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2579_cor_different_schema.py",
      "description": "CREATE (id, val) + INSERT + COR (id, name, score) + INSERT.",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:12.342709+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/257_ict_multi_writer",
      "num": 257,
      "name": "ict_multi_writer",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/257_ict_multi_writer.sql",
      "read_script": "generator/spark-reads-iceberg/verify_257_ict_multi_writer.py",
      "description": "## Table Details Tests ICT ordering across concurrent writers (DBX and DeltaForge). DBX creates initial commit, DeltaForge will add next, then DBX again.",
      "status": "pass",
      "duration_ms": 97,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:12.708127+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 26,
      "skip_feature": null,
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      "write_cold_ms": 24,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2580_cor_with_cdc",
      "num": 2580,
      "name": "cor_with_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2580_cor_with_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2580_cor_with_cdc.py",
      "description": "CDC + CREATE + INSERT + COR with CDC + INSERT.",
      "status": "pass",
      "duration_ms": 148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:12.649497+00:00",
      "read_cold_ms": 30,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
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      "verified_at": "2026-05-18T20:21:31.460239+00:00",
      "read_cold_ms": 37,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 93,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2661_etl_backfill_historical",
      "num": 2661,
      "name": "etl_backfill_historical",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2661_etl_backfill_historical.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2661_etl_backfill_historical.py",
      "description": "INSERT 500 (ids 501-1000) + INSERT 500 (ids 1-500, \"older\" backfill data). Verify 1000 total, all ids 1-1000 present.",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:31.673090+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2662_etl_dedup_merge",
      "num": 2662,
      "name": "etl_dedup_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2662_etl_dedup_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2662_etl_dedup_merge.py",
      "description": "INSERT 500 unique rows + MERGE 500 source (all match, ids 1-500). WHEN MATCHED AND src.val > t.val THEN UPDATE SET val=src.val. Keeps max val per id.",
      "status": "pass",
      "duration_ms": 266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:31.940213+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 91,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2663_etl_late_arriving",
      "num": 2663,
      "name": "etl_late_arriving",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2663_etl_late_arriving.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2663_etl_late_arriving.py",
      "description": "INSERT 500 (ids 1-500 with ts based on i) + INSERT 200 (ids 501-700 with earlier timestamps). Verify 700 total rows.",
      "status": "pass",
      "duration_ms": 187,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:32.128416+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2664_etl_gdpr_delete",
      "num": 2664,
      "name": "etl_gdpr_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2664_etl_gdpr_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2664_etl_gdpr_delete.py",
      "description": "CDC + INSERT 500 customers + DELETE WHERE id <= 50. Verify 450 rows + CDF has 50 delete records.",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:32.359773+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 70,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2665_etl_schema_migration",
      "num": 2665,
      "name": "etl_schema_migration",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2665_etl_schema_migration.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2665_etl_schema_migration.py",
      "description": "INSERT 500 (id, val) + ALTER ADD COLUMN tag STRING + UPDATE SET tag for ids 1-250. Verify 500 rows, 250 non-null tags, 250 null tags.",
      "status": "pass",
      "duration_ms": 296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:32.656306+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
      "write_warm_ms": 93,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2666_etl_compact_micro_batch",
      "num": 2666,
      "name": "etl_compact_micro_batch",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2666_etl_compact_micro_batch.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2666_etl_compact_micro_batch.py",
      "description": "50 x INSERT 20 rows + OPTIMIZE. Verify 1000 rows, fewer files after optimize.",
      "status": "pass",
      "duration_ms": 140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:32.797352+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 4552,
      "write_warm_ms": 4931,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2667_etl_cdc_audit_trail",
      "num": 2667,
      "name": "etl_cdc_audit_trail",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2667_etl_cdc_audit_trail.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2667_etl_cdc_audit_trail.py",
      "description": "CDC + INSERT 300 + UPDATE 100 (ids 1-100) + DELETE 50 (ids 201-250). Verify 250 rows + CDF has insert(300) + update images + delete(50).",
      "status": "pass",
      "duration_ms": 246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:33.044033+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 131,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2668_etl_merge_schema_evolve",
      "num": 2668,
      "name": "etl_merge_schema_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2668_etl_merge_schema_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2668_etl_merge_schema_evolve.py",
      "description": "INSERT 300 (id, val) + ALTER ADD tag STRING + MERGE 200 source (with tag). WHEN MATCHED UPDATE SET tag=src.tag, WHEN NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:33.331767+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2669_etl_daily_partition_append",
      "num": 2669,
      "name": "etl_daily_partition_append",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2669_etl_daily_partition_append.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2669_etl_daily_partition_append.py",
      "description": "PARTITIONED BY(day_key STRING) + 10 inserts of 100 rows each with day_key='day_01' through 'day_10'. Verify 1000 rows, 10 partitions.",
      "status": "pass",
      "duration_ms": 223,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:33.555444+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 448,
      "write_warm_ms": 560,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/266_invariant_violation",
      "num": 266,
      "name": "invariant_violation",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/266_invariant_violation.sql",
      "read_script": "generator/spark-reads-iceberg/verify_266_invariant_violation.py",
      "description": "Proper error handling when constraints are violated.",
      "status": "pass",
      "duration_ms": 104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:14.249964+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2670_etl_upsert_soft_delete",
      "num": 2670,
      "name": "etl_upsert_soft_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2670_etl_upsert_soft_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2670_etl_upsert_soft_delete.py",
      "description": "INSERT 500 (deleted=false) + MERGE 200 source: WHEN MATCHED AND src.action='delete' THEN UPDATE SET deleted=true WHEN MATCHED THEN UPDATE SET val=src.val",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:33.982514+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 102,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2671_etl_cdc_replay_chain",
      "num": 2671,
      "name": "etl_cdc_replay_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2671_etl_cdc_replay_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2671_etl_cdc_replay_chain.py",
      "description": "CDC + INSERT 200 + UPDATE 100 (ids 1-100) + DELETE 50 (ids 151-200) + INSERT 100 (ids 201-300). Full DML chain. Verify 250 rows + CDF covers all operations.",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:34.223961+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 298,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2672_etl_merge_insert_only",
      "num": 2672,
      "name": "etl_merge_insert_only",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2672_etl_merge_insert_only.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2672_etl_merge_insert_only.py",
      "description": "INSERT 500 + MERGE 500 source (ids 501-1000, no overlap) WHEN NOT MATCHED INSERT. Verify 1000 rows.",
      "status": "pass",
      "duration_ms": 272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:34.496452+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2673_etl_merge_update_only",
      "num": 2673,
      "name": "etl_merge_update_only",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2673_etl_merge_update_only.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2673_etl_merge_update_only.py",
      "description": "INSERT 500 + MERGE 500 source (ids 1-500, all overlap) WHEN MATCHED UPDATE SET val=src.val. Verify 500 rows, all updated.",
      "status": "pass",
      "duration_ms": 307,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:34.804262+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2674_etl_merge_conditional_update",
      "num": 2674,
      "name": "etl_merge_conditional_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2674_etl_merge_conditional_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2674_etl_merge_conditional_update.py",
      "description": "INSERT 500 (val=i*10) + MERGE 500 source (val=i*7 for all). WHEN MATCHED AND src.val > t.val THEN UPDATE SET val=src.val. i*7 > i*10 is never true, so NO rows get updated. Wait, let's make it interesting: source val = (501-i)*10 so some are bigger.",
      "status": "pass",
      "duration_ms": 310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:35.114695+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2675_etl_multi_stage_merge",
      "num": 2675,
      "name": "etl_multi_stage_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2675_etl_multi_stage_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2675_etl_multi_stage_merge.py",
      "description": "INSERT 300 (stage 1) + MERGE 200 (stage 2: 100 update ids 201-300 + 100 insert ids 301-400) + MERGE 200 (stage 3: 100 update ids 301-400 + 100 insert ids 401-500). Verify 500 rows after all stages.",
      "status": "pass",
      "duration_ms": 304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:35.419852+00:00",
      "read_cold_ms": 104,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 221,
      "write_warm_ms": 216,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2676_merge_all_match_update",
      "num": 2676,
      "name": "merge_all_match_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2676_merge_all_match_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2676_merge_all_match_update.py",
      "description": "INSERT 500 + MERGE 500 (all match) WHEN MATCHED UPDATE. 100% match rate.",
      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:35.702758+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 150,
      "write_warm_ms": 152,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2677_merge_no_match_insert",
      "num": 2677,
      "name": "merge_no_match_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2677_merge_no_match_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2677_merge_no_match_insert.py",
      "description": "INSERT 500 + MERGE 500 (ids 501-1000, none match) WHEN NOT MATCHED INSERT. Verify 1000.",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:35.917657+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2678_merge_delete_all",
      "num": 2678,
      "name": "merge_delete_all",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2678_merge_delete_all.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2678_merge_delete_all.py",
      "description": "INSERT 500 + MERGE 500 (all match) WHEN MATCHED THEN DELETE. Verify 0 rows.",
      "status": "pass",
      "duration_ms": 131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:36.049473+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2679_merge_compound_key",
      "num": 2679,
      "name": "merge_compound_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2679_merge_compound_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2679_merge_compound_key.py",
      "description": "INSERT 500 with (a, b) composite key + MERGE ON t.a=s.a AND t.b=s.b. a = i / 10, b = i % 10 (deterministic). Source overlaps first 250.",
      "status": "pass",
      "duration_ms": 303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:36.353535+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 125,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/267_invariant_preserve",
      "num": 267,
      "name": "invariant_preserve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/267_invariant_preserve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_267_invariant_preserve.py",
      "description": "Constraints survive DeltaForge roundtrip.",
      "status": "pass",
      "duration_ms": 59,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:14.309183+00:00",
      "read_cold_ms": 19,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 61,
      "write_warm_ms": 49,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2680_merge_three_clauses",
      "num": 2680,
      "name": "merge_three_clauses",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2680_merge_three_clauses.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2680_merge_three_clauses.py",
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      "status": "pass",
      "duration_ms": 264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:36.763574+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2681_merge_with_subquery_source",
      "num": 2681,
      "name": "merge_with_subquery_source",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2681_merge_with_subquery_source.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2681_merge_with_subquery_source.py",
      "description": "INSERT 500 + MERGE USING (SELECT from generate_series) as source ON t.id=src.id WHEN MATCHED UPDATE. Source is ids 1-300.",
      "status": "pass",
      "duration_ms": 286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:37.050109+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2682_merge_nmbys_update",
      "num": 2682,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2682_merge_nmbys_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2682_merge_nmbys_update.py",
      "description": "INSERT 500 + MERGE 300 source (ids 1-300): WHEN MATCHED THEN UPDATE SET val=src.val WHEN NOT MATCHED BY SOURCE THEN UPDATE SET val=-1 Verify ids 301-500 have val=-1.",
      "status": "pass",
      "duration_ms": 271,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:37.321982+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2683_merge_nmbys_delete",
      "num": 2683,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2683_merge_nmbys_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2683_merge_nmbys_delete.py",
      "description": "INSERT 500 + MERGE 300 source (ids 1-300): WHEN MATCHED THEN UPDATE SET val=src.val WHEN NOT MATCHED BY SOURCE THEN DELETE Verify 300 rows (ids 301-500 deleted).",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:37.562488+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 101,
      "tags": [
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        "dml:insert",
        "dml:merge",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2684_merge_large_source_small_target",
      "num": 2684,
      "name": "merge_large_source_small_target",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2684_merge_large_source_small_target.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2684_merge_large_source_small_target.py",
      "description": "INSERT 100 + MERGE 1000 source. 100 match (update), 900 not matched (insert). Verify 1000 rows.",
      "status": "pass",
      "duration_ms": 278,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:37.841686+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 130,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2685_merge_small_source_large_target",
      "num": 2685,
      "name": "merge_small_source_large_target",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2685_merge_small_source_large_target.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2685_merge_small_source_large_target.py",
      "description": "INSERT 1000 + MERGE 100 source (ids 1-100, all match). Verify 1000 rows, 100 updated.",
      "status": "pass",
      "duration_ms": 319,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:38.161156+00:00",
      "read_cold_ms": 95,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 135,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2686_merge_self_join_update",
      "num": 2686,
      "name": "merge_self_join_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2686_merge_self_join_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2686_merge_self_join_update.py",
      "description": "INSERT 500 + MERGE table with itself ON t.id=s.id WHEN MATCHED UPDATE SET val=val*2. Verify all val doubled.",
      "status": "pass",
      "duration_ms": 279,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:38.440616+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 104,
      "tags": [
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        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2687_merge_with_cdc_all_clauses",
      "num": 2687,
      "name": "merge_with_cdc_all_clauses",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2687_merge_with_cdc_all_clauses.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2687_merge_with_cdc_all_clauses.py",
      "description": "CDC + INSERT 300 + MERGE 300 source with all clause types: WHEN MATCHED AND target.id <= 150 THEN DELETE (50 rows) WHEN MATCHED THEN UPDATE SET val=src.val (150 rows: ids 151-300) WHEN NOT MATCHED THEN INSERT (100 rows: ids 301-400)",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:38.700702+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 322,
      "write_warm_ms": 386,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2688_merge_partitioned_cross",
      "num": 2688,
      "name": "merge_partitioned_cross",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2688_merge_partitioned_cross.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2688_merge_partitioned_cross.py",
      "description": "PARTITIONED BY(region) + INSERT 500 + MERGE 300 source across different partitions. WHEN MATCHED THEN UPDATE SET val=src.val, region=src.region WHEN NOT MATCHED INSERT (none expected -- all 1-300 exist)",
      "status": "pass",
      "duration_ms": 291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:38.992576+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 121,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2689_merge_with_identity",
      "num": 2689,
      "name": "merge_with_identity",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2689_merge_with_identity.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2689_merge_with_identity.py",
      "description": "IDENTITY column + INSERT 300 (omit id) + MERGE 200 WHEN NOT MATCHED INSERT (omit id). Verify all rows have unique auto IDs.",
      "status": "pass",
      "duration_ms": 234,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:39.227271+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 93,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:default-values",
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        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/268_invariant_nested",
      "num": 268,
      "name": "invariant_nested",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/268_invariant_nested.sql",
      "read_script": "generator/spark-reads-iceberg/verify_268_invariant_nested.py",
      "description": "Constraints on struct field values.",
      "status": "pass",
      "duration_ms": 105,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:14.414343+00:00",
      "read_cold_ms": 24,
      "read_warm_ms": 17,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 53,
      "tags": [
        "type:integer",
        "type:struct",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2690_merge_update_computed_expr",
      "num": 2690,
      "name": "merge_update_computed_expr",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2690_merge_update_computed_expr.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2690_merge_update_computed_expr.py",
      "description": "INSERT 500 + MERGE 500 WHEN MATCHED UPDATE SET val = t.val + src.val, name = CONCAT(t.name, '_merged'). Verify computed results.",
      "status": "pass",
      "duration_ms": 316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:39.744930+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 136,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2691_restore_basic_to_v1",
      "num": 2691,
      "name": "restore_basic_to_v1",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2691_restore_basic_to_v1.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2691_restore_basic_to_v1.py",
      "description": "Basic RESTORE TO VERSION AS OF 1.",
      "status": "pass",
      "duration_ms": 436,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:21:40.181252+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 175,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2692_restore_after_delete",
      "num": 2692,
      "name": "restore_after_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2692_restore_after_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2692_restore_after_delete.py",
      "description": "RESTORE after DELETE restores deleted rows.",
      "status": "pass",
      "duration_ms": 348,
      "error": null,
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        "type:integer",
        "type:string",
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        "delta:partitioning",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2776_edge_hundred_partitions",
      "num": 2776,
      "name": "edge_hundred_partitions",
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      "reader": "spark",
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      "description": "Partitioned table with 100 distinct partition values (one row per partition)",
      "status": "pass",
      "duration_ms": 293,
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      "notes": null,
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      "skip_feature": null,
      "skip_cause": null,
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        "type:string",
        "dml:insert",
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        "iceberg:uniform",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2777_edge_wide_table_50_cols",
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      "name": "edge_wide_table_50_cols",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2777_edge_wide_table_50_cols.py",
      "description": "Wide table with 50 columns (id + 49 integer columns)",
      "status": "pass",
      "duration_ms": 139,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:04.439044+00:00",
      "read_cold_ms": 51,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 62,
      "write_warm_ms": 57,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:wide-schema",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2778_edge_wide_table_100_cols",
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      "name": "edge_wide_table_100_cols",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2778_edge_wide_table_100_cols.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2778_edge_wide_table_100_cols.py",
      "description": "Wide table with 100 columns (id + 99 integer columns)",
      "status": "pass",
      "duration_ms": 262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:04.701409+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 40,
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      "skip_cause": null,
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      "write_warm_ms": 83,
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        "dml:insert",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2779_edge_single_column_table",
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      "name": "edge_single_column_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2779_edge_single_column_table.sql",
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      "status": "pass",
      "duration_ms": 148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:04.849678+00:00",
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      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/277_conflict_retry",
      "num": 277,
      "name": "conflict_retry",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/277_conflict_retry.sql",
      "read_script": "generator/spark-reads-iceberg/verify_277_conflict_retry.py",
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      "status": "pass",
      "duration_ms": 78,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:15.995762+00:00",
      "read_cold_ms": 22,
      "read_warm_ms": 20,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
      "write_warm_ms": 20,
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        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2780_edge_delete_all_rows",
      "num": 2780,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2780_edge_delete_all_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2780_edge_delete_all_rows.py",
      "description": "DELETE WHERE removes every row; deletion vectors mark all 50 rows deleted",
      "status": "pass",
      "duration_ms": 271,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:05.289856+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 64,
      "write_warm_ms": 64,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2781_edge_delete_all_reinsert",
      "num": 2781,
      "name": "edge_delete_all_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2781_edge_delete_all_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2781_edge_delete_all_reinsert.py",
      "description": "Delete all rows via Deletion Vectors then reinsert new rows; old data must be fully absent",
      "status": "pass",
      "duration_ms": 264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:05.554742+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 120,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2782_edge_update_no_matching_rows",
      "num": 2782,
      "name": "edge_update_no_matching_rows",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2782_edge_update_no_matching_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2782_edge_update_no_matching_rows.py",
      "description": "UPDATE with a predicate that matches zero rows leaves data unchanged",
      "status": "pass",
      "duration_ms": 129,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:05.684842+00:00",
      "read_cold_ms": 39,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 67,
      "write_warm_ms": 60,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2783_edge_insert_duplicate_ids",
      "num": 2783,
      "name": "edge_insert_duplicate_ids",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2783_edge_insert_duplicate_ids.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2783_edge_insert_duplicate_ids.py",
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      "status": "pass",
      "duration_ms": 193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:05.878527+00:00",
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      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 88,
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/2784_edge_many_small_versions",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2784_edge_many_small_versions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2784_edge_many_small_versions.py",
      "description": "20 single-row INSERT statements produce 20 Delta log versions; table reads correctly across all",
      "status": "pass",
      "duration_ms": 216,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:06.094746+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1300,
      "write_warm_ms": 1418,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2785_edge_partition_null_key",
      "num": 2785,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2785_edge_partition_null_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2785_edge_partition_null_key.py",
      "description": "Partitioned table where some partition key values are NULL; Spark must read the null partition correctly",
      "status": "pass",
      "duration_ms": 194,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:06.289388+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 31,
      "skip_feature": null,
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      "write_cold_ms": 72,
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        "type:integer",
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        "dml:insert",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2786_edge_string_with_delimiters",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2786_edge_string_with_delimiters.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2786_edge_string_with_delimiters.py",
      "description": "String values containing delimiter characters (comma, quote, semicolon, pipe, backslash, equals)",
      "status": "pass",
      "duration_ms": 143,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-18T20:22:06.433100+00:00",
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      "skip_feature": null,
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
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      "id": "df-writes/iceberg/2787_edge_decimal_zero_scale",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2787_edge_decimal_zero_scale.py",
      "description": "DECIMAL(10,0) columns store whole numbers without fractional part; min/max/sum read correctly",
      "status": "pass",
      "duration_ms": 120,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:06.553912+00:00",
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      "skip_feature": null,
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2788_edge_insert_empty_batch",
      "num": 2788,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2788_edge_insert_empty_batch.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2788_edge_insert_empty_batch.py",
      "description": "An INSERT that selects zero rows via a false WHERE clause creates a new version but adds no data",
      "status": "pass",
      "duration_ms": 113,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:06.668001+00:00",
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      "manual_tags": [],
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2789_edge_tinyint_overflow_boundary.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2789_edge_tinyint_overflow_boundary.py",
      "description": "TINYINT boundary values -128 and 127 round-trip correctly through Delta and Parquet",
      "status": "pass",
      "duration_ms": 120,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:06.788512+00:00",
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      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/278_decimal_basic",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/278_decimal_basic.sql",
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      "status": "pass",
      "duration_ms": 101,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:16.097546+00:00",
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      "skip_feature": null,
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        "format:iceberg"
      ],
      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/2790_edge_smallint_boundaries",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2790_edge_smallint_boundaries.py",
      "description": "SMALLINT boundary values -32768 and 32767 round-trip correctly through Delta and Parquet",
      "status": "pass",
      "duration_ms": 123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:07.033678+00:00",
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      "skip_feature": null,
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      "write_cold_ms": 65,
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/2791_map_string_string_basic",
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      "name": "map_string_string_basic",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2791_map_string_string_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2791_map_string_string_basic.py",
      "description": "Basic MAP<STRING, STRING> column with 2 keys per row",
      "status": "pass",
      "duration_ms": 122,
      "error": null,
      "notes": null,
      "extras": {},
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      "write_cold_ms": 52,
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2792_map_string_int_with_nulls",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2792_map_string_int_with_nulls.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2792_map_string_int_with_nulls.py",
      "description": "MAP<STRING, INT> column with NULL maps every 3rd row",
      "status": "pass",
      "duration_ms": 123,
      "error": null,
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      "read_cold_ms": 46,
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      "skip_feature": null,
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      "write_cold_ms": 52,
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        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "num": 2793,
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2793_map_after_update.py",
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      "duration_ms": 238,
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      "notes": null,
      "extras": {},
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      "read_cold_ms": 75,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 92,
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        "type:integer",
        "type:map",
        "type:string",
        "dml:insert",
        "dml:update",
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        "delta:deletion-vectors",
        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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        "type:integer",
        "type:map",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2795_map_with_cdc.sql",
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        "type:map",
        "type:string",
        "dml:cdc-write",
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        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2797_map_int_double",
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        "type:integer",
        "type:map",
        "dml:insert",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
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      "write_cold_ms": 50,
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        "type:map",
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
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      "duration_ms": 126,
      "error": null,
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      "extras": {},
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      "read_warm_ms": 48,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/279_zorder_comprehensive",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/279_zorder_comprehensive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_279_zorder_comprehensive.py",
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      "duration_ms": 499,
      "error": null,
      "notes": null,
      "extras": {},
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        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
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      ],
      "manual_tags": [],
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    },
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      "duration_ms": 308,
      "error": null,
      "notes": null,
      "extras": {},
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        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
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        "format:iceberg"
      ],
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    },
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      "reader": "spark",
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      "duration_ms": 135,
      "error": null,
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      "extras": {},
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        "format:iceberg"
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      "manual_tags": [],
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    },
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2801_array_after_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2801_array_after_update.py",
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      "status": "pass",
      "duration_ms": 273,
      "error": null,
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      "extras": {},
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      "skip_feature": null,
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        "dml:insert",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2802_array_after_delete.sql",
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      "status": "pass",
      "duration_ms": 181,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:09.921088+00:00",
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2803_array_with_nulls.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2803_array_with_nulls.py",
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      "status": "pass",
      "duration_ms": 141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:10.063131+00:00",
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2804_array_empty_vs_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2804_array_empty_vs_null.py",
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      "duration_ms": 150,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:10.214136+00:00",
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        "dml:insert",
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2805_array_with_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2805_array_with_cdc.py",
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      "status": "pass",
      "duration_ms": 581,
      "error": null,
      "notes": null,
      "extras": {},
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        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2806_struct_three_level_nested.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2806_struct_three_level_nested.py",
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      "status": "pass",
      "duration_ms": 165,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:10.961341+00:00",
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2807_struct_with_array_field.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2807_struct_with_array_field.py",
      "description": "STRUCT that contains an ARRAY<INT> field alongside a STRING",
      "status": "pass",
      "duration_ms": 158,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:22:11.120020+00:00",
      "read_cold_ms": 60,
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        "type:string",
        "type:struct",
        "dml:insert",
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        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2808_struct_with_map_field.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2808_struct_with_map_field.py",
      "description": "STRUCT containing a MAP<STRING, STRING> field. The map value",
      "status": "pass",
      "duration_ms": 154,
      "error": null,
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      "duration_ms": 645,
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      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
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        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/2987_identity_vacuum_hwm_preserved",
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      "description": "IDENTITY column high-water-mark (HWM) is preserved after",
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      "duration_ms": 723,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:09.712099+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 134,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2988_identity_constraint_range_check",
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      "read_script": "generator/spark-reads-iceberg/verify_2988_identity_constraint_range_check.py",
      "description": "IDENTITY column combined with CHECK constraint on a range.",
      "status": "pass",
      "duration_ms": 643,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:10.355873+00:00",
      "read_cold_ms": 48,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2989_identity_constraint_positive",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_2989_identity_constraint_positive.py",
      "description": "IDENTITY column with explicit START WITH 1 INCREMENT BY 1,",
      "status": "pass",
      "duration_ms": 174,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:10.530567+00:00",
      "read_cold_ms": 81,
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      "skip_feature": null,
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      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/298_update_multi_col",
      "num": 298,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_298_update_multi_col.py",
      "description": "UPDATE setting multiple columns at once",
      "status": "pass",
      "duration_ms": 106,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:21.081250+00:00",
      "read_cold_ms": 23,
      "read_warm_ms": 24,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 45,
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        "type:integer",
        "type:string",
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        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2990_constraint_vacuum_check_preserved",
      "num": 2990,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2990_constraint_vacuum_check_preserved.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2990_constraint_vacuum_check_preserved.py",
      "description": "CHECK constraint metadata survives VACUUM. After DELETE and",
      "status": "pass",
      "duration_ms": 750,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:11.404175+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 93,
      "write_warm_ms": 111,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2991_constraint_vacuum_multi_check",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2991_constraint_vacuum_multi_check.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2991_constraint_vacuum_multi_check.py",
      "description": "Multiple CHECK constraints both survive OPTIMIZE + VACUUM.",
      "status": "pass",
      "duration_ms": 655,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:12.060085+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2992_gencol_merge_upsert",
      "num": 2992,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2992_gencol_merge_upsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2992_gencol_merge_upsert.py",
      "description": "Generated column total = price * qty is recalculated correctly",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:12.320291+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
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      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2993_gencol_merge_delete_reinsert",
      "num": 2993,
      "name": "gencol_merge_delete_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2993_gencol_merge_delete_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2993_gencol_merge_delete_reinsert.py",
      "description": "Generated column doubled = base * 2 is correctly computed",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:12.589309+00:00",
      "read_cold_ms": 85,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 125,
      "write_warm_ms": 113,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2994_gencol_colmap_computed",
      "num": 2994,
      "name": "gencol_colmap_computed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2994_gencol_colmap_computed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2994_gencol_colmap_computed.py",
      "description": "Generated column sum_ab = a + b combined with column mapping",
      "status": "pass",
      "duration_ms": 167,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:12.756750+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 48,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2995_uniform_merge_upsert",
      "num": 2995,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2995_uniform_merge_upsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2995_uniform_merge_upsert.py",
      "description": "UniForm Iceberg format combined with MERGE upsert.",
      "status": "pass",
      "duration_ms": 236,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:12.993423+00:00",
      "read_cold_ms": 71,
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "dml:merge",
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        "delta:deletion-vectors",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2996_uniform_cdc_tracking",
      "num": 2996,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2996_uniform_cdc_tracking.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2996_uniform_cdc_tracking.py",
      "description": "UniForm Iceberg combined with CDC (Change Data Feed).",
      "status": "pass",
      "duration_ms": 288,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:13.282464+00:00",
      "read_cold_ms": 85,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
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      "tags": [
        "type:integer",
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        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2997_uniform_optimize_compaction",
      "num": 2997,
      "name": "uniform_optimize_compaction",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2997_uniform_optimize_compaction.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2997_uniform_optimize_compaction.py",
      "description": "UniForm Iceberg combined with OPTIMIZE compaction. Five",
      "status": "pass",
      "duration_ms": 729,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:14.012763+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 47,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 240,
      "write_warm_ms": 239,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2998_ict_vacuum_timestamp_survive",
      "num": 2998,
      "name": "ict_vacuum_timestamp_survive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2998_ict_vacuum_timestamp_survive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2998_ict_vacuum_timestamp_survive.py",
      "description": "In-Commit Timestamp (ICT) metadata survives VACUUM.",
      "status": "pass",
      "duration_ms": 659,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:14.672196+00:00",
      "read_cold_ms": 55,
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      "skip_feature": null,
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      "tags": [
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        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/2999_ict_vacuum_multi_version",
      "num": 2999,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/2999_ict_vacuum_multi_version.sql",
      "read_script": "generator/spark-reads-iceberg/verify_2999_ict_vacuum_multi_version.py",
      "description": "ICT across many versions survives VACUUM. Ten separate INSERT",
      "status": "pass",
      "duration_ms": 759,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:15.432221+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1516,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/299_delete_cdc",
      "num": 299,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/299_delete_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_299_delete_cdc.py",
      "description": "DELETE with CDC (Change Data Capture) enabled - captures pre-images",
      "status": "pass",
      "duration_ms": 116,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:21.198289+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 31,
      "skip_feature": null,
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      "tags": [
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        "type:string",
        "dml:delete",
        "dml:insert",
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        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/29_column_mapping_id_mode_physical",
      "num": 29,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/29_column_mapping_id_mode_physical.sql",
      "read_script": "generator/spark-reads-iceberg/verify_29_column_mapping_id_mode_physical.py",
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      "status": "pass",
      "duration_ms": 389,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:21.587441+00:00",
      "read_cold_ms": 122,
      "read_warm_ms": 136,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 492,
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      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/3000_rowtrack_vacuum_metadata",
      "num": 3000,
      "name": "rowtrack_vacuum_metadata",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3000_rowtrack_vacuum_metadata.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3000_rowtrack_vacuum_metadata.py",
      "description": "Row tracking metadata survives DELETE + VACUUM. After INSERT 80",
      "status": "pass",
      "duration_ms": 667,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:16.551854+00:00",
      "read_cold_ms": 47,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 121,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3001_rowtrack_vacuum_optimize",
      "num": 3001,
      "name": "rowtrack_vacuum_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3001_rowtrack_vacuum_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3001_rowtrack_vacuum_optimize.py",
      "description": "Row tracking metadata survives OPTIMIZE + VACUUM. Five",
      "status": "pass",
      "duration_ms": 648,
      "error": null,
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        "format:iceberg"
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3018_merge_partition_cross_partition",
      "num": 3018,
      "name": "merge_partition_cross_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3018_merge_partition_cross_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3018_merge_partition_cross_partition.py",
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      "status": "pass",
      "duration_ms": 244,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:22.904504+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 100,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3019_merge_flat_struct_update",
      "num": 3019,
      "name": "merge_flat_struct_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3019_merge_flat_struct_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3019_merge_flat_struct_update.py",
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      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:23.135403+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 114,
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        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 301,
      "name": "delete_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/301_delete_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_301_delete_partition.py",
      "description": "DELETE with partition pruning",
      "status": "pass",
      "duration_ms": 71,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:21.755650+00:00",
      "read_cold_ms": 21,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 41,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "num": 3020,
      "name": "merge_ten_versions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3020_merge_ten_versions.py",
      "description": "Ten successive MERGE operations each updating all 50 rows. After 10 rounds, val = sum(1..10) = 55 and round = 10. Verifies correctness across many Delta versions.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:23.522954+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 778,
      "write_warm_ms": 647,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3021_merge_large_1000_rows",
      "num": 3021,
      "name": "merge_large_1000_rows",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3021_merge_large_1000_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3021_merge_large_1000_rows.py",
      "description": "MERGE on a 1000-row table. Source updates 500 existing rows, inserts 500 new rows.",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:23.743883+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 117,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3022_merge_delete_all_reinsert",
      "num": 3022,
      "name": "merge_delete_all_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3022_merge_delete_all_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3022_merge_delete_all_reinsert.py",
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      "status": "pass",
      "duration_ms": 217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:23.961393+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 86,
      "write_warm_ms": 97,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3023_merge_gencol_auto_compute",
      "num": 3023,
      "name": "merge_gencol_auto_compute",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3023_merge_gencol_auto_compute.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3023_merge_gencol_auto_compute.py",
      "description": "MERGE on a table with a GENERATED ALWAYS AS column (total = price * qty). MERGE updates price for ids 1-15 and inserts ids 31-40. The generated column must auto-recompute for all affected rows.",
      "status": "pass",
      "duration_ms": 226,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:24.188449+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3024_merge_evolve_add_col_then_merge",
      "num": 3024,
      "name": "merge_evolve_add_col_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3024_merge_evolve_add_col_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3024_merge_evolve_add_col_then_merge.py",
      "description": "MERGE updates ids 1-20 with a score value, inserts ids 41-55 with score. Ids 21-40 survive with score=NULL (pre-evolution rows).",
      "status": "pass",
      "duration_ms": 218,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:24.407257+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3025_merge_widen_then_merge_bigint",
      "num": 3025,
      "name": "merge_widen_then_merge_bigint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3025_merge_widen_then_merge_bigint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3025_merge_widen_then_merge_bigint.py",
      "description": "then MERGE with values exceeding INT range (3000000000).",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:24.628735+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 123,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3026_merge_cdc_dv_partition_quad",
      "num": 3026,
      "name": "merge_cdc_dv_partition_quad",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3026_merge_cdc_dv_partition_quad.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3026_merge_cdc_dv_partition_quad.py",
      "description": "MERGE on a partitioned table (4 regions) with CDC and Deletion Vectors enabled. Verifies CDF contains all three change types across partitions.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:24.882308+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 169,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3027_merge_optimize_then_merge",
      "num": 3027,
      "name": "merge_optimize_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3027_merge_optimize_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3027_merge_optimize_then_merge.py",
      "description": "Two MERGE operations with an OPTIMIZE compaction between them. Verifies that compacted files are transparently read/written by the second MERGE.",
      "status": "pass",
      "duration_ms": 246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:25.129483+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 166,
      "write_warm_ms": 182,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3028_merge_vacuum_then_merge",
      "num": 3028,
      "name": "merge_vacuum_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3028_merge_vacuum_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3028_merge_vacuum_then_merge.py",
      "description": "DELETE rows, VACUUM to remove old files, then MERGE to verify the table is still fully writable after vacuum. Final state has 50 rows (30 updated + 20 new).",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:25.344237+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 132,
      "write_warm_ms": 129,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3029_merge_colmap_cdc_evolve_quad",
      "num": 3029,
      "name": "merge_colmap_cdc_evolve_quad",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3029_merge_colmap_cdc_evolve_quad.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3029_merge_colmap_cdc_evolve_quad.py",
      "description": "INSERT 40 rows, ADD COLUMN score INT, then MERGE updating ids 1-20 and inserting ids 41-55. Verifies all four features interact correctly.",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:25.605610+00:00",
      "read_cold_ms": 85,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 146,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/302_delete_large",
      "num": 302,
      "name": "delete_large",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/302_delete_large.sql",
      "read_script": "generator/spark-reads-iceberg/verify_302_delete_large.py",
      "description": "DELETE removing majority of data (90%)",
      "status": "pass",
      "duration_ms": 152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:21.908270+00:00",
      "read_cold_ms": 26,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 76,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3030_merge_ict_rowtrack",
      "num": 3030,
      "name": "merge_ict_rowtrack",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3030_merge_ict_rowtrack.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3030_merge_ict_rowtrack.py",
      "description": "MERGE on a table with In-Commit Timestamps and Row Tracking enabled. Verifies both protocol features are recorded in the Delta log and that the MERGE result is correct.",
      "status": "pass",
      "duration_ms": 215,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:25.982115+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 88,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3031_merge_string_partition_key",
      "num": 3031,
      "name": "merge_string_partition_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3031_merge_string_partition_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3031_merge_string_partition_key.py",
      "description": "MERGE on a table partitioned by a STRING date column. Rows span 28 distinct partition values (2024-01-01 through 2024-01-28). MERGE updates val for ids 1-30; partition dirs are verified post-merge.",
      "status": "pass",
      "duration_ms": 283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:26.266140+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 348,
      "write_warm_ms": 372,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3032_merge_decimal_38_18_precision",
      "num": 3032,
      "name": "merge_decimal_38_18_precision",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3032_merge_decimal_38_18_precision.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3032_merge_decimal_38_18_precision.py",
      "description": "MERGE on a table with DECIMAL(38,18) amounts. Update doubles the amount for ids 1-15; insert ids 31-40 with new decimals. Verifies full decimal precision is preserved through MERGE.",
      "status": "pass",
      "duration_ms": 243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:26.509932+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 91,
      "tags": [
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        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3033_merge_boolean_predicate",
      "num": 3033,
      "name": "merge_boolean_predicate",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
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        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/304_merge_full",
      "num": 304,
      "name": "merge_full",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/304_merge_full.sql",
      "read_script": "generator/spark-reads-iceberg/verify_304_merge_full.py",
      "description": "MERGE with all clauses (INSERT, UPDATE, DELETE)",
      "status": "pass",
      "duration_ms": 62,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:22.035016+00:00",
      "read_cold_ms": 22,
      "read_warm_ms": 14,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 37,
      "write_warm_ms": 32,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3050_maint_vacuum_widen_table",
      "num": 3050,
      "name": "maint_vacuum_widen_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3050_maint_vacuum_widen_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3050_maint_vacuum_widen_table.py",
      "description": "VACUUM after INT->BIGINT type widening.",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:30.186795+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 50,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3051_maint_restore_after_vacuum",
      "num": 3051,
      "name": "maint_restore_after_vacuum",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3051_maint_restore_after_vacuum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3051_maint_restore_after_vacuum.py",
      "description": "RESTORE behavior after VACUUM -- documents whether restore succeeds",
      "status": "pass",
      "duration_ms": 129,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:30.316957+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 105,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3052_maint_optimize_struct_table",
      "num": 3052,
      "name": "maint_optimize_struct_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3052_maint_optimize_struct_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3052_maint_optimize_struct_table.py",
      "description": "OPTIMIZE on a table with multiple flat columns (simulating struct-like",
      "status": "pass",
      "duration_ms": 146,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:30.463942+00:00",
      "read_cold_ms": 39,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 256,
      "write_warm_ms": 262,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3053_maint_vacuum_partition_prune",
      "num": 3053,
      "name": "maint_vacuum_partition_prune",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3053_maint_vacuum_partition_prune.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3053_maint_vacuum_partition_prune.py",
      "description": "VACUUM after deleting entire partitions (LATAM and MEA) removes",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:30.625121+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 110,
      "write_warm_ms": 129,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3054_maint_optimize_map_columns",
      "num": 3054,
      "name": "maint_optimize_map_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3054_maint_optimize_map_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3054_maint_optimize_map_columns.py",
      "description": "OPTIMIZE on a table with key-value style columns (label + score patterns",
      "status": "pass",
      "duration_ms": 153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:30.778615+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 174,
      "write_warm_ms": 194,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3055_maint_vacuum_dv_cleanup",
      "num": 3055,
      "name": "maint_vacuum_dv_cleanup",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3055_maint_vacuum_dv_cleanup.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3055_maint_vacuum_dv_cleanup.py",
      "description": "Full DV lifecycle -- DELETE via DVs, OPTIMIZE applies DVs (rewrites without",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:30.979013+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 100,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3056_cdc_merge_three_clause_cdf",
      "num": 3056,
      "name": "cdc_merge_three_clause_cdf",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3056_cdc_merge_three_clause_cdf.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3056_cdc_merge_three_clause_cdf.py",
      "description": "MERGE with three WHEN clauses on a CDC+DV table. ids 1-20 matched and UPDATED, ids 21-30 matched and DELETED, ids 61-70 not matched and INSERTED.",
      "status": "pass",
      "duration_ms": 238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:31.217988+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 119,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3057_cdc_truncate_cdf_record",
      "num": 3057,
      "name": "cdc_truncate_cdf_record",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3057_cdc_truncate_cdf_record.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3057_cdc_truncate_cdf_record.py",
      "description": "INSERT 50 rows then TRUNCATE TABLE. Verifies CDF records the truncate/delete event. After truncate, no rows remain. CDF should have 50 inserts plus truncate records.",
      "status": "pass",
      "duration_ms": 652,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:31.870406+00:00",
      "read_cold_ms": 26,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 58,
      "write_warm_ms": 63,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:truncate",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3058_cdc_schema_evolve_cdf_columns",
      "num": 3058,
      "name": "cdc_schema_evolve_cdf_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3058_cdc_schema_evolve_cdf_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3058_cdc_schema_evolve_cdf_columns.py",
      "description": "CDC table with schema evolution. INSERT 30 rows, ADD COLUMN score INT, INSERT 20 more rows with score, UPDATE first 10 rows to set score=999. CDF must contain all change types including records with the new score column.",
      "status": "pass",
      "duration_ms": 275,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:32.146686+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 81,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
      "write_warm_ms": 147,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3059_cdc_delete_all_cdf_complete",
      "num": 3059,
      "name": "cdc_delete_all_cdf_complete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3059_cdc_delete_all_cdf_complete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3059_cdc_delete_all_cdf_complete.py",
      "description": "INSERT 100 rows, then DELETE all rows. CDF must capture all 100 inserts and all 100 deletes. Final table has 0 rows.",
      "status": "pass",
      "duration_ms": 173,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:32.320452+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 66,
      "write_warm_ms": 67,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/305_zorder_comprehensive_basic",
      "num": 305,
      "name": "zorder_comprehensive_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/305_zorder_comprehensive_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_305_zorder_comprehensive_basic.py",
      "description": "Basic Z-ORDER coexistence test - DBX creates -> DeltaForge Z-ORDER -> DBX verifies",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:22.265640+00:00",
      "read_cold_ms": 45,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 521,
      "write_warm_ms": 536,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:z-order",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3060_cdc_update_same_row_10x",
      "num": 3060,
      "name": "cdc_update_same_row_10x",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3060_cdc_update_same_row_10x.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3060_cdc_update_same_row_10x.py",
      "description": "INSERT 1 row, then UPDATE the same row 10 times sequentially. Each update increments counter by 1 and updates the label. CDF must record 1 insert + 10 update_preimage + 10 update_postimage = 21 records.",
      "status": "pass",
      "duration_ms": 823,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:33.332606+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 528,
      "write_warm_ms": 573,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3061_cdc_partition_cdf_per_partition",
      "num": 3061,
      "name": "cdc_partition_cdf_per_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3061_cdc_partition_cdf_per_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3061_cdc_partition_cdf_per_partition.py",
      "description": "Partitioned CDC table. INSERT 60 rows across 3 regions, DELETE all 'US' rows. CDF delete records must all have region='US'. Final: 40 rows (EU + APAC).",
      "status": "pass",
      "duration_ms": 158,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:33.491204+00:00",
      "read_cold_ms": 48,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 81,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3062_cdc_identity_cdf_id_tracking",
      "num": 3062,
      "name": "cdc_identity_cdf_id_tracking",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3062_cdc_identity_cdf_id_tracking.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3062_cdc_identity_cdf_id_tracking.py",
      "description": "Identity column with CDC. INSERT 30 rows, DELETE ids 1-10, INSERT 10 more. CDF must track auto-assigned identity IDs in insert/delete records.",
      "status": "pass",
      "duration_ms": 242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:33.734267+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 99,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3063_cdc_optimize_cdf_survives",
      "num": 3063,
      "name": "cdc_optimize_cdf_survives",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3063_cdc_optimize_cdf_survives.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3063_cdc_optimize_cdf_survives.py",
      "description": "CDC table with UPDATE, DELETE, then OPTIMIZE. Verifies CDF is still readable after OPTIMIZE compacts files. OPTIMIZE must not corrupt or clear CDF records.",
      "status": "pass",
      "duration_ms": 657,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:34.392503+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 139,
      "write_warm_ms": 145,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3064_cdc_default_col_cdf",
      "num": 3064,
      "name": "cdc_default_col_cdf",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3064_cdc_default_col_cdf.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3064_cdc_default_col_cdf.py",
      "description": "CDC table with a DEFAULT column value. INSERT 30 rows omitting 'status' (defaults to 'new'). UPDATE first 10 rows to set status='processed'. CDF pre-images have status='new', post-images have status='processed'.",
      "status": "pass",
      "duration_ms": 254,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:34.647660+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3065_cdc_struct_column_changes",
      "num": 3065,
      "name": "cdc_struct_column_changes",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3065_cdc_struct_column_changes.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3065_cdc_struct_column_changes.py",
      "description": "CDC table with flat columns simulating struct-like before/after change capture. UPDATE first 15 rows to modify detail_a and detail_b. CDF must capture full before/after for both detail columns.",
      "status": "pass",
      "duration_ms": 237,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:34.885896+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 86,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3066_cdc_colmap_cdf_logical_names",
      "num": 3066,
      "name": "cdc_colmap_cdf_logical_names",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3066_cdc_colmap_cdf_logical_names.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3066_cdc_colmap_cdf_logical_names.py",
      "description": "CDC table with column mapping mode=name. CDF records must expose logical column names (user_name, score) not physical UUIDs. UPDATE first 20 rows score += 500.",
      "status": "pass",
      "duration_ms": 224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:35.110498+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3067_cdc_constraint_valid_merge",
      "num": 3067,
      "name": "cdc_constraint_valid_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3067_cdc_constraint_valid_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3067_cdc_constraint_valid_merge.py",
      "description": "CDC table with a CHECK constraint. All MERGE operations produce valid scores (>= 0). MERGE updates ids 1-15 (score += 100) and inserts ids 31-40 (score = (id-30)*10). Constraint must be respected throughout. CDF tracks all changes.",
      "status": "pass",
      "duration_ms": 267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:35.378436+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 124,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3068_cdc_restore_cdf_history",
      "num": 3068,
      "name": "cdc_restore_cdf_history",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3068_cdc_restore_cdf_history.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3068_cdc_restore_cdf_history.py",
      "description": "CDC table with RESTORE. INSERT 30 rows (tag='v1'), UPDATE 10 rows (tag='v2'), then RESTORE to version 1. CDF history includes all operations including restore.",
      "status": "pass",
      "duration_ms": 686,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:36.065496+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 94,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3069_cdc_widen_cdf_type_change",
      "num": 3069,
      "name": "cdc_widen_cdf_type_change",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3069_cdc_widen_cdf_type_change.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3069_cdc_widen_cdf_type_change.py",
      "description": "CDC table with type widening. INSERT 30 rows (val INT), ALTER val to BIGINT, UPDATE ids 1-10 setting val to large BIGINT values. CDF records post-widen updates.",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:36.297732+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/306_zorder_numeric_columns",
      "num": 306,
      "name": "zorder_numeric_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/306_zorder_numeric_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_306_zorder_numeric_columns.py",
      "description": "Z-ORDER on numeric columns (INT, BIGINT, DOUBLE, DECIMAL)",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:22.491438+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 213,
      "write_warm_ms": 308,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:z-order",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3070_cdc_many_versions_50",
      "num": 3070,
      "name": "cdc_many_versions_50",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3070_cdc_many_versions_50.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3070_cdc_many_versions_50.py",
      "description": "CDC table with many versions. 10 separate INSERT batches of 10 rows each, batch column tracks which version each row came from. CDF has 100 insert records across 10 version batches.",
      "status": "pass",
      "duration_ms": 680,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:37.212625+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 488,
      "write_warm_ms": 625,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3071_cdc_dv_delete_cdf_correct",
      "num": 3071,
      "name": "cdc_dv_delete_cdf_correct",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3071_cdc_dv_delete_cdf_correct.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3071_cdc_dv_delete_cdf_correct.py",
      "description": "Deletion Vectors + CDC. INSERT 60 rows, DELETE ids 1-20 (via DV). CDF must correctly record the 20 deletions. Deletion vectors mark rows as deleted without rewriting data files. Final: 40 rows.",
      "status": "pass",
      "duration_ms": 159,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:37.372108+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3072_cdc_map_column_update",
      "num": 3072,
      "name": "cdc_map_column_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3072_cdc_map_column_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3072_cdc_map_column_update.py",
      "description": "CDC table simulating key-value update patterns with label+score+extra columns. UPDATE first 10 rows changing score and extra. CDF captures pre/post images with old and new values for all modified columns.",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:37.598247+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 133,
      "write_warm_ms": 103,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3073_cdc_gencol_cdf_computed",
      "num": 3073,
      "name": "cdc_gencol_cdf_computed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3073_cdc_gencol_cdf_computed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3073_cdc_gencol_cdf_computed.py",
      "description": "CDC table with a GENERATED ALWAYS AS computed column (total = price * qty). UPDATE first 15 rows to increment price by 100. CDF post-images must have recomputed total = (price+100)*qty. Verifies generated columns in CDF records.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:37.844064+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3074_evolve_drop_col_then_merge",
      "num": 3074,
      "name": "evolve_drop_col_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3074_evolve_drop_col_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3074_evolve_drop_col_then_merge.py",
      "description": "DROP a column then run MERGE; verify absent column and merge correctness",
      "status": "pass",
      "duration_ms": 239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:38.084011+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 163,
      "write_warm_ms": 170,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3075_evolve_rename_col_then_merge",
      "num": 3075,
      "name": "evolve_rename_col_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3075_evolve_rename_col_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3075_evolve_rename_col_then_merge.py",
      "description": "RENAME a column then run MERGE; verify renamed column and merge correctness",
      "status": "pass",
      "duration_ms": 252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:38.337055+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 156,
      "write_warm_ms": 161,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3076_evolve_add_three_cols_sequential",
      "num": 3076,
      "name": "evolve_add_three_cols_sequential",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3076_evolve_add_three_cols_sequential.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3076_evolve_add_three_cols_sequential.py",
      "description": "ADD three columns one at a time; verify NULL backfill patterns per batch",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:38.577676+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 315,
      "write_warm_ms": 319,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3077_evolve_drop_add_same_name",
      "num": 3077,
      "name": "evolve_drop_add_same_name",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3077_evolve_drop_add_same_name.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3077_evolve_drop_add_same_name.py",
      "description": "DROP column x then ADD COLUMN x with different type (INT -> STRING)",
      "status": "pass",
      "duration_ms": 188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:38.766476+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 51,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 121,
      "write_warm_ms": 135,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3078_evolve_add_col_with_cdc_colmap",
      "num": 3078,
      "name": "evolve_add_col_with_cdc_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3078_evolve_add_col_with_cdc_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3078_evolve_add_col_with_cdc_colmap.py",
      "description": "ADD COLUMN with CDC + colmap enabled; verify CDF sees new column",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:39.035333+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 164,
      "write_warm_ms": 191,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3079_evolve_add_not_null_col_default",
      "num": 3079,
      "name": "evolve_add_not_null_col_default",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3079_evolve_add_not_null_col_default.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3079_evolve_add_not_null_col_default.py",
      "description": "ADD COLUMN with DEFAULT value after existing rows; verify NULL vs default behaviour",
      "status": "pass",
      "duration_ms": 171,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:39.207462+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 143,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3096_partition_vacuum_selective",
      "num": 3096,
      "name": "partition_vacuum_selective",
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      "writer": "delta-forge",
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      "status": "pass",
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      "notes": null,
      "extras": {},
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      "read_cold_ms": 46,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
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        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/3097_partition_cdc_per_partition_cdf",
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      "reader": "spark",
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      "duration_ms": 265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:43.121886+00:00",
      "read_cold_ms": 85,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/3098_partition_identity_per_partition",
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      "notes": null,
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      "read_cold_ms": 59,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/3099_partition_colmap_directory_names",
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      "duration_ms": 155,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:43.459382+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 43,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/309_zorder_partitioned_table",
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      "reader": "spark",
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      "verified_at": "2026-05-20T12:25:23.258091+00:00",
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        "type:integer",
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        "scale:large-dataset",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/30_column_mapping_name_mode_logical",
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      "writer": "delta-forge",
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      "duration_ms": 315,
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        "type:integer",
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        "dml:delete",
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        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/3100_partition_constraint_per_partition",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3100_partition_constraint_per_partition.sql",
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      "status": "pass",
      "duration_ms": 182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:44.222665+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 51,
      "skip_feature": null,
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      "write_cold_ms": 70,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3101_partition_evolve_add_col",
      "num": 3101,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3101_partition_evolve_add_col.sql",
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      "description": "Schema evolution (ADD COLUMN) on a partitioned table.",
      "status": "pass",
      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:44.424969+00:00",
      "read_cold_ms": 49,
      "read_warm_ms": 51,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/3102_partition_zorder_within_partition",
      "num": 3102,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3102_partition_zorder_within_partition.sql",
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      "status": "pass",
      "duration_ms": 186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:44.611832+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 45,
      "skip_feature": null,
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      "write_cold_ms": 100,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3103_partition_restore_version_boundary",
      "num": 3103,
      "name": "partition_restore_version_boundary",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3103_partition_restore_version_boundary.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3103_partition_restore_version_boundary.py",
      "description": "RESTORE TO VERSION on a partitioned table.",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:44.737335+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 120,
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        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3104_stats_after_merge_three_clause",
      "num": 3104,
      "name": "stats_after_merge_three_clause",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3104_stats_after_merge_three_clause.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3104_stats_after_merge_three_clause.py",
      "description": "Delta file statistics are correct after a three-clause",
      "status": "pass",
      "duration_ms": 256,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:44.993694+00:00",
      "read_cold_ms": 82,
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      "skip_feature": null,
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      "tags": [
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        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3105_stats_after_schema_evolve_add",
      "num": 3105,
      "name": "stats_after_schema_evolve_add",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3105_stats_after_schema_evolve_add.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3105_stats_after_schema_evolve_add.py",
      "description": "Delta file statistics are correct after schema evolution",
      "status": "pass",
      "duration_ms": 177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:45.171692+00:00",
      "read_cold_ms": 56,
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      "skip_feature": null,
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      "write_cold_ms": 114,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3106_stats_string_truncation_long",
      "num": 3106,
      "name": "stats_string_truncation_long",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3106_stats_string_truncation_long.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3106_stats_string_truncation_long.py",
      "description": "Delta stats truncation for long strings. Delta truncates",
      "status": "pass",
      "duration_ms": 135,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:45.306922+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 26,
      "skip_feature": null,
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      "write_cold_ms": 63,
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      "tags": [
        "type:integer",
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        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "storage:statistics",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3107_stats_decimal_precision_38",
      "num": 3107,
      "name": "stats_decimal_precision_38",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3107_stats_decimal_precision_38.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3107_stats_decimal_precision_38.py",
      "description": "Delta statistics for DECIMAL(38,18) -- maximum precision.",
      "status": "pass",
      "duration_ms": 141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:45.448864+00:00",
      "read_cold_ms": 37,
      "read_warm_ms": 26,
      "skip_feature": null,
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      "write_cold_ms": 46,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3108_stats_null_heavy_column",
      "num": 3108,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3108_stats_null_heavy_column.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3108_stats_null_heavy_column.py",
      "description": "Delta statistics for a column with many NULLs.",
      "status": "pass",
      "duration_ms": 164,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:45.613947+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 49,
      "skip_feature": null,
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      "write_cold_ms": 43,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3109_stats_after_optimize",
      "num": 3109,
      "name": "stats_after_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3109_stats_after_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3109_stats_after_optimize.py",
      "description": "Delta file statistics are correct after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 139,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:45.754034+00:00",
      "read_cold_ms": 35,
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      "skip_feature": null,
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      "write_cold_ms": 549,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/310_zorder_after_spark_optimize",
      "num": 310,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/310_zorder_after_spark_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_310_zorder_after_spark_optimize.py",
      "description": "DeltaForge Z-ORDER on already Spark-optimized table",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:23.824801+00:00",
      "read_cold_ms": 34,
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      "skip_feature": null,
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      "write_cold_ms": 43,
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      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
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    },
    {
      "id": "df-writes/iceberg/3110_pushdown_int_range_with_dv",
      "num": 3110,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3110_pushdown_int_range_with_dv.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3110_pushdown_int_range_with_dv.py",
      "description": "Integer range pushdown with Deletion Vectors.",
      "status": "pass",
      "duration_ms": 451,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-18T20:23:46.455341+00:00",
      "read_cold_ms": 56,
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      "skip_feature": null,
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      "write_cold_ms": 71,
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      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3111_pushdown_string_prefix",
      "num": 3111,
      "name": "pushdown_string_prefix",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3111_pushdown_string_prefix.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3111_pushdown_string_prefix.py",
      "description": "String prefix distribution in Delta table.",
      "status": "pass",
      "duration_ms": 193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:46.649131+00:00",
      "read_cold_ms": 39,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 47,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3112_pushdown_timestamp_range",
      "num": 3112,
      "name": "pushdown_timestamp_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3112_pushdown_timestamp_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3112_pushdown_timestamp_range.py",
      "description": "Timestamp-like string range pushdown.",
      "status": "pass",
      "duration_ms": 183,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:46.833201+00:00",
      "read_cold_ms": 30,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 48,
      "write_warm_ms": 41,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3113_pushdown_decimal_range",
      "num": 3113,
      "name": "pushdown_decimal_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3113_pushdown_decimal_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3113_pushdown_decimal_range.py",
      "description": "DECIMAL range pushdown. Delta statistics track",
      "status": "pass",
      "duration_ms": 204,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:47.037827+00:00",
      "read_cold_ms": 36,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 48,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3114_pushdown_null_is_null",
      "num": 3114,
      "name": "pushdown_null_is_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3114_pushdown_null_is_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3114_pushdown_null_is_null.py",
      "description": "IS NULL / IS NOT NULL pushdown with Delta statistics.",
      "status": "pass",
      "duration_ms": 252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:47.290767+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
      "write_warm_ms": 44,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3115_pushdown_partition_plus_stats_combo",
      "num": 3115,
      "name": "pushdown_partition_plus_stats_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3115_pushdown_partition_plus_stats_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3115_pushdown_partition_plus_stats_combo.py",
      "description": "Combined partition pruning + file-level stats pushdown.",
      "status": "pass",
      "duration_ms": 263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:47.554472+00:00",
      "read_cold_ms": 47,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 46,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3116_identity_merge_hwm_correctness",
      "num": 3116,
      "name": "identity_merge_hwm_correctness",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3116_identity_merge_hwm_correctness.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3116_identity_merge_hwm_correctness.py",
      "description": "MERGE inserts rows into a table with GENERATED BY DEFAULT AS IDENTITY. Source names do not match target ('merge_j' vs 'row_i'), so all 30 source rows are inserted via NOT MATCHED. Identity HWM must continue from 50 to reach 80.",
      "status": "pass",
      "duration_ms": 182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:47.737019+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 48,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 128,
      "write_warm_ms": 203,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3117_identity_delete_all_reinsert_hwm",
      "num": 3117,
      "name": "identity_delete_all_reinsert_hwm",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3117_identity_delete_all_reinsert_hwm.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3117_identity_delete_all_reinsert_hwm.py",
      "description": "DELETE all rows then re-INSERT to verify the identity HWM is preserved across a full delete. New inserts must get ids 51-100, not restart from 1.",
      "status": "pass",
      "duration_ms": 249,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:47.986759+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 114,
      "write_warm_ms": 108,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3118_identity_by_default_explicit_values",
      "num": 3118,
      "name": "identity_by_default_explicit_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3118_identity_by_default_explicit_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3118_identity_by_default_explicit_values.py",
      "description": "GENERATED BY DEFAULT AS IDENTITY allows explicit id values. This test inserts auto-generated ids (1-20), then 10 explicit large ids (100,200,...,1000), then more auto-generated ids that continue past the HWM.",
      "status": "pass",
      "duration_ms": 215,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:48.202973+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 118,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3119_identity_cdc_hwm_tracking",
      "num": 3119,
      "name": "identity_cdc_hwm_tracking",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3119_identity_cdc_hwm_tracking.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3119_identity_cdc_hwm_tracking.py",
      "description": "Identity column with CDC enabled. INSERT 30, DELETE 10, INSERT 20 more. Verifies that CDF captures all operations and new inserts continue from HWM.",
      "status": "pass",
      "duration_ms": 223,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:48.426356+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 104,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/311_zorder_with_deletion_vectors",
      "num": 311,
      "name": "zorder_with_deletion_vectors",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/311_zorder_with_deletion_vectors.sql",
      "read_script": "generator/spark-reads-iceberg/verify_311_zorder_with_deletion_vectors.py",
      "description": "Z-ORDER on table with deletion vectors",
      "status": "pass",
      "duration_ms": 179,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:24.005598+00:00",
      "read_cold_ms": 28,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 73,
      "write_warm_ms": 44,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3120_identity_colmap_physical_name",
      "num": 3120,
      "name": "identity_colmap_physical_name",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3120_identity_colmap_physical_name.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3120_identity_colmap_physical_name.py",
      "description": "Identity column combined with column mapping (mode=name). Logical names must be readable through Spark even though Delta uses physical names internally.",
      "status": "pass",
      "duration_ms": 119,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:48.724101+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 40,
      "write_warm_ms": 39,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3121_identity_partition_global_unique",
      "num": 3121,
      "name": "identity_partition_global_unique",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3121_identity_partition_global_unique.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3121_identity_partition_global_unique.py",
      "description": "Identity column on a partitioned table. Three separate INSERT operations each insert into a different partition. Identity must assign globally unique ids across all partitions (not per-partition sequences).",
      "status": "pass",
      "duration_ms": 155,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:48.880285+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 118,
      "write_warm_ms": 120,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3122_identity_optimize_hwm_stable",
      "num": 3122,
      "name": "identity_optimize_hwm_stable",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3122_identity_optimize_hwm_stable.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3122_identity_optimize_hwm_stable.py",
      "description": "OPTIMIZE between two identity inserts. HWM must survive the compaction and the second batch must receive ids 51-100 (not restart from 1).",
      "status": "pass",
      "duration_ms": 191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:49.072535+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3123_default_merge_not_matched",
      "num": 3123,
      "name": "default_merge_not_matched",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3123_default_merge_not_matched.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3123_default_merge_not_matched.py",
      "description": "MERGE NOT MATCHED inserts rows omitting the DEFAULT column (status). Pre-existing rows have status='active'; merged rows should receive 'pending' (default).",
      "status": "pass",
      "duration_ms": 166,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:49.238995+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 43,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 90,
      "write_warm_ms": 91,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3124_default_evolve_change_default",
      "num": 3124,
      "name": "default_evolve_change_default",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3124_default_evolve_change_default.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3124_default_evolve_change_default.py",
      "description": "ALTER TABLE ... ALTER COLUMN ... SET DEFAULT changes the default mid-stream. First 30 rows get priority=0 (old default); last 20 rows get priority=99 (new default).",
      "status": "pass",
      "duration_ms": 173,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:49.412878+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 51,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 98,
      "write_warm_ms": 111,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3125_default_cdc_pre_post_image",
      "num": 3125,
      "name": "default_cdc_pre_post_image",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3125_default_cdc_pre_post_image.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3125_default_cdc_pre_post_image.py",
      "description": "CDC captures pre/post images for rows with DEFAULT columns. INSERT omits status (gets default 'new'), then UPDATE changes it to 'processed'. CDF must contain pre-images with 'new' and post-images with 'processed'.",
      "status": "pass",
      "duration_ms": 226,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:49.639181+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 95,
      "write_warm_ms": 82,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3126_default_null_vs_default",
      "num": 3126,
      "name": "default_null_vs_default",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3126_default_null_vs_default.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3126_default_null_vs_default.py",
      "description": "Distinguishes explicit NULL from omitted column (DEFAULT). id=1 has x=NULL (explicitly inserted); ids 2-10 omit x and get x=42 (DEFAULT).",
      "status": "pass",
      "duration_ms": 167,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:49.807256+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 48,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 80,
      "write_warm_ms": 75,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3127_default_expression_current",
      "num": 3127,
      "name": "default_expression_current",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3127_default_expression_current.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3127_default_expression_current.py",
      "description": "Numeric DEFAULT 0 vs explicit total. First 30 rows provide total=price*qty; last 20 rows omit total and get DEFAULT 0.",
      "status": "pass",
      "duration_ms": 149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:49.956635+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 43,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3128_gencol_update_base_recompute",
      "num": 3128,
      "name": "gencol_update_base_recompute",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3128_gencol_update_base_recompute.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3128_gencol_update_base_recompute.py",
      "description": "UPDATE a base column. The generated column (sum_ab = a + b) must auto-recompute for all updated rows. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:50.225122+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 89,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 3129,
      "name": "gencol_cdc_computed_in_cdf",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3129_gencol_cdc_computed_in_cdf.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3129_gencol_cdc_computed_in_cdf.py",
      "description": "CDC with a generated column (total = price * qty). UPDATE changes price for ids 1-15. CDF post-images must contain the recomputed total. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:50.484910+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 85,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/312_zorder_high_cardinality",
      "num": 312,
      "name": "zorder_high_cardinality",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/312_zorder_high_cardinality.sql",
      "read_script": "generator/spark-reads-iceberg/verify_312_zorder_high_cardinality.py",
      "description": "Z-ORDER on high cardinality columns",
      "status": "pass",
      "duration_ms": 383,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:24.388866+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2397,
      "write_warm_ms": 2176,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:z-order",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 3130,
      "name": "gencol_partition_by_gencol",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3130_gencol_partition_by_gencol.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3130_gencol_partition_by_gencol.py",
      "description": "Generated column on a partitioned table (partitioned by a regular column, not the generated column itself). The doubled column (price * 2) must be correct for all rows across all partitions. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:50.953433+00:00",
      "read_cold_ms": 45,
      "read_warm_ms": 43,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 71,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 3131,
      "name": "gencol_identity_plus_gencol",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3131_gencol_identity_plus_gencol.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3131_gencol_identity_plus_gencol.py",
      "description": "Table combines GENERATED BY DEFAULT AS IDENTITY with a GENERATED ALWAYS AS column. Requires minWriterVersion=4 for the generated expression column.",
      "status": "pass",
      "duration_ms": 116,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.069671+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 74,
      "write_warm_ms": 58,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3132_gencol_three_gen_cols",
      "num": 3132,
      "name": "gencol_three_gen_cols",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3132_gencol_three_gen_cols.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3132_gencol_three_gen_cols.py",
      "description": "Table with three independent generated columns (gen1=a+b, gen2=b*c, gen3=a+c). All three must be computed correctly for every row. Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 156,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.226530+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 53,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3133_gencol_string_concat_gen",
      "num": 3133,
      "name": "gencol_string_concat_gen",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3133_gencol_string_concat_gen.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3133_gencol_string_concat_gen.py",
      "description": "Generated column using string CONCAT (full_name = CONCAT(first_name, ' ', last_name)). Requires minWriterVersion=4.",
      "status": "pass",
      "duration_ms": 128,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.354828+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 50,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3134_zorder_after_merge",
      "num": 3134,
      "name": "zorder_after_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3134_zorder_after_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3134_zorder_after_merge.py",
      "description": "OPTIMIZE after MERGE. 100 initial rows, merge inserts 50",
      "status": "pass",
      "duration_ms": 157,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.512193+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 191,
      "write_warm_ms": 190,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3135_zorder_cdc_table",
      "num": 3135,
      "name": "zorder_cdc_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3135_zorder_cdc_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3135_zorder_cdc_table.py",
      "description": "OPTIMIZE on a CDC-enabled table after UPDATE.",
      "status": "pass",
      "duration_ms": 133,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.646485+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 170,
      "write_warm_ms": 181,
      "tags": [
        "type:integer",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3136_zorder_colmap_table",
      "num": 3136,
      "name": "zorder_colmap_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3136_zorder_colmap_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3136_zorder_colmap_table.py",
      "description": "OPTIMIZE on a column-mapping-enabled table.",
      "status": "pass",
      "duration_ms": 143,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.790315+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 87,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3137_zorder_multi_column_three",
      "num": 3137,
      "name": "zorder_multi_column_three",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3137_zorder_multi_column_three.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3137_zorder_multi_column_three.py",
      "description": "OPTIMIZE on a table with three Z-order candidate columns.",
      "status": "pass",
      "duration_ms": 176,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:51.966691+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 46,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 98,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3138_zorder_timestamp_column",
      "num": 3138,
      "name": "zorder_timestamp_column",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3138_zorder_timestamp_column.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3138_zorder_timestamp_column.py",
      "description": "OPTIMIZE on a table containing timestamp-string data.",
      "status": "pass",
      "duration_ms": 133,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:52.100177+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 35,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 85,
      "write_warm_ms": 82,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3139_zorder_decimal_column",
      "num": 3139,
      "name": "zorder_decimal_column",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3139_zorder_decimal_column.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3139_zorder_decimal_column.py",
      "description": "OPTIMIZE on a table with DECIMAL(18,4) column.",
      "status": "pass",
      "duration_ms": 152,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:52.253125+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 94,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/313_zorder_mixed_types",
      "num": 313,
      "name": "zorder_mixed_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/313_zorder_mixed_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_313_zorder_mixed_types.py",
      "description": "Z-ORDER on mixed column types (INT + STRING + DATE)",
      "status": "pass",
      "duration_ms": 387,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:24.776714+00:00",
      "read_cold_ms": 51,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 310,
      "write_warm_ms": 335,
      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:z-order",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3140_zorder_after_schema_evolve",
      "num": 3140,
      "name": "zorder_after_schema_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3140_zorder_after_schema_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3140_zorder_after_schema_evolve.py",
      "description": "OPTIMIZE after schema evolution (ADD COLUMN).",
      "status": "pass",
      "duration_ms": 137,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:52.688510+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 231,
      "write_warm_ms": 368,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3141_zorder_restore_after_zorder",
      "num": 3141,
      "name": "zorder_restore_after_zorder",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3141_zorder_restore_after_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3141_zorder_restore_after_zorder.py",
      "description": "RESTORE TO VERSION 1 (pre-optimize) after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 161,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:23:52.850382+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 136,
      "write_warm_ms": 139,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:time-travel",
        "delta:z-order",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3142_zorder_partitioned_multi_key",
      "num": 3142,
      "name": "zorder_partitioned_multi_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3142_zorder_partitioned_multi_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3142_zorder_partitioned_multi_key.py",
      "description": "OPTIMIZE on a partitioned table with multi-column Z-order keys.",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 3192,
      "name": "dv_partition_cross",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3192_dv_partition_cross.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3192_dv_partition_cross.py",
      "description": "DV + partitioning. DELETE only from specific partitions,",
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      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:07.417495+00:00",
      "read_cold_ms": 63,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3193_dv_restore_deleted_rows.py",
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      "status": "pass",
      "duration_ms": 153,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:07.571058+00:00",
      "read_cold_ms": 40,
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      "skip_feature": null,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "format": "iceberg",
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      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:07.765264+00:00",
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        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3195_dv_vacuum_cleanup.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3195_dv_vacuum_cleanup.py",
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      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 68,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 172,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "delta:optimize",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3196_dv_widen_after_delete",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 170,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:08.144722+00:00",
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "schema:type-widening",
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        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:08.307670+00:00",
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      "read_warm_ms": 50,
      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3198_ict_constraint_check.py",
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      "error": null,
      "notes": null,
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      "verified_at": "2026-05-18T20:24:08.416617+00:00",
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      "read_warm_ms": 30,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3199_ict_default_values",
      "num": 3199,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3199_ict_default_values.py",
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      "duration_ms": 189,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:08.605800+00:00",
      "read_cold_ms": 29,
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      "skip_feature": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "id": "df-writes/iceberg/319_merge_comprehensive_basic",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/319_merge_comprehensive_basic.sql",
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      "verified_at": "2026-05-20T12:25:26.652327+00:00",
      "read_cold_ms": 20,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 32,
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        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
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        "delta:column-mapping",
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        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_31_dv_descriptor_schema_validation.py",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:27.049047+00:00",
      "read_cold_ms": 144,
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      "skip_feature": null,
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        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
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        "dml:update",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "writer": "delta-forge",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:09.251873+00:00",
      "read_cold_ms": 45,
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        "dml:insert",
        "delta:column-mapping",
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        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3201_ict_optimize_timestamps.sql",
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      "duration_ms": 113,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:09.366002+00:00",
      "read_cold_ms": 32,
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      "skip_feature": null,
      "skip_cause": null,
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      "write_warm_ms": 620,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:09.490126+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 30,
      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3203_ict_restore_timestamps.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3203_ict_restore_timestamps.py",
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      "duration_ms": 163,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:09.654113+00:00",
      "read_cold_ms": 51,
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3204_ict_time_travel_ts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3204_ict_time_travel_ts.py",
      "description": "ICT + time travel. INSERT 50, INSERT 50 more.",
      "status": "pass",
      "duration_ms": 177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:09.832410+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 46,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
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      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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    {
      "id": "df-writes/iceberg/3205_ict_vacuum_safe",
      "num": 3205,
      "name": "ict_vacuum_safe",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3205_ict_vacuum_safe.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3205_ict_vacuum_safe.py",
      "description": "ICT + VACUUM. INSERT 50, DELETE WHERE id<=20, VACUUM.",
      "status": "pass",
      "duration_ms": 196,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:10.028891+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
      "write_warm_ms": 177,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "id": "df-writes/iceberg/3206_ict_widen_preserve",
      "num": 3206,
      "name": "ict_widen_preserve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3206_ict_widen_preserve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3206_ict_widen_preserve.py",
      "description": "ICT + type widening (INT->BIGINT).",
      "status": "pass",
      "duration_ms": 131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:10.160206+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 189,
      "write_warm_ms": 157,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3207_widen_checkpoint_metadata",
      "num": 3207,
      "name": "widen_checkpoint_metadata",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3207_widen_checkpoint_metadata.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3207_widen_checkpoint_metadata.py",
      "description": "Type widening + checkpoint. INSERT 50, widen INT->BIGINT,",
      "status": "pass",
      "duration_ms": 187,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:24:10.347608+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 942,
      "write_warm_ms": 928,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:type-widening",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "manual_tags": [],
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      "writer": "delta-forge",
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      "manual_tags": [],
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      "notes": null,
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      "manual_tags": [],
      "concern_note": null
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "manual_tags": [],
      "concern_note": null
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      "manual_tags": [],
      "concern_note": null
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      "writer": "delta-forge",
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      "error": null,
      "notes": null,
      "extras": {},
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        "dml:insert",
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      "manual_tags": [],
      "concern_note": null
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3252_truncate_optimize_empty.sql",
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      "error": null,
      "notes": null,
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      "manual_tags": [],
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      "type": "df-writes",
      "format": "iceberg",
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      "error": null,
      "notes": null,
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        "dml:insert",
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        "delta:column-mapping",
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      "manual_tags": [],
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      "error": null,
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      "manual_tags": [],
      "concern_note": null
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      "format": "iceberg",
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      "reader": "spark",
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      "error": null,
      "notes": null,
      "extras": {},
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        "dml:insert",
        "delta:column-mapping",
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      "manual_tags": [],
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_cold_ms": 52,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 174,
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3403_partition_boolean_key",
      "num": 3403,
      "name": "partition_boolean_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3403_partition_boolean_key.py",
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      "status": "pass",
      "duration_ms": 163,
      "error": null,
      "notes": null,
      "extras": {},
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      "read_cold_ms": 56,
      "read_warm_ms": 47,
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      "write_cold_ms": 155,
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        "type:boolean",
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3404_partition_int_key",
      "num": 3404,
      "name": "partition_int_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3404_partition_int_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3404_partition_int_key.py",
      "description": "INT partition key with 10 buckets",
      "status": "pass",
      "duration_ms": 199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:06.586154+00:00",
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 256,
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        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3405_partition_date_key",
      "num": 3405,
      "name": "partition_date_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3405_partition_date_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3405_partition_date_key.py",
      "description": "DATE partition key with 5 distinct dates",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:06.711453+00:00",
      "read_cold_ms": 31,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3406_partition_delete_one",
      "num": 3406,
      "name": "partition_delete_one",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3406_partition_delete_one.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3406_partition_delete_one.py",
      "description": "DELETE entire partition",
      "status": "pass",
      "duration_ms": 178,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:06.890555+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 240,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3407_partition_update_within",
      "num": 3407,
      "name": "partition_update_within",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3407_partition_update_within.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3407_partition_update_within.py",
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      "status": "pass",
      "duration_ms": 278,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:07.169211+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 81,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 263,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3408_partition_merge_cross",
      "num": 3408,
      "name": "partition_merge_cross",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3408_partition_merge_cross.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3408_partition_merge_cross.py",
      "description": "MERGE across partitions",
      "status": "pass",
      "duration_ms": 309,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:07.479529+00:00",
      "read_cold_ms": 93,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3409_partition_optimize_per",
      "num": 3409,
      "name": "partition_optimize_per",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3409_partition_optimize_per.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3409_partition_optimize_per.py",
      "description": "OPTIMIZE on partitioned table after multiple inserts",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:07.639970+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 780,
      "write_warm_ms": 814,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/340_dv_write_compat",
      "num": 340,
      "name": "dv_write_compat",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/340_dv_write_compat.sql",
      "read_script": "generator/spark-reads-iceberg/verify_340_dv_write_compat.py",
      "description": "DBX reads DeltaForge-created DVs",
      "status": "pass",
      "duration_ms": 61,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:33.368549+00:00",
      "read_cold_ms": 21,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 36,
      "write_warm_ms": 20,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3410_partition_vacuum_selective",
      "num": 3410,
      "name": "partition_vacuum_selective",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3410_partition_vacuum_selective.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3410_partition_vacuum_selective.py",
      "description": "VACUUM after partition delete",
      "status": "pass",
      "duration_ms": 169,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:07.948724+00:00",
      "read_cold_ms": 45,
      "read_warm_ms": 50,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 248,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3411_partition_restore_full",
      "num": 3411,
      "name": "partition_restore_full",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3411_partition_restore_full.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3411_partition_restore_full.py",
      "description": "RESTORE partitioned table to prior version",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:08.109870+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 280,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3412_partition_time_travel",
      "num": 3412,
      "name": "partition_time_travel",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3412_partition_time_travel.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3412_partition_time_travel.py",
      "description": "Time travel on partitioned table",
      "status": "pass",
      "duration_ms": 208,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:08.318548+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 266,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:time-travel",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3413_partition_stats_pushdown",
      "num": 3413,
      "name": "partition_stats_pushdown",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3413_partition_stats_pushdown.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3413_partition_stats_pushdown.py",
      "description": "Stats on partitioned table (400 rows, 100 per region)",
      "status": "pass",
      "duration_ms": 219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:08.538300+00:00",
      "read_cold_ms": 53,
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      "skip_feature": null,
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      "tags": [
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        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3414_partition_cdc_per_partition",
      "num": 3414,
      "name": "partition_cdc_per_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3414_partition_cdc_per_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3414_partition_cdc_per_partition.py",
      "description": "CDC per partition -- CDF has update records for US only",
      "status": "pass",
      "duration_ms": 273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:08.811722+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 116,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 283,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3415_partition_identity_unique",
      "num": 3415,
      "name": "partition_identity_unique",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3415_partition_identity_unique.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3415_partition_identity_unique.py",
      "description": "IDENTITY column across partitions",
      "status": "pass",
      "duration_ms": 140,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:08.952422+00:00",
      "read_cold_ms": 39,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 175,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:default-values",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3416_partition_colmap_names",
      "num": 3416,
      "name": "partition_colmap_names",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3416_partition_colmap_names.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3416_partition_colmap_names.py",
      "description": "Column mapping (name mode) on partitioned table",
      "status": "pass",
      "duration_ms": 133,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:09.086203+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 176,
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      "tags": [
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3417_partition_constraint_per",
      "num": 3417,
      "name": "partition_constraint_per",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3417_partition_constraint_per.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3417_partition_constraint_per.py",
      "description": "CHECK constraint on partitioned table",
      "status": "pass",
      "duration_ms": 148,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:09.234796+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 183,
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      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3418_partition_widen_col",
      "num": 3418,
      "name": "partition_widen_col",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3418_partition_widen_col.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3418_partition_widen_col.py",
      "description": "Widen column type on partitioned table (INT -> BIGINT)",
      "status": "pass",
      "duration_ms": 162,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:09.397663+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 281,
      "write_warm_ms": 311,
      "tags": [
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        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3419_partition_dv_per_partition",
      "num": 3419,
      "name": "partition_dv_per_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3419_partition_dv_per_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3419_partition_dv_per_partition.py",
      "description": "Deletion vectors per partition -- DELETE only in US partition",
      "status": "pass",
      "duration_ms": 193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:25:09.591835+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 54,
      "skip_feature": null,
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      "write_cold_ms": 234,
      "write_warm_ms": 231,
      "tags": [
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        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/341_dv_roaring_format",
      "num": 341,
      "name": "dv_roaring_format",
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      "writer": "delta-forge",
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        "dml:insert",
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    },
    {
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      "verified_at": "2026-05-18T20:26:11.950665+00:00",
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        "dml:insert",
        "delta:column-mapping",
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        "format:iceberg"
      ],
      "manual_tags": [],
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    },
    {
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      "read_cold_ms": 33,
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      "manual_tags": [],
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    },
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      "verified_at": "2026-05-18T20:26:12.183148+00:00",
      "read_cold_ms": 28,
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      "skip_cause": null,
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        "dml:insert",
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      "manual_tags": [],
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    },
    {
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      "duration_ms": 109,
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      "extras": {},
      "verified_at": "2026-05-18T20:26:12.293336+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 27,
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      "write_cold_ms": 50,
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        "type:integer",
        "type:timestamp",
        "dml:insert",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
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      "description": "Schema changes during restore/time travel scenarios",
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      "verified_at": "2026-05-20T12:25:38.015984+00:00",
      "read_cold_ms": 44,
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
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      "duration_ms": 130,
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      "verified_at": "2026-05-18T20:26:12.584398+00:00",
      "read_cold_ms": 47,
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        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3691_stats_after_update.py",
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      "status": "pass",
      "duration_ms": 211,
      "error": null,
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      "verified_at": "2026-05-18T20:26:12.795674+00:00",
      "read_cold_ms": 65,
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      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
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      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3692_stats_after_delete",
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
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      "description": "Stats -- INSERT 500 then DELETE WHERE val<=10.",
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      "verified_at": "2026-05-18T20:26:12.958419+00:00",
      "read_cold_ms": 49,
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
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      "writer": "delta-forge",
      "reader": "spark",
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      "duration_ms": 252,
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      "extras": {},
      "verified_at": "2026-05-18T20:26:13.211027+00:00",
      "read_cold_ms": 85,
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      "skip_feature": null,
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      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3694_stats_after_optimize",
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      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3694_stats_after_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3694_stats_after_optimize.py",
      "description": "Stats -- INSERT 500 then OPTIMIZE (compaction).",
      "status": "pass",
      "duration_ms": 136,
      "error": null,
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      "verified_at": "2026-05-18T20:26:13.347767+00:00",
      "read_cold_ms": 47,
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        "iceberg:uniform",
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      "manual_tags": [],
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    },
    {
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      "num": 3695,
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      "reader": "spark",
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      "error": null,
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      "read_cold_ms": 45,
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      "skip_feature": null,
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      "manual_tags": [],
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    },
    {
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      "format": "iceberg",
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3696_stats_binary_col.py",
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      "error": null,
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      "verified_at": "2026-05-18T20:26:13.624250+00:00",
      "read_cold_ms": 39,
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      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3697_stats_partition_stats.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3697_stats_partition_stats.py",
      "description": "Stats -- PARTITIONED BY(region) with 400 rows, per-partition stats.",
      "status": "pass",
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      "error": null,
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      "read_cold_ms": 42,
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      "skip_feature": null,
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      "tags": [
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      "manual_tags": [],
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    },
    {
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      "reader": "spark",
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      "error": null,
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      "read_cold_ms": 50,
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      "manual_tags": [],
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3699_stats_wide_column_stats.py",
      "description": "Stats -- 20-column table with stats collected per column. INSERT 200.",
      "status": "pass",
      "duration_ms": 137,
      "error": null,
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      "verified_at": "2026-05-18T20:26:14.053045+00:00",
      "read_cold_ms": 52,
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null
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      "num": 369,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/369_restore_partitioned.sql",
      "read_script": "generator/spark-reads-iceberg/verify_369_restore_partitioned.py",
      "description": "Partitioned table operations with DELETE and restore scenarios",
      "status": "pass",
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      "error": null,
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      "verified_at": "2026-05-20T12:25:38.175923+00:00",
      "read_cold_ms": 60,
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      "skip_feature": null,
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        "delta:column-mapping",
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        "delta:time-travel",
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      "format": "iceberg",
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      "reader": "spark",
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      "description": "Demonstrates deletion vector binary format using RoaringBitmap. DVs use portable RoaringBitmap serialization format (Croaring library). Stores row indices efficiently with run-length encoding.",
      "status": "pass",
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      "error": null,
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        "delta:column-mapping",
        "delta:constraints",
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      "manual_tags": [],
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3700_stats_long_string_truncate.py",
      "description": "Stats -- long strings (>1KB) should be truncated in stats min/max.",
      "status": "pass",
      "duration_ms": 111,
      "error": null,
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      "verified_at": "2026-05-18T20:26:14.624792+00:00",
      "read_cold_ms": 35,
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      "tags": [
        "type:integer",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3718_zorder_negative_range",
      "num": 3718,
      "name": "zorder_negative_range",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3718_zorder_negative_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3718_zorder_negative_range.py",
      "description": "Z-ORDER on negative INT range. INSERT 200 with val = -100..100.",
      "status": "pass",
      "duration_ms": 276,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:21.145607+00:00",
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      "skip_feature": null,
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        "type:integer",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "num": 3719,
      "name": "zorder_boolean_col",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3719_zorder_boolean_col.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3719_zorder_boolean_col.py",
      "description": "Z-ORDER on BOOLEAN column. INSERT 300 + OPTIMIZE ZORDER BY (flag).",
      "status": "pass",
      "duration_ms": 360,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:21.506341+00:00",
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      "skip_feature": null,
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        "type:boolean",
        "type:integer",
        "dml:insert",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/371_restore_after_optimize",
      "num": 371,
      "name": "restore_after_optimize",
      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/371_restore_after_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_371_restore_after_optimize.py",
      "description": "Testing RESTORE operation after OPTIMIZE.",
      "status": "pass",
      "duration_ms": 374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:39.033398+00:00",
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      "skip_feature": null,
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        "type:integer",
        "type:string",
        "dml:insert",
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      ],
      "manual_tags": [],
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    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
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      "read_script": "generator/spark-reads-iceberg/verify_3720_zorder_after_widen.py",
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      "status": "pass",
      "duration_ms": 274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:22.006042+00:00",
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      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
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      "write_script": "generator/df-sql-iceberg/3721_zorder_after_restore.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3721_zorder_after_restore.py",
      "description": "Z-ORDER after RESTORE. INSERT 200 + DELETE 50 + RESTORE v1 + ZORDER.",
      "status": "pass",
      "duration_ms": 326,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:22.332513+00:00",
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      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3722_zorder_multi_round",
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      "type": "df-writes",
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3722_zorder_multi_round.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3722_zorder_multi_round.py",
      "description": "Multiple ZORDER rounds. INSERT+ZORDER x3.",
      "status": "pass",
      "duration_ms": 316,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:22.649054+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
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        "type:integer",
        "dml:insert",
        "delta:column-mapping",
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        "delta:z-order",
        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3723_struct_two_fields",
      "num": 3723,
      "name": "struct_two_fields",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3723_struct_two_fields.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3723_struct_two_fields.py",
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      "duration_ms": 149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:22.798590+00:00",
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      "read_warm_ms": 30,
      "skip_feature": null,
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      "write_cold_ms": 39,
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      "tags": [
        "type:integer",
        "type:string",
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        "dml:insert",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3724_struct_five_fields",
      "num": 3724,
      "name": "struct_five_fields",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3724_struct_five_fields.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3724_struct_five_fields.py",
      "description": "STRUCT with 5 mixed-type fields. INSERT 100.",
      "status": "pass",
      "duration_ms": 143,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:22.942648+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 46,
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      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
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        "dml:insert",
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        "delta:deletion-vectors",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3725_struct_with_null",
      "num": 3725,
      "name": "struct_with_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3725_struct_with_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3725_struct_with_null.py",
      "description": "STRUCT where one field is NULL (conditionally).",
      "status": "pass",
      "duration_ms": 141,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:23.084876+00:00",
      "read_cold_ms": 36,
      "read_warm_ms": 43,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 42,
      "write_warm_ms": 42,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "delta:column-mapping",
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3726_struct_with_decimal",
      "num": 3726,
      "name": "struct_with_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3726_struct_with_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3726_struct_with_decimal.py",
      "description": "STRUCT<price:DECIMAL(18,2), qty:INT>. INSERT 100.",
      "status": "pass",
      "duration_ms": 143,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:23.229050+00:00",
      "read_cold_ms": 47,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 38,
      "write_warm_ms": 45,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:struct",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3727_struct_with_boolean",
      "num": 3727,
      "name": "struct_with_boolean",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3727_struct_with_boolean.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3727_struct_with_boolean.py",
      "description": "STRUCT<active:BOOLEAN, val:INT>. INSERT 100.",
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      "duration_ms": 150,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:23.379467+00:00",
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      "read_warm_ms": 31,
      "skip_feature": null,
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      "tags": [
        "type:boolean",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3728_struct_with_date",
      "num": 3728,
      "name": "struct_with_date",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3728_struct_with_date.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3728_struct_with_date.py",
      "description": "STRUCT<dt:DATE, val:INT>. INSERT 100.",
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      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:23.540354+00:00",
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      "read_warm_ms": 26,
      "skip_feature": null,
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      "tags": [
        "type:date",
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        "iceberg:uniform",
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3729_struct_nested_two",
      "num": 3729,
      "name": "struct_nested_two",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3729_struct_nested_two.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3729_struct_nested_two.py",
      "description": "STRUCT<outer:STRING, inner:STRUCT<a:INT, b:STRING>>. INSERT 50.",
      "status": "pass",
      "duration_ms": 168,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-18T20:26:23.709178+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 44,
      "skip_feature": null,
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      "write_warm_ms": 39,
      "tags": [
        "type:integer",
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      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/372_restore_creates_version",
      "num": 372,
      "name": "restore_creates_version",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/372_restore_creates_version.sql",
      "read_script": "generator/spark-reads-iceberg/verify_372_restore_creates_version.py",
      "description": "Version tracking during restore operations",
      "status": "pass",
      "duration_ms": 262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:39.295699+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 43,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 237,
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      "tags": [
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      "manual_tags": [],
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    {
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      "num": 3730,
      "name": "struct_nested_three",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3730_struct_nested_three.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3730_struct_nested_three.py",
      "description": "3-level nested STRUCT. INSERT 30.",
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      "duration_ms": 174,
      "error": null,
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      "extras": {},
      "verified_at": "2026-05-18T20:26:24.062272+00:00",
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      "skip_feature": null,
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      "tags": [
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/3731_struct_update_field",
      "num": 3731,
      "name": "struct_update_field",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3731_struct_update_field.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3731_struct_update_field.py",
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      "duration_ms": 242,
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      "skip_feature": null,
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      "tags": [
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      "manual_tags": [],
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      "id": "df-writes/iceberg/3732_array_int_basic",
      "num": 3732,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/3732_array_int_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_3732_array_int_basic.py",
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      "duration_ms": 136,
      "error": null,
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      "skip_feature": null,
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      "write_warm_ms": 50,
      "tags": [
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      "num": 3733,
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3733_array_string_basic.py",
      "description": "ARRAY<STRING> with 3 elements per row. INSERT 50.",
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      "duration_ms": 161,
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      "tags": [
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      "manual_tags": [],
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    {
      "id": "df-writes/iceberg/3734_array_of_arrays",
      "num": 3734,
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_3734_array_of_arrays.py",
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        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/380_vacuum_cdc_cross",
      "num": 380,
      "name": "vacuum_cdc_cross",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/380_vacuum_cdc_cross.sql",
      "read_script": "generator/spark-reads-iceberg/verify_380_vacuum_cdc_cross.py",
      "description": "DeltaForge VACUUM on DBX CDF table",
      "status": "pass",
      "duration_ms": 903,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:45.693704+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 225,
      "write_warm_ms": 264,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:vacuum",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/381_vacuum_cdc_comprehensive",
      "num": 381,
      "name": "vacuum_cdc_comprehensive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/381_vacuum_cdc_comprehensive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_381_vacuum_cdc_comprehensive.py",
      "description": "Full VACUUM CDC roundtrip with all scenarios",
      "status": "pass",
      "duration_ms": 2039,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:47.733704+00:00",
      "read_cold_ms": 339,
      "read_warm_ms": 348,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 14788,
      "write_warm_ms": 13734,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/382_multipart_checkpoint",
      "num": 382,
      "name": "multipart_checkpoint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/382_multipart_checkpoint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_382_multipart_checkpoint.py",
      "description": "Large multi-part V2 checkpoint creation",
      "status": "pass",
      "duration_ms": 1397,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:49.131324+00:00",
      "read_cold_ms": 294,
      "read_warm_ms": 148,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 13421,
      "write_warm_ms": 13745,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:checkpoint-multipart",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/383_multipart_read",
      "num": 383,
      "name": "multipart_read",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/383_multipart_read.sql",
      "read_script": "generator/spark-reads-iceberg/verify_383_multipart_read.py",
      "description": "DeltaForge reads DBX multi-part checkpoint",
      "status": "pass",
      "duration_ms": 887,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:50.019188+00:00",
      "read_cold_ms": 105,
      "read_warm_ms": 104,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 5111,
      "write_warm_ms": 4800,
      "tags": [
        "type:array",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/384_multipart_write",
      "num": 384,
      "name": "multipart_write",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/384_multipart_write.sql",
      "read_script": "generator/spark-reads-iceberg/verify_384_multipart_write.py",
      "description": "DeltaForge creates multi-part checkpoint",
      "status": "pass",
      "duration_ms": 490,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:50.509955+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1470,
      "write_warm_ms": 1221,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/385_multipart_dv",
      "num": 385,
      "name": "multipart_dv",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/385_multipart_dv.sql",
      "read_script": "generator/spark-reads-iceberg/verify_385_multipart_dv.py",
      "description": "Multi-part checkpoint including deletion vector metadata",
      "status": "pass",
      "duration_ms": 2654,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:53.164358+00:00",
      "read_cold_ms": 143,
      "read_warm_ms": 173,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 8980,
      "write_warm_ms": 6801,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/386_multipart_comprehensive",
      "num": 386,
      "name": "multipart_comprehensive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/386_multipart_comprehensive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_386_multipart_comprehensive.py",
      "description": "Full multi-part checkpoint roundtrip",
      "status": "pass",
      "duration_ms": 1879,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:55.044412+00:00",
      "read_cold_ms": 194,
      "read_warm_ms": 119,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 7619,
      "write_warm_ms": 7682,
      "tags": [
        "type:array",
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/387_concurrent_optimize",
      "num": 387,
      "name": "concurrent_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/387_concurrent_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_387_concurrent_optimize.py",
      "description": "Demonstrates OPTIMIZE while both engines write: 1. Generator creates fragmented table 2. DeltaForge INSERT runs concurrently 3. DeltaForge OPTIMIZE runs 4. DBX verifies no conflicts, both succeed",
      "status": "pass",
      "duration_ms": 226,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:55.271032+00:00",
      "read_cold_ms": 19,
      "read_warm_ms": 12,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 57,
      "write_warm_ms": 48,
      "tags": [
        "type:integer",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/388_concurrent_vacuum",
      "num": 388,
      "name": "concurrent_vacuum",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/388_concurrent_vacuum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_388_concurrent_vacuum.py",
      "description": "Demonstrates VACUUM while both engines write: 1. Generator creates table with history 2. Active writes from both engines 3. DeltaForge VACUUM runs 4. All operations should succeed",
      "status": "pass",
      "duration_ms": 300,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:55.572077+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 21,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 47,
      "write_warm_ms": 38,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:vacuum",
        "iceberg:uniform",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/389_concurrent_zorder",
      "num": 389,
      "name": "concurrent_zorder",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/389_concurrent_zorder.sql",
      "read_script": "generator/spark-reads-iceberg/verify_389_concurrent_zorder.py",
      "description": "Demonstrates Z-ORDER while both engines write: 1. Generator creates table with data for Z-ORDER 2. Table being actively written to 3. Z-ORDER operation from DeltaForge 4. Both should complete successfully",
      "status": "pass",
      "duration_ms": 361,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:55.934166+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 41,
      "write_warm_ms": 99,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:z-order",
        "iceberg:uniform",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/38_iceberg_compat_v1_uniform",
      "num": 38,
      "name": "iceberg_compat_v1_uniform",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/38_iceberg_compat_v1_uniform.sql",
      "read_script": "generator/spark-reads-iceberg/verify_38_iceberg_compat_v1_uniform.py",
      "description": "Demonstrates Iceberg compatibility V1 enabling reading Delta tables as Iceberg tables. Writes Iceberg metadata alongside Delta metadata for universal data lake access.",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:56.155015+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 259,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/390_interleaved_maintenance",
      "num": 390,
      "name": "interleaved_maintenance",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/390_interleaved_maintenance.sql",
      "read_script": "generator/spark-reads-iceberg/verify_390_interleaved_maintenance.py",
      "description": "Demonstrates interleaved maintenance operations between engines: 1. DBX creates table and performs OPTIMIZE 2. DeltaForge INSERT 3. DeltaForge VACUUM 4. DBX INSERT 5. All history preserved correctly",
      "status": "pass",
      "duration_ms": 674,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:56.829904+00:00",
      "read_cold_ms": 21,
      "read_warm_ms": 13,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 26,
      "write_warm_ms": 24,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:optimize",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/391_coordinated_comprehensive",
      "num": 391,
      "name": "coordinated_comprehensive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/391_coordinated_comprehensive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_391_coordinated_comprehensive.py",
      "description": "Demonstrates full coordinated commits scenario: 1. Both engines actively writing 2. Maintenance operations from both 3. Conflict resolution validation 4. Full history and CDC verification",
      "status": "pass",
      "duration_ms": 1186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:58.016316+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 202,
      "write_warm_ms": 139,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/392_dv_cdc_delete",
      "num": 392,
      "name": "dv_cdc_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/392_dv_cdc_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_392_dv_cdc_delete.py",
      "description": "Demonstrates deletion vectors + change data feed + DELETE operation: 1. Insert 200 rows with deterministic data 2. DELETE WHERE id % 5 = 0 (removes 40 rows) 3. Final state: 160 rows",
      "status": "pass",
      "duration_ms": 130,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:58.147051+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 152,
      "write_warm_ms": 64,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/393_dv_cdc_update",
      "num": 393,
      "name": "dv_cdc_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/393_dv_cdc_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_393_dv_cdc_update.py",
      "description": "Demonstrates deletion vectors + change data feed + UPDATE operation: 1. Insert 200 rows with deterministic data (all status = 'open') 2. UPDATE SET status = 'closed' WHERE id <= 50 3. Final state: 200 rows (50 closed, 150 open)",
      "status": "pass",
      "duration_ms": 188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:58.335896+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 97,
      "write_warm_ms": 106,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/394_dv_cdc_merge",
      "num": 394,
      "name": "dv_cdc_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/394_dv_cdc_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_394_dv_cdc_merge.py",
      "description": "Demonstrates deletion vectors + change data feed + MERGE operation: 1. Insert 100 rows (id 1..100) 2. MERGE from a CTE source of 120 rows (id 51..170) - MATCHED (id 51..100): UPDATE SET score = source.score + 1000 - NOT MATCHED (id 101..170): INSERT all source columns 3. Final...",
      "status": "pass",
      "duration_ms": 195,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:58.531752+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 411,
      "write_warm_ms": 348,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/395_dv_cdc_delete_update",
      "num": 395,
      "name": "dv_cdc_delete_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/395_dv_cdc_delete_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_395_dv_cdc_delete_update.py",
      "description": "Demonstrates deletion vectors + change data feed + DELETE then UPDATE: 1. Insert 300 rows (id 1..300) 2. DELETE WHERE id % 10 = 0 (removes 30 rows) 3. UPDATE SET value = value * 2 WHERE id % 3 = 0 (among remaining rows) 4. Final state: 270 rows",
      "status": "pass",
      "duration_ms": 194,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:58.726136+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 210,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/396_evolve_then_update",
      "num": 396,
      "name": "evolve_then_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/396_evolve_then_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_396_evolve_then_update.py",
      "description": "Demonstrates schema evolution + UPDATE: 1. Insert 100 rows (id 1..100) with 3 columns 2. ALTER TABLE ADD COLUMN priority INT 3. UPDATE SET priority = id % 5 WHERE id <= 50 4. INSERT 20 new rows (id 101..120) with priority populated 5. Final state: 120 rows",
      "status": "pass",
      "duration_ms": 186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:58.912263+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 129,
      "write_warm_ms": 205,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/397_evolve_then_delete",
      "num": 397,
      "name": "evolve_then_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/397_evolve_then_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_397_evolve_then_delete.py",
      "description": "Schema evolution (ADD COLUMN) followed by DELETE. 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN extra STRING 3. INSERT 50 rows (id 101-150) with extra populated 4. DELETE WHERE id <= 30",
      "status": "pass",
      "duration_ms": 632,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:25:59.544586+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 96,
      "write_warm_ms": 146,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/398_evolve_then_merge",
      "num": 398,
      "name": "evolve_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/398_evolve_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_398_evolve_then_merge.py",
      "description": "Schema evolution (ADD COLUMN) followed by MERGE. 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN tag STRING 3. MERGE from 120-row CTE (id 1-120): - MATCHED: UPDATE SET tag='merged', value=source.value - NOT MATCHED: INSERT all columns",
      "status": "pass",
      "duration_ms": 630,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:00.175526+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 71,
      "write_warm_ms": 110,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/399_evolve_add_then_drop",
      "num": 399,
      "name": "evolve_add_then_drop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/399_evolve_add_then_drop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_399_evolve_add_then_drop.py",
      "description": "ADD COLUMN then DROP COLUMN with column mapping enabled. 1. INSERT 50 rows (id 1-50) 2. ALTER TABLE ADD COLUMN temp STRING 3. INSERT 20 rows (id 51-70) with temp populated 4. ALTER TABLE DROP COLUMN temp 5. INSERT 10 rows (id 71-80) with 3 columns",
      "status": "pass",
      "duration_ms": 222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:00.398517+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 111,
      "write_warm_ms": 88,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/39_iceberg_compat_v2_advanced",
      "num": 39,
      "name": "iceberg_compat_v2_advanced",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/39_iceberg_compat_v2_advanced.sql",
      "read_script": "generator/spark-reads-iceberg/verify_39_iceberg_compat_v2_advanced.py",
      "description": "Demonstrates Iceberg compatibility V2 with column mapping for row-level operations. V2 adds support for row-level operations metadata compatibility.",
      "status": "pass",
      "duration_ms": 252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:00.650939+00:00",
      "read_cold_ms": 42,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 227,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/400_colmap_update_delete",
      "num": 400,
      "name": "colmap_update_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/400_colmap_update_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_400_colmap_update_delete.py",
      "description": "Column mapping with UPDATE and DELETE operations. 1. INSERT 200 rows (id 1-200) 2. UPDATE SET status='closed' WHERE score < 20 3. DELETE WHERE category='D' (removes 50 rows where i%4=3)",
      "status": "pass",
      "duration_ms": 1014,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:01.665898+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 86,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 122,
      "write_warm_ms": 55,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/401_colmap_merge",
      "num": 401,
      "name": "colmap_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/401_colmap_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_401_colmap_merge.py",
      "description": "Column mapping with MERGE operation. 1. INSERT 100 rows (id 1-100) 2. MERGE from 130-row CTE (id 1-130): - MATCHED: UPDATE SET price=source.price - NOT MATCHED: INSERT all columns",
      "status": "pass",
      "duration_ms": 517,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:02.183355+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 247,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/402_colmap_evolve_dml",
      "num": 402,
      "name": "colmap_evolve_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/402_colmap_evolve_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_402_colmap_evolve_dml.py",
      "description": "Column mapping (name mode) + schema evolution + DML: 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN rating INT 3. UPDATE SET rating = score % 10 WHERE id <= 50 4. INSERT 30 rows (id 101-130) with rating populated",
      "status": "pass",
      "duration_ms": 238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:02.422077+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 256,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/403_cdc_evolve_dml",
      "num": 403,
      "name": "cdc_evolve_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/403_cdc_evolve_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_403_cdc_evolve_dml.py",
      "description": "CDC (Change Data Feed) + schema evolution + DML: 1. INSERT 100 rows (id 1-100) with 3 columns 2. ALTER TABLE ADD COLUMN tag STRING 3. INSERT 30 rows (id 101-130) with tag populated 4. UPDATE SET tag = 'migrated' WHERE id <= 50 5. DELETE WHERE id > 90 AND id <= 100",
      "status": "pass",
      "duration_ms": 206,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:02.628226+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 242,
      "write_warm_ms": 228,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/404_dv_evolve_delete",
      "num": 404,
      "name": "dv_evolve_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/404_dv_evolve_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_404_dv_evolve_delete.py",
      "description": "Deletion vectors + schema evolution after DV-creating DELETE: 1. INSERT 200 rows (id 1-200) with 3 columns 2. DELETE WHERE id % 4 = 0 (removes 50 rows, creates DVs) 3. ALTER TABLE ADD COLUMN extra STRING 4. INSERT 50 rows (id 201-250) with extra populated",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:02.837428+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 186,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/405_dv_evolve_update",
      "num": 405,
      "name": "dv_evolve_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/405_dv_evolve_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_405_dv_evolve_update.py",
      "description": "Deletion vectors + schema evolution + UPDATE after evolution: 1. INSERT 200 rows (id 1-200) with 3 columns 2. DELETE WHERE id <= 20 (removes 20 rows) 3. ALTER TABLE ADD COLUMN flag BOOLEAN 4. UPDATE SET flag = true WHERE id > 180",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:03.045188+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 135,
      "write_warm_ms": 107,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/406_cdc_partition_dml",
      "num": 406,
      "name": "cdc_partition_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/406_cdc_partition_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_406_cdc_partition_dml.py",
      "description": "CDC (Change Data Feed) + partitioning + DML: 1. INSERT 120 rows across 3 partitions (US/EU/AP, 40 each) 2. UPDATE SET status = 'closed' WHERE region = 'US' 3. DELETE WHERE region = 'EU' AND id % 5 = 0",
      "status": "pass",
      "duration_ms": 293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:03.338419+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 236,
      "write_warm_ms": 140,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/407_constraint_update",
      "num": 407,
      "name": "constraint_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/407_constraint_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_407_constraint_update.py",
      "description": "CHECK constraint + UPDATE interaction. 1. INSERT 100 rows with scores 0-99 2. ALTER TABLE ADD CONSTRAINT score_check CHECK (score >= 0 AND score <= 100) 3. UPDATE SET score = score - 10 WHERE score >= 50",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:03.574574+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 74,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/408_constraint_delete",
      "num": 408,
      "name": "constraint_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/408_constraint_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_408_constraint_delete.py",
      "description": "CHECK constraint + DELETE interaction. 1. INSERT 100 rows with non-empty names 2. ALTER TABLE ADD CONSTRAINT name_len CHECK (LENGTH(name) > 0) 3. DELETE WHERE id % 3 = 0",
      "status": "pass",
      "duration_ms": 136,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:03.711640+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 115,
      "write_warm_ms": 91,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/409_constraint_merge",
      "num": 409,
      "name": "constraint_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/409_constraint_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_409_constraint_merge.py",
      "description": "CHECK constraint + MERGE interaction. 1. INSERT 80 rows with positive values 2. ALTER TABLE ADD CONSTRAINT val_positive CHECK (value > 0) 3. MERGE from 100-row CTE (80 updates + 20 inserts)",
      "status": "pass",
      "duration_ms": 249,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:03.961836+00:00",
      "read_cold_ms": 101,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 96,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/40_timestamp_ntz_without_timezone",
      "num": 40,
      "name": "timestamp_ntz_without_timezone",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/40_timestamp_ntz_without_timezone.sql",
      "read_script": "generator/spark-reads-iceberg/verify_40_timestamp_ntz_without_timezone.py",
      "description": "Demonstrates timestamps stored without timezone information as INT64 microseconds. Requires readerVersion >= 3, writerVersion >= 7, timestampNtz feature.",
      "status": "pass",
      "duration_ms": 563,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:04.524964+00:00",
      "read_cold_ms": 87,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 215,
      "write_warm_ms": 234,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/410_dv_partition_update",
      "num": 410,
      "name": "dv_partition_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/410_dv_partition_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_410_dv_partition_update.py",
      "description": "Deletion vectors + partitioning + UPDATE + DELETE. 1. INSERT 150 rows across 3 partitions (US, EU, AP) 2. UPDATE amount = amount * 1.1 WHERE region = 'US' 3. DELETE WHERE region = 'EU' AND id % 5 = 0",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:04.745347+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 138,
      "write_warm_ms": 85,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/411_dv_partition_merge",
      "num": 411,
      "name": "dv_partition_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/411_dv_partition_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_411_dv_partition_merge.py",
      "description": "Deletion vectors + partitioning + MERGE. 1. INSERT 150 rows across 3 partitions (US, EU, AP) 2. MERGE from 180-row CTE (150 updates + 30 inserts)",
      "status": "pass",
      "duration_ms": 254,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:05.000336+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 123,
      "write_warm_ms": 164,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/412_delete_reinsert_keys",
      "num": 412,
      "name": "delete_reinsert_keys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/412_delete_reinsert_keys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_412_delete_reinsert_keys.py",
      "description": "DELETE then re-INSERT of the same key values. Tests that deletion vectors correctly handle re-inserted keys that were previously deleted. 1. INSERT 100 rows (id 1-100, version_tag=1) 2. DELETE WHERE id <= 30 3. INSERT 30 rows (id 1-30 again, version_tag=2, different name)",
      "status": "pass",
      "duration_ms": 217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:05.217475+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 214,
      "write_warm_ms": 81,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/413_delete_all_reinsert",
      "num": 413,
      "name": "delete_all_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/413_delete_all_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_413_delete_all_reinsert.py",
      "description": "DELETE all rows then INSERT new data. Tests empty-table-via-DV intermediate state where every row in the table has a deletion vector. 1. INSERT 50 rows (id 1-50) 2. DELETE all rows (WHERE true) 3. INSERT 50 new rows (id 101-150)",
      "status": "pass",
      "duration_ms": 68,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:05.286553+00:00",
      "read_cold_ms": 25,
      "read_warm_ms": 17,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 111,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/414_update_chain",
      "num": 414,
      "name": "update_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/414_update_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_414_update_chain.py",
      "description": "Multiple sequential UPDATEs to the same rows. Tests DV accumulation across chained updates where rows are updated more than once. 1. INSERT 100 rows (all status='new', priority=0) 2. UPDATE SET status='processing' WHERE id <= 50 3. UPDATE SET status='complete' WHERE id <= 25 4...",
      "status": "pass",
      "duration_ms": 260,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:05.547004+00:00",
      "read_cold_ms": 87,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 182,
      "write_warm_ms": 171,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/415_update_all_rows",
      "num": 415,
      "name": "update_all_rows",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/415_update_all_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_415_update_all_rows.py",
      "description": "UPDATE all rows (no WHERE clause). Tests DV behavior when every single row in the table gets a deletion vector. Two consecutive full-table updates stress-test DV file management. 1. INSERT 200 rows 2. UPDATE value = value * 2 (all 200 rows) 3. UPDATE name = CONCAT('v2_', name)...",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:05.789005+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 211,
      "write_warm_ms": 107,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/416_merge_delete_clause",
      "num": 416,
      "name": "merge_delete_clause",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/416_merge_delete_clause.sql",
      "read_script": "generator/spark-reads-iceberg/verify_416_merge_delete_clause.py",
      "description": "MERGE with WHEN MATCHED THEN DELETE clause. Tests that MERGE can selectively delete rows based on a condition while updating others. 1. INSERT 200 rows (all status='active') 2. MERGE from 50-row source (id 1-50): - WHEN MATCHED AND target.score < 10 THEN DELETE - WHEN MATCHED...",
      "status": "pass",
      "duration_ms": 194,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:05.984088+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 236,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/417_merge_insert_only",
      "num": 417,
      "name": "merge_insert_only",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/417_merge_insert_only.sql",
      "read_script": "generator/spark-reads-iceberg/verify_417_merge_insert_only.py",
      "description": "MERGE with only WHEN NOT MATCHED THEN INSERT (no MATCHED clause). 1. INSERT 100 rows (id 1-100) 2. MERGE from CTE of 50 rows (id 101-150) with WHEN NOT MATCHED THEN INSERT",
      "status": "pass",
      "duration_ms": 178,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:06.162756+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 47,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 102,
      "write_warm_ms": 64,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/418_merge_update_only",
      "num": 418,
      "name": "merge_update_only",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/418_merge_update_only.sql",
      "read_script": "generator/spark-reads-iceberg/verify_418_merge_update_only.py",
      "description": "MERGE with only WHEN MATCHED THEN UPDATE (no NOT MATCHED clause). 1. INSERT 100 rows (id 1-100) with tag='original' 2. MERGE from CTE of 100 rows (id 1-100) with WHEN MATCHED THEN UPDATE",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:06.363745+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 178,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/419_optimize_then_delete",
      "num": 419,
      "name": "optimize_then_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/419_optimize_then_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_419_optimize_then_delete.py",
      "description": "OPTIMIZE followed by DELETE. 1. INSERT 500 rows in 5 batches of 100 2. OPTIMIZE to compact data files 3. DELETE WHERE id % 4 = 0 (removes 125 rows)",
      "status": "pass",
      "duration_ms": 118,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:06.482258+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 364,
      "write_warm_ms": 345,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/41_v2_checkpoint_feature_enabled",
      "num": 41,
      "name": "v2_checkpoint_feature_enabled",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/41_v2_checkpoint_feature_enabled.sql",
      "read_script": "generator/spark-reads-iceberg/verify_41_v2_checkpoint_feature_enabled.py",
      "description": "Demonstrates V2 checkpoint with writeStatsAsStruct enabled. V2 checkpoints store statistics as structured data for faster metadata access.",
      "status": "pass",
      "duration_ms": 291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:06.773797+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 32,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 222,
      "write_warm_ms": 197,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/420_optimize_then_update",
      "num": 420,
      "name": "optimize_then_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/420_optimize_then_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_420_optimize_then_update.py",
      "description": "OPTIMIZE followed by UPDATE. 1. INSERT 500 rows in 5 batches of 100 2. OPTIMIZE to compact data files 3. UPDATE SET status='refreshed' WHERE id <= 100 (updates 100 rows)",
      "status": "pass",
      "duration_ms": 306,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:07.080363+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 292,
      "write_warm_ms": 394,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/421_optimize_then_merge",
      "num": 421,
      "name": "optimize_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/421_optimize_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_421_optimize_then_merge.py",
      "description": "OPTIMIZE followed by MERGE. 1. INSERT 300 rows in 3 batches of 100 2. OPTIMIZE to compact data files 3. MERGE from 350-row CTE (300 updates + 50 inserts)",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:07.290210+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 324,
      "write_warm_ms": 259,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/422_dv_cdc_evolve",
      "num": 422,
      "name": "dv_cdc_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/422_dv_cdc_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_422_dv_cdc_evolve.py",
      "description": "Deletion vectors + CDC + schema evolution (three-way interaction): 1. INSERT 100 rows (id 1-100) 2. DELETE WHERE id % 10 = 0 (removes 10 rows) 3. ALTER TABLE ADD COLUMN tag STRING 4. INSERT 30 rows (id 101-130) with tag populated 5. UPDATE SET tag = 'backfill' WHERE id <= 20",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:07.522517+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 276,
      "write_warm_ms": 141,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/423_dv_cdc_partition_merge",
      "num": 423,
      "name": "dv_cdc_partition_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/423_dv_cdc_partition_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_423_dv_cdc_partition_merge.py",
      "description": "Deletion vectors + CDC + partitioning + MERGE (four-way interaction): 1. INSERT 150 rows across 3 partitions (US, EU, AP) 2. MERGE from 180-row CTE (150 updates + 30 inserts)",
      "status": "pass",
      "duration_ms": 291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:07.814626+00:00",
      "read_cold_ms": 92,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 403,
      "write_warm_ms": 520,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/424_null_heavy_dml",
      "num": 424,
      "name": "null_heavy_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/424_null_heavy_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_424_null_heavy_dml.py",
      "description": "NULL-heavy table with DML operations. All nullable columns are NULL throughout the test to verify correct handling of all-NULL columns during UPDATE and DELETE operations with deletion vectors. 1. INSERT 100 rows (nullable_a and nullable_b always NULL) 2. UPDATE SET name =...",
      "status": "pass",
      "duration_ms": 213,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:08.028176+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 216,
      "write_warm_ms": 130,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/425_null_partition_delete",
      "num": 425,
      "name": "null_partition_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/425_null_partition_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_425_null_partition_delete.py",
      "description": "NULL partition values + DELETE targeting the NULL partition. Verifies correct handling of IS NULL predicates on partition columns with deletion vectors enabled. 1. INSERT 90 rows across 3 partitions (A, B, NULL) 2. DELETE WHERE category IS NULL",
      "status": "pass",
      "duration_ms": 121,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:08.149666+00:00",
      "read_cold_ms": 26,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 190,
      "write_warm_ms": 80,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/426_null_in_merge_key",
      "num": 426,
      "name": "null_in_merge_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/426_null_in_merge_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_426_null_in_merge_key.py",
      "description": "MERGE with NULL join keys. Verifies that NULL != NULL semantics are respected in the MERGE ON condition: NULL keys in the source never match NULL keys in the target, resulting in INSERT instead of UPDATE. 1. INSERT 50 rows (10 with rec_id = NULL, 40 with non-NULL rec_id) 2...",
      "status": "pass",
      "duration_ms": 125,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:08.275492+00:00",
      "read_cold_ms": 29,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 168,
      "write_warm_ms": 170,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/427_bigint_boundaries",
      "num": 427,
      "name": "bigint_boundaries",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/427_bigint_boundaries.sql",
      "read_script": "generator/spark-reads-iceberg/verify_427_bigint_boundaries.py",
      "description": "BIGINT boundary values combined with DML operations: 1. INSERT 100 rows (id 1-100) with deterministic formulas 2. INSERT 5 boundary rows using VALUES (min+1, -1, 0, max-1, max) 3. DELETE WHERE id = 0 (removes 1 row) 4. UPDATE SET name='extreme' WHERE id = -9223372036854775807",
      "status": "pass",
      "duration_ms": 201,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:08.476826+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 357,
      "write_warm_ms": 262,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/428_empty_string_values",
      "num": 428,
      "name": "empty_string_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/428_empty_string_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_428_empty_string_values.py",
      "description": "Empty string values vs NULL string values with DML: 1. INSERT 100 rows with a mix of empty strings, NULLs, and normal names 2. UPDATE SET status='blank' WHERE name = '' (updates 20 rows) 3. DELETE WHERE name IS NULL (removes 20 rows)",
      "status": "pass",
      "duration_ms": 236,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:08.713511+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 208,
      "write_warm_ms": 148,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/429_decimal_full_precision_dml",
      "num": 429,
      "name": "decimal_full_precision_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/429_decimal_full_precision_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_429_decimal_full_precision_dml.py",
      "description": "DECIMAL(10,4) precision preserved through UPDATE and DELETE operations: 1. INSERT 100 rows with DECIMAL(10,4) amounts 2. UPDATE amount = amount + 0.0001 WHERE id <= 50 3. DELETE WHERE amount < 1.0",
      "status": "pass",
      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:08.916350+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 140,
      "write_warm_ms": 154,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/42_row_tracking_stable_row_ids",
      "num": 42,
      "name": "row_tracking_stable_row_ids",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/42_row_tracking_stable_row_ids.sql",
      "read_script": "generator/spark-reads-iceberg/verify_42_row_tracking_stable_row_ids.py",
      "description": "Demonstrates row tracking with stable row IDs for each row across versions. Each row maintains a unique, stable identifier across updates and compactions.",
      "status": "pass",
      "duration_ms": 374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:09.290482+00:00",
      "read_cold_ms": 87,
      "read_warm_ms": 121,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 399,
      "write_warm_ms": 591,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:row-tracking",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/430_single_row_dml_cycle",
      "num": 430,
      "name": "single_row_dml_cycle",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/430_single_row_dml_cycle.sql",
      "read_script": "generator/spark-reads-iceberg/verify_430_single_row_dml_cycle.py",
      "description": "Full DML cycle on a single-row table: 1. INSERT 1 row (id=1) 2. UPDATE the row (value and status) 3. DELETE the row 4. INSERT a new row (id=2)",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:09.498002+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 216,
      "write_warm_ms": 235,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/431_multi_version_insert_update",
      "num": 431,
      "name": "multi_version_insert_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/431_multi_version_insert_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_431_multi_version_insert_update.py",
      "description": "15 versions alternating INSERT and UPDATE to test multi-version correctness: - 8 INSERT batches (50 rows each, ids 1-400) - 7 UPDATE operations (set status per batch range) - Total: 15 versions (version 0-14)",
      "status": "pass",
      "duration_ms": 304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:09.802354+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1092,
      "write_warm_ms": 969,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/432_multi_version_mixed_dml",
      "num": 432,
      "name": "multi_version_mixed_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/432_multi_version_mixed_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_432_multi_version_mixed_dml.py",
      "description": "12 versions of mixed DML (INSERT, UPDATE, DELETE, MERGE) to test long DML chains with interleaved operations.",
      "status": "pass",
      "duration_ms": 257,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:10.059630+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 737,
      "write_warm_ms": 717,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/433_constraint_then_evolve",
      "num": 433,
      "name": "constraint_then_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/433_constraint_then_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_433_constraint_then_evolve.py",
      "description": "CHECK constraint + schema evolution + DML: 1. INSERT 80 rows (id 1-80) 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0) 3. ALTER TABLE ADD COLUMN priority INT 4. INSERT 20 rows (id 81-100) with priority populated 5. UPDATE SET priority = 1 WHERE id <= 40 AND score > 50",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:10.319013+00:00",
      "read_cold_ms": 122,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 141,
      "write_warm_ms": 205,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/434_dv_constraint_dml",
      "num": 434,
      "name": "dv_constraint_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/434_dv_constraint_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_434_dv_constraint_dml.py",
      "description": "Deletion vectors + CHECK constraint + DML: 1. INSERT 100 rows (id 1-100) 2. ALTER TABLE ADD CONSTRAINT val_positive CHECK (value > 0) 3. DELETE WHERE id % 4 = 0 (25 rows removed) 4. UPDATE SET tag = 'v2', value = value + 1.0 WHERE id % 3 = 0",
      "status": "pass",
      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:10.522582+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 176,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/435_cdc_multi_version_dml",
      "num": 435,
      "name": "cdc_multi_version_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/435_cdc_multi_version_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_435_cdc_multi_version_dml.py",
      "description": "CDC (Change Data Feed) + 10 versions of mixed DML to test long CDF version history with INSERT, UPDATE, DELETE, and MERGE operations.",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:10.758432+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 713,
      "write_warm_ms": 455,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/436_wide_table_dml",
      "num": 436,
      "name": "wide_table_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/436_wide_table_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_436_wide_table_dml.py",
      "description": "Wide table (15 columns) + DML operations to test correctness with many columns across UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:11.052539+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 89,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/437_partition_optimize_dml",
      "num": 437,
      "name": "partition_optimize_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/437_partition_optimize_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_437_partition_optimize_dml.py",
      "description": "Partition + OPTIMIZE + DML: - INSERT 300 rows in 5 batches across 3 partitions (US, EU, AP) - OPTIMIZE to compact files - DELETE rows where id%5=0 (60 rows removed) - UPDATE rows in US partition to status='post_opt",
      "status": "pass",
      "duration_ms": 312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:11.364942+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 387,
      "write_warm_ms": 715,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/438_dv_cdc_optimize",
      "num": 438,
      "name": "dv_cdc_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/438_dv_cdc_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_438_dv_cdc_optimize.py",
      "description": "DV + CDC + OPTIMIZE interaction: - INSERT 200 rows in 4 batches of 50 each (with CDF enabled) - DELETE rows where id%10=0 (creates deletion vectors) - OPTIMIZE to compact files (should NOT emit CDC rows) - INSERT 20 more rows after OPTIMIZE",
      "status": "pass",
      "duration_ms": 168,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:11.533445+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 383,
      "write_warm_ms": 311,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/439_insert_overwrite_then_dml",
      "num": 439,
      "name": "insert_overwrite_then_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/439_insert_overwrite_then_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_439_insert_overwrite_then_dml.py",
      "description": "INSERT OVERWRITE followed by DML operations: - INSERT OVERWRITE seeds the table with 200 rows - UPDATE first 50 rows to status='processed' - DELETE rows where id>180 (removes 20 rows)",
      "status": "pass",
      "duration_ms": 197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:11.731252+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 171,
      "write_warm_ms": 83,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/43_row_tracking_commit_versions",
      "num": 43,
      "name": "row_tracking_commit_versions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/43_row_tracking_commit_versions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_43_row_tracking_commit_versions.py",
      "description": "Demonstrates row tracking with commit versions showing when each row was last modified. Tracks the Delta version number when each row was created or last updated.",
      "status": "pass",
      "duration_ms": 1354,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:13.085526+00:00",
      "read_cold_ms": 456,
      "read_warm_ms": 423,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 6938,
      "write_warm_ms": 8947,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:row-tracking",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/440_interleaved_evolve_dml",
      "num": 440,
      "name": "interleaved_evolve_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/440_interleaved_evolve_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_440_interleaved_evolve_dml.py",
      "description": "Interleaved ALTER TABLE ADD COLUMN + DML operations: - INSERT 100 rows with 3 columns - ALTER ADD COLUMN cat STRING - UPDATE cat for first 50 rows - ALTER ADD COLUMN priority INT - UPDATE priority for all surviving rows - DELETE rows where id%10=0 - INSERT 20 new rows with all 5...",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:13.354134+00:00",
      "read_cold_ms": 104,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 489,
      "write_warm_ms": 467,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/441_partition_constraint_dml",
      "num": 441,
      "name": "partition_constraint_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/441_partition_constraint_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_441_partition_constraint_dml.py",
      "description": "Partition + CHECK constraint + DML: - INSERT 120 rows across 3 partitions (US, EU, AP) - ALTER TABLE ADD CONSTRAINT score_range CHECK (score >= 0 AND score <= 100) - UPDATE US rows (score = score - 5 WHERE score >= 5) - DELETE EU rows where score < 20",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:13.586629+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 146,
      "write_warm_ms": 179,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/442_cdc_colmap_merge",
      "num": 442,
      "name": "cdc_colmap_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/442_cdc_colmap_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_442_cdc_colmap_merge.py",
      "description": "CDC + column mapping (name mode) + MERGE. Three-way combo never tested. - INSERT 100 rows - MERGE from 120-row CTE (id 1-120): - MATCHED (id 1-100): UPDATE SET price=source.price, status='merged' - NOT MATCHED (id 101-120): INSERT all columns",
      "status": "pass",
      "duration_ms": 188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:13.775150+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 269,
      "write_warm_ms": 347,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/443_cdc_constraint_dml",
      "num": 443,
      "name": "cdc_constraint_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/443_cdc_constraint_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_443_cdc_constraint_dml.py",
      "description": "CDC + CHECK constraint + DML. Never combined before. - INSERT 100 rows - ALTER TABLE ADD CONSTRAINT score_valid CHECK (score >= 0 AND score <= 100) - UPDATE SET score = score - 5 WHERE score >= 50 - DELETE WHERE score < 5",
      "status": "pass",
      "duration_ms": 228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:14.003933+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 145,
      "write_warm_ms": 160,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/444_dv_cdc_constraint",
      "num": 444,
      "name": "dv_cdc_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/444_dv_cdc_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_444_dv_cdc_constraint.py",
      "description": "DV + CDC + CHECK constraint. Three-way combo never tested. - INSERT 100 rows - ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) - DELETE WHERE id % 4 = 0 (25 rows removed) - UPDATE SET tag = 'checked' WHERE id % 3 = 0 (of surviving rows)",
      "status": "pass",
      "duration_ms": 242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:14.246099+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 148,
      "write_warm_ms": 261,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/445_colmap_partition_dml",
      "num": 445,
      "name": "colmap_partition_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/445_colmap_partition_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_445_colmap_partition_dml.py",
      "description": "Column mapping (name mode) + partitioning + DML. Never combined. - INSERT 150 rows across 3 partitions (US, EU, AP) - UPDATE amount * 1.5 for US partition - DELETE EU rows where id % 5 = 0",
      "status": "pass",
      "duration_ms": 496,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:14.742606+00:00",
      "read_cold_ms": 92,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 230,
      "write_warm_ms": 222,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/446_optimize_schema_evolve",
      "num": 446,
      "name": "optimize_schema_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/446_optimize_schema_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_446_optimize_schema_evolve.py",
      "description": "OPTIMIZE + schema evolution (ALTER TABLE ADD COLUMN). Never combined in SQL. - INSERT 300 rows in 3 batches of 100 - OPTIMIZE (compacts the 3 data files) - ALTER TABLE ADD COLUMN extra STRING - INSERT 50 rows (id 301-350) with extra column - UPDATE SET extra = 'backfill' WHERE...",
      "status": "pass",
      "duration_ms": 246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:14.988702+00:00",
      "read_cold_ms": 91,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 312,
      "write_warm_ms": 331,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/447_optimize_cdc",
      "num": 447,
      "name": "optimize_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/447_optimize_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_447_optimize_cdc.py",
      "description": "OPTIMIZE + CDC in SQL. Tests that OPTIMIZE produces no CDF rows. - INSERT 300 rows in 6 batches of 50 each (creates 6 small files) - UPDATE first 50 rows - OPTIMIZE (should NOT emit CDF rows) - INSERT 50 more rows",
      "status": "pass",
      "duration_ms": 123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:15.112037+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 40,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 463,
      "write_warm_ms": 580,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/448_merge_large_scale",
      "num": 448,
      "name": "merge_large_scale",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/448_merge_large_scale.sql",
      "read_script": "generator/spark-reads-iceberg/verify_448_merge_large_scale.py",
      "description": "Large-scale MERGE (1000 rows + 1200-row MERGE). Tests MERGE at scale in SQL. - INSERT 1000 rows - MERGE from 1200-row CTE (id 1-1200): - MATCHED (id 1-1000): UPDATE SET score = score + 1000, status = 'updated' - NOT MATCHED (id 1001-1200): INSERT all columns",
      "status": "pass",
      "duration_ms": 233,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:15.345569+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 179,
      "write_warm_ms": 141,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/449_delete_compound_predicate",
      "num": 449,
      "name": "delete_compound_predicate",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/449_delete_compound_predicate.sql",
      "read_script": "generator/spark-reads-iceberg/verify_449_delete_compound_predicate.py",
      "description": "DELETE with compound AND/OR predicates. Tests complex predicate pushdown. - INSERT 500 rows - DELETE WHERE (category='A' AND score<20) OR (category='B' AND NOT active) OR (id>480)",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:15.586956+00:00",
      "read_cold_ms": 54,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 113,
      "write_warm_ms": 85,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/44_vacuum_protocol_check_enabled",
      "num": 44,
      "name": "vacuum_protocol_check_enabled",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/44_vacuum_protocol_check_enabled.sql",
      "read_script": "generator/spark-reads-iceberg/verify_44_vacuum_protocol_check_enabled.py",
      "description": "Demonstrates table with deletion vectors that creates multiple obsolete files. This tests vacuum behavior and file retention for readers using older protocol versions.",
      "status": "pass",
      "duration_ms": 944,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:16.531956+00:00",
      "read_cold_ms": 120,
      "read_warm_ms": 142,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 596,
      "write_warm_ms": 581,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "delta:vacuum",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/450_update_case_expression",
      "num": 450,
      "name": "update_case_expression",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/450_update_case_expression.sql",
      "read_script": "generator/spark-reads-iceberg/verify_450_update_case_expression.py",
      "description": "UPDATE with CASE expression in SET clause. Tests complex UPDATE expressions. - INSERT 200 rows - UPDATE SET grade = CASE WHEN score>=90 THEN 'A' ... END (all rows) - UPDATE SET status = CASE WHEN grade IN ('A','B') THEN 'honor' ... END (all rows)",
      "status": "pass",
      "duration_ms": 289,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:16.821831+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 241,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/451_merge_self_dedup",
      "num": 451,
      "name": "merge_self_dedup",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/451_merge_self_dedup.sql",
      "read_script": "generator/spark-reads-iceberg/verify_451_merge_self_dedup.py",
      "description": "MERGE where source has recomputed values, updating only rows where the source value exceeds the target value. Tests conditional MERGE with a WHEN MATCHED AND <condition> clause. - INSERT 100 rows with target formula values - MERGE from 100-row CTE with source formula values: -...",
      "status": "pass",
      "duration_ms": 213,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:17.035823+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 87,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/452_timestamp_dml",
      "num": 452,
      "name": "timestamp_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/452_timestamp_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_452_timestamp_dml.py",
      "description": "TIMESTAMP columns with DML operations: - INSERT 100 rows with TIMESTAMP column using arrow_cast microsecond encoding - UPDATE rows with timestamp predicate (event_ts < threshold) - DELETE rows with timestamp predicate (event_ts > threshold)",
      "status": "pass",
      "duration_ms": 208,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:17.244071+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 75,
      "write_warm_ms": 164,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/453_boolean_dml",
      "num": 453,
      "name": "boolean_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/453_boolean_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_453_boolean_dml.py",
      "description": "BOOLEAN columns as DML predicates and update values: - INSERT 200 rows with two boolean columns (active, verified) - UPDATE verified=true WHERE active=true AND score>50 - DELETE WHERE active=false AND verified=false AND score<20",
      "status": "pass",
      "duration_ms": 198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:17.442490+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 173,
      "write_warm_ms": 132,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/454_struct_dml",
      "num": 454,
      "name": "struct_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/454_struct_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_454_struct_dml.py",
      "description": "Nested STRUCT columns with DML operations: - INSERT 100 rows with a STRUCT<age: INT, city: STRING> column - UPDATE non-struct column (value) for id<=30 - DELETE rows where id%7=0 - Verifies struct values are preserved through UPDATE/DELETE operations",
      "status": "pass",
      "duration_ms": 390,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:17.832771+00:00",
      "read_cold_ms": 93,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 206,
      "write_warm_ms": 106,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/455_array_dml",
      "num": 455,
      "name": "array_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/455_array_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_455_array_dml.py",
      "description": "String tags column (simulating array-like data) with DML operations: - INSERT 100 rows with a tags STRING column containing comma-separated values - UPDATE tags='gold' WHERE score>80 - DELETE WHERE tags='green' AND score<20 - Tests tag-based filtering and update through DML",
      "status": "pass",
      "duration_ms": 252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:18.085694+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 115,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/456_multi_table_merge",
      "num": 456,
      "name": "multi_table_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/456_multi_table_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_456_multi_table_merge.py",
      "description": "MERGE with conditional update logic: - INSERT 200 rows with category column - MERGE from a 250-row CTE source - WHEN MATCHED AND target.category != 'D' THEN UPDATE (skip category D rows) - WHEN NOT MATCHED THEN INSERT - Tests selective MERGE with predicate-guarded updates",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:18.286270+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 65,
      "write_warm_ms": 60,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/457_dv_no_dv_comparison",
      "num": 457,
      "name": "dv_no_dv_comparison",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/457_dv_no_dv_comparison.sql",
      "read_script": "generator/spark-reads-iceberg/verify_457_dv_no_dv_comparison.py",
      "description": "Table WITHOUT deletion vectors (full file rewrites for DML): - INSERT 100 rows - UPDATE first 30 rows (full rewrite, no DVs) - DELETE last 20 rows (full rewrite, no DVs) - Verifies non-DV writes produce correct results - All other tests in this series use...",
      "status": "pass",
      "duration_ms": 92,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:18.379517+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 15,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 174,
      "write_warm_ms": 89,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/458_partition_multi_col",
      "num": 458,
      "name": "partition_multi_col",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/458_partition_multi_col.sql",
      "read_script": "generator/spark-reads-iceberg/verify_458_partition_multi_col.py",
      "description": "Multi-column partitioning with DML operations: - PARTITIONED BY (region, year) -- two partition columns - INSERT 120 rows across 6 partition combinations (3 regions x 2 years) - UPDATE rows in one specific partition (region='US', year=2024) - DELETE rows in another partition...",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:18.610180+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 104,
      "write_warm_ms": 157,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/459_insert_overwrite_partition",
      "num": 459,
      "name": "insert_overwrite_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/459_insert_overwrite_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_459_insert_overwrite_partition.py",
      "description": "INSERT OVERWRITE replacing entire table contents, followed by DML: - INSERT 90 rows across 3 categories - INSERT OVERWRITE with 50 new rows (replaces entire table) - UPDATE first 20 of the new rows - Verifies INSERT OVERWRITE + subsequent DML correctness",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:18.841337+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 589,
      "write_warm_ms": 255,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/45_variant_data_type_basic",
      "num": 45,
      "name": "variant_data_type_basic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/45_variant_data_type_basic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_45_variant_data_type_basic.py",
      "description": "Demonstrates variant data type for semi-structured JSON data. Efficiently stores and queries nested JSON without fixed schema.",
      "status": "pass",
      "duration_ms": 2478,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:21.319566+00:00",
      "read_cold_ms": 45,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 431,
      "write_warm_ms": 348,
      "tags": [
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/460_delete_then_optimize",
      "num": 460,
      "name": "delete_then_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/460_delete_then_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_460_delete_then_optimize.py",
      "description": "DELETE creating deletion vectors, then OPTIMIZE to materialize them: - INSERT 1000 rows in 10 batches of 100 (creates 10 data files) - DELETE rows where id%3=0 (creates DVs on each data file) - OPTIMIZE compacts files and materializes DVs into clean data files - Verifies...",
      "status": "pass",
      "duration_ms": 91,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:21.410993+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 12,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 935,
      "write_warm_ms": 991,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/461_update_then_optimize",
      "num": 461,
      "name": "update_then_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/461_update_then_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_461_update_then_optimize.py",
      "description": "UPDATE creating deletion vectors, then OPTIMIZE to compact: - INSERT 500 rows in 5 batches of 100 (creates 5 data files) - UPDATE first 200 rows (creates DVs + new data files for updated rows) - OPTIMIZE compacts all files into fewer, clean files - Verifies correct values after...",
      "status": "pass",
      "duration_ms": 79,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:21.491008+00:00",
      "read_cold_ms": 19,
      "read_warm_ms": 15,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 659,
      "write_warm_ms": 807,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/462_constraint_drop_insert",
      "num": 462,
      "name": "constraint_drop_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/462_constraint_drop_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_462_constraint_drop_insert.py",
      "description": "Constraint lifecycle: ADD CONSTRAINT then DROP CONSTRAINT then insert violating data. 1. INSERT 50 rows with score = (i*53)%100 (range [0, 99]) 2. ALTER TABLE ADD CONSTRAINT score_bound CHECK (score >= 0 AND score <= 100) 3. INSERT 30 more valid rows (id 51-80) 4. ALTER TABLE...",
      "status": "pass",
      "duration_ms": 105,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:21.596737+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 491,
      "write_warm_ms": 373,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/463_evolve_rename_dml",
      "num": 463,
      "name": "evolve_rename_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/463_evolve_rename_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_463_evolve_rename_dml.py",
      "description": "RENAME COLUMN then DML using the new column name. Tests column mapping after rename. 1. INSERT 100 rows with old_name column 2. ALTER TABLE RENAME COLUMN old_name TO display_name 3. UPDATE SET display_name = CONCAT('renamed_', CAST(id AS STRING)) WHERE id <= 50 4. DELETE WHERE...",
      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:21.866062+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 265,
      "write_warm_ms": 229,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/464_evolve_multi_add",
      "num": 464,
      "name": "evolve_multi_add",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/464_evolve_multi_add.sql",
      "read_script": "generator/spark-reads-iceberg/verify_464_evolve_multi_add.py",
      "description": "Multiple ALTER ADD COLUMN operations interleaved with INSERTs. Tests multi-step schema growth from 2 columns to 6 columns. 1. INSERT 50 rows (2 cols: id, name) 2. ALTER ADD COLUMN val1 INT 3. INSERT 30 rows (id 51-80) with val1 4. ALTER ADD COLUMN val2 DOUBLE 5. INSERT 20 rows...",
      "status": "pass",
      "duration_ms": 139,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:22.006163+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 357,
      "write_warm_ms": 413,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/465_partition_five_regions",
      "num": 465,
      "name": "partition_five_regions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/465_partition_five_regions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_465_partition_five_regions.py",
      "description": "5-region partition + DML across all partitions. 1. INSERT 250 rows across 5 partitions (NA, EU, AP, SA, AF -- 50 each) 2. UPDATE SET value = value * 2 WHERE region = 'NA' OR region = 'SA' 3. DELETE WHERE region = 'AF' AND id % 5 = 0",
      "status": "pass",
      "duration_ms": 332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:22.339208+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 200,
      "write_warm_ms": 221,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/466_dv_cdc_partition_evolve",
      "num": 466,
      "name": "dv_cdc_partition_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/466_dv_cdc_partition_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_466_dv_cdc_partition_evolve.py",
      "description": "Four-way combo: DV + CDC + partition + schema evolution. Most complex combo. 1. INSERT 120 rows across 3 regions (US, EU, AP -- 40 each) 2. DELETE WHERE id % 10 = 0 (12 rows removed) 3. ALTER TABLE ADD COLUMN priority INT 4. INSERT 30 rows (id 121-150) with priority 5. UPDATE...",
      "status": "pass",
      "duration_ms": 174,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:22.513778+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 493,
      "write_warm_ms": 342,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/467_merge_three_clause",
      "num": 467,
      "name": "merge_three_clause",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/467_merge_three_clause.sql",
      "read_script": "generator/spark-reads-iceberg/verify_467_merge_three_clause.py",
      "description": "MERGE with all 3 clause types: MATCHED UPDATE, MATCHED DELETE, NOT MATCHED INSERT. 1. INSERT 200 rows with score = (i*53)%100, status = 'active' 2. MERGE from 250-row CTE: WHEN MATCHED AND target.score < 10 THEN DELETE WHEN MATCHED THEN UPDATE SET status = 'verified', score =...",
      "status": "pass",
      "duration_ms": 298,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:22.812698+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 126,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 211,
      "write_warm_ms": 276,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/468_cdc_dv_merge_partition",
      "num": 468,
      "name": "cdc_dv_merge_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/468_cdc_dv_merge_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_468_cdc_dv_merge_partition.py",
      "description": "Four-way: CDC + DV + MERGE + partition. 1. INSERT 200 rows across 4 partitions (W, X, Y, Z -- 50 each) 2. MERGE from 250-row CTE: WHEN MATCHED THEN UPDATE; WHEN NOT MATCHED THEN INSERT",
      "status": "pass",
      "duration_ms": 299,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:23.112512+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 708,
      "write_warm_ms": 984,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/469_constraint_not_null",
      "num": 469,
      "name": "constraint_not_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/469_constraint_not_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_469_constraint_not_null.py",
      "description": "NOT NULL constraint + DML. Tests NOT NULL enforcement during UPDATE. 1. INSERT 100 rows with all non-null values 2. UPDATE SET score = NULL WHERE id <= 20 (score is nullable, should work) 3. DELETE WHERE id % 4 = 0",
      "status": "pass",
      "duration_ms": 298,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:23.411428+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 126,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 235,
      "write_warm_ms": 150,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/46_variant_parquet_shredding",
      "num": 46,
      "name": "variant_parquet_shredding",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/46_variant_parquet_shredding.sql",
      "read_script": "generator/spark-reads-iceberg/verify_46_variant_parquet_shredding.py",
      "description": "Demonstrates Variant data shredding in Parquet format. Optimizes Variant storage by extracting common fields into typed columns.",
      "status": "pass",
      "duration_ms": 851,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:24.263195+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 278,
      "write_warm_ms": 297,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/470_many_small_inserts",
      "num": 470,
      "name": "many_small_inserts",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/470_many_small_inserts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_470_many_small_inserts.py",
      "description": "Many small INSERT batches (20 batches of 10 rows). Tests fragmented table then DML. 1. 20 INSERT batches: batch 1 = ids 1-10, batch 2 = ids 11-20, ..., batch 20 = ids 191-200 2. DELETE WHERE batch_num <= 5 (removes 50 rows) 3. UPDATE SET payload = CONCAT('updated_', payload)...",
      "status": "pass",
      "duration_ms": 361,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:24.625108+00:00",
      "read_cold_ms": 155,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2288,
      "write_warm_ms": 1221,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/471_optimize_partition",
      "num": 471,
      "name": "optimize_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/471_optimize_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_471_optimize_partition.py",
      "description": "OPTIMIZE on partitioned table then DML. Tests per-partition compaction. 1. INSERT 150 rows in 3 batches of 50 (creates 3 data files per partition) 2. OPTIMIZE to compact data files 3. DELETE WHERE id % 6 = 0 (25 rows removed) 4. UPDATE SET value = 0 WHERE region = 'US' AND id %...",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:24.838011+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 487,
      "write_warm_ms": 336,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/472_cdc_delete_update_merge",
      "num": 472,
      "name": "cdc_delete_update_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/472_cdc_delete_update_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_472_cdc_delete_update_merge.py",
      "description": "CDC with all 3 DML types (DELETE, UPDATE, MERGE) in one script. Tests that CDF captures all operation types. 1. INSERT 200 rows 2. DELETE WHERE id % 10 = 0 (removes 20 rows) 3. UPDATE SET status = 'modified' WHERE id <= 50 (updates surviving rows with id <= 50) 4. MERGE from...",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:25.079895+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 309,
      "write_warm_ms": 204,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/473_colmap_rename_merge",
      "num": 473,
      "name": "colmap_rename_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/473_colmap_rename_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_473_colmap_rename_merge.py",
      "description": "Column mapping + RENAME COLUMN + MERGE using the renamed column. Tests that MERGE works correctly after a column rename. 1. INSERT 100 rows with old_val column 2. ALTER TABLE RENAME COLUMN old_val TO display_val 3. MERGE from 120-row CTE: WHEN MATCHED UPDATE SET display_val...",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:25.287147+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 83,
      "write_warm_ms": 183,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/474_partition_evolve_merge",
      "num": 474,
      "name": "partition_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/474_partition_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_474_partition_evolve_merge.py",
      "description": "Partition + schema evolution (ADD COLUMN) + MERGE. Three-way combination. 1. INSERT 120 rows across 3 regions 2. ALTER TABLE ADD COLUMN priority INT 3. MERGE from 150-row CTE with priority set: WHEN MATCHED UPDATE SET priority; WHEN NOT MATCHED INSERT",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:25.517974+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 85,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/475_dv_cdc_delete_reinsert",
      "num": 475,
      "name": "dv_cdc_delete_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/475_dv_cdc_delete_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_475_dv_cdc_delete_reinsert.py",
      "description": "DV + CDC + delete-then-reinsert pattern. Tests CDF for delete+insert of same keys. 1. INSERT 100 rows (version_tag = 1) 2. DELETE WHERE id <= 20 (removes 20 rows) 3. INSERT 20 rows (id 1-20, version_tag = 2)",
      "status": "pass",
      "duration_ms": 185,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:25.703939+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 63,
      "write_warm_ms": 83,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/476_constraint_evolve_merge",
      "num": 476,
      "name": "constraint_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/476_constraint_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_476_constraint_evolve_merge.py",
      "description": "Constraint + schema evolution (ADD COLUMN) + MERGE. Three-way combination. 1. INSERT 80 rows with score = (i * 53) % 100 2. ALTER TABLE ADD CONSTRAINT score_valid CHECK (score >= 0 AND score <= 100) 3. ALTER TABLE ADD COLUMN tag STRING 4. MERGE from 100-row CTE with tag and...",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:25.913097+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 112,
      "write_warm_ms": 78,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/477_partition_constraint_merge",
      "num": 477,
      "name": "partition_constraint_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/477_partition_constraint_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_477_partition_constraint_merge.py",
      "description": "Partition + constraint + MERGE. Three-way combination. 1. INSERT 120 rows across 3 regions 2. ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) 3. MERGE from 150-row CTE (all value > 0): WHEN MATCHED UPDATE value; WHEN NOT MATCHED INSERT",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:26.145054+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 153,
      "write_warm_ms": 87,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/478_colmap_cdc_evolve",
      "num": 478,
      "name": "colmap_cdc_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/478_colmap_cdc_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_478_colmap_cdc_evolve.py",
      "description": "Column mapping + CDC + schema evolution (ADD COLUMN). Three-way combination. 1. INSERT 100 rows 2. ALTER TABLE ADD COLUMN extra STRING 3. INSERT 50 rows (id 101-150) with extra populated 4. UPDATE SET extra = 'filled' WHERE id <= 30",
      "status": "pass",
      "duration_ms": 175,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:26.320810+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 82,
      "write_warm_ms": 193,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/479_delete_between_range",
      "num": 479,
      "name": "delete_between_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/479_delete_between_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_479_delete_between_range.py",
      "description": "DELETE with BETWEEN range predicate. Tests range-based deletion. 1. INSERT 500 rows 2. DELETE WHERE id BETWEEN 100 AND 200 (removes 101 rows) 3. UPDATE SET category = 'remaining' WHERE id BETWEEN 201 AND 300",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:26.527945+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 76,
      "write_warm_ms": 50,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/47_variant_combined_with_features",
      "num": 47,
      "name": "variant_combined_with_features",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/47_variant_combined_with_features.sql",
      "read_script": "generator/spark-reads-iceberg/verify_47_variant_combined_with_features.py",
      "description": "Demonstrates Variant data combined with other Delta features like CDC. Shows Variant type working alongside Change Data Feed, deletion vectors, etc.",
      "status": "pass",
      "duration_ms": 2135,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:28.663412+00:00",
      "read_cold_ms": 121,
      "read_warm_ms": 96,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2746,
      "write_warm_ms": 2823,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/480_update_string_ops",
      "num": 480,
      "name": "update_string_ops",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/480_update_string_ops.sql",
      "read_script": "generator/spark-reads-iceberg/verify_480_update_string_ops.py",
      "description": "UPDATE with string operations (CONCAT). Tests string manipulation in SET clauses. 1. INSERT 100 rows with first_name, last_name, empty full_name and email 2. UPDATE SET full_name = CONCAT(first_name, '_', last_name) 3. UPDATE SET email = CONCAT(first_name, '.', last_name...",
      "status": "pass",
      "duration_ms": 219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:28.882823+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 219,
      "write_warm_ms": 118,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/481_merge_selective_update",
      "num": 481,
      "name": "merge_selective_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/481_merge_selective_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_481_merge_selective_update.py",
      "description": "MERGE where only some matched rows are updated (conditional WHEN MATCHED). No WHEN NOT MATCHED clause -- source fully overlaps target. 1. INSERT 200 rows: score = (i * 53) % 100, tier = 'bronze' 2. MERGE from 200-row CTE: WHEN MATCHED AND target.score >= 80 THEN UPDATE SET tier...",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:29.092591+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 105,
      "write_warm_ms": 96,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/482_dv_multi_delete",
      "num": 482,
      "name": "dv_multi_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/482_dv_multi_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_482_dv_multi_delete.py",
      "description": "Multiple DELETE operations creating accumulated deletion vectors. Tests DV stacking correctness across successive deletes. 1. INSERT 500 rows with round_num=0 2. DELETE WHERE id%2=0 (250 removed, round 1) 3. DELETE WHERE id%3=0 (of remaining 250 odd ids, removes ~83, round 2) 4...",
      "status": "pass",
      "duration_ms": 107,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:29.200537+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 144,
      "write_warm_ms": 265,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/483_update_arithmetic",
      "num": 483,
      "name": "update_arithmetic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/483_update_arithmetic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_483_update_arithmetic.py",
      "description": "UPDATE with arithmetic expressions. Tests complex SET expressions computing multiple derived columns in a single UPDATE. 1. INSERT 100 rows with a, b populated, computed columns zeroed 2. UPDATE SET sum_ab=a+b, product_ab=a*b, ratio=ROUND(a/b, 4)",
      "status": "pass",
      "duration_ms": 315,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:29.516254+00:00",
      "read_cold_ms": 102,
      "read_warm_ms": 119,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 113,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/484_partition_null_update",
      "num": 484,
      "name": "partition_null_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/484_partition_null_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_484_partition_null_update.py",
      "description": "Partitioned table with NULL partition values + UPDATE targeting the NULL partition. Tests that DV-based UPDATE correctly handles NULL partition keys. 1. INSERT 120 rows across 4 partitions (US, EU, AP, NULL) 2. UPDATE SET value=value*3 WHERE region IS NULL (30 rows) 3. DELETE...",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:29.769637+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 91,
      "write_warm_ms": 142,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/485_cdc_dv_update_chain",
      "num": 485,
      "name": "cdc_dv_update_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/485_cdc_dv_update_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_485_cdc_dv_update_chain.py",
      "description": "CDC + DV + chained UPDATEs. Tests that CDF captures each UPDATE version and that successive DV-based updates on overlapping row sets work correctly. 1. INSERT 100 rows: counter=0, status='v0' 2. UPDATE counter+1, status='v1' WHERE id<=50 3. UPDATE counter+1, status='v2' WHERE...",
      "status": "pass",
      "duration_ms": 290,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:30.059766+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 115,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 89,
      "write_warm_ms": 95,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/486_colmap_dv_optimize",
      "num": 486,
      "name": "colmap_dv_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/486_colmap_dv_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_486_colmap_dv_optimize.py",
      "description": "Column mapping + DV + OPTIMIZE. Tests that OPTIMIZE correctly compacts files on a column-mapped table after DV-based deletes. 1. INSERT 100 rows (batch 1, id 1-100) 2. INSERT 100 rows (batch 2, id 101-200) 3. INSERT 100 rows (batch 3, id 201-300) 4. DELETE WHERE id%5=0 (60 rows...",
      "status": "pass",
      "duration_ms": 149,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:30.209237+00:00",
      "read_cold_ms": 45,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 131,
      "write_warm_ms": 159,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/487_evolve_constraint_delete",
      "num": 487,
      "name": "evolve_constraint_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/487_evolve_constraint_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_487_evolve_constraint_delete.py",
      "description": "Schema evolution + constraint + DELETE. Three-way feature interaction. 1. INSERT 100 rows 2. ALTER TABLE ADD COLUMN priority INT 3. ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) 4. UPDATE SET priority = id % 5 5. DELETE WHERE priority=0 AND value<10.0",
      "status": "pass",
      "duration_ms": 324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:30.533313+00:00",
      "read_cold_ms": 87,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 69,
      "write_warm_ms": 99,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/488_merge_partition_evolve",
      "num": 488,
      "name": "merge_partition_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/488_merge_partition_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_488_merge_partition_evolve.py",
      "description": "MERGE + partition + schema evolution. Three-way feature interaction. 1. INSERT 120 rows across 3 regions 2. ALTER TABLE ADD COLUMN flag BOOLEAN 3. MERGE from 150-row CTE: MATCHED->UPDATE flag=true, NOT MATCHED->INSERT with flag=false",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:30.763576+00:00",
      "read_cold_ms": 85,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 325,
      "write_warm_ms": 154,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/489_dv_cdc_colmap",
      "num": 489,
      "name": "dv_cdc_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/489_dv_cdc_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_489_dv_cdc_colmap.py",
      "description": "DV + CDC + column mapping. Three-way feature interaction. Tests that deletion vectors, change data feed, and column mapping all work correctly together. 1. INSERT 200 rows 2. DELETE WHERE id%8=0 (25 rows removed via DVs) 3. UPDATE SET amount=amount+100 WHERE active=true",
      "status": "pass",
      "duration_ms": 180,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:30.944056+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 109,
      "write_warm_ms": 106,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/48_writer_atomic_log_entry_creation",
      "num": 48,
      "name": "writer_atomic_log_entry_creation",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/48_writer_atomic_log_entry_creation.sql",
      "read_script": "generator/spark-reads-iceberg/verify_48_writer_atomic_log_entry_creation.py",
      "description": "Demonstrates atomic creation of transaction log entries. Writers MUST never overwrite an existing log entry and should use atomic primitives of the underlying filesystem to ensure concurrent writers do not overwrite each other's entries.",
      "status": "pass",
      "duration_ms": 1849,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:32.793621+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 97,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 599,
      "write_warm_ms": 460,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/490_optimize_constraint",
      "num": 490,
      "name": "optimize_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/490_optimize_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_490_optimize_constraint.py",
      "description": "OPTIMIZE + constraint interaction. Tests that constraint metadata survives OPTIMIZE file compaction. 1. INSERT 200 rows in 4 batches of 50 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0) 3. DELETE WHERE id%6=0 (~33 rows removed) 4. OPTIMIZE 5. INSERT 30 rows (id...",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:32.954144+00:00",
      "read_cold_ms": 52,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 502,
      "write_warm_ms": 576,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/491_merge_cdc_constraint",
      "num": 491,
      "name": "merge_cdc_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/491_merge_cdc_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_491_merge_cdc_constraint.py",
      "description": "MERGE + CDC + constraint. Three-way feature interaction. Tests that MERGE respects CHECK constraint and CDF captures merge changes. 1. INSERT 80 rows 2. ALTER TABLE ADD CONSTRAINT val_pos CHECK (value > 0) 3. MERGE from 100-row CTE: MATCHED->UPDATE value+tag, NOT MATCHED->INSERT",
      "status": "pass",
      "duration_ms": 217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:33.171621+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 389,
      "write_warm_ms": 310,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/492_five_way_combo",
      "num": 492,
      "name": "five_way_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/492_five_way_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_492_five_way_combo.py",
      "description": "Five-way stress test: DV + CDC + partition + constraint + schema evolution. Exercises every major Delta feature in a single table lifecycle. 1. INSERT 150 rows across 3 regions (US, EU, AP -- 50 each) 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0 AND score <= 100) 3...",
      "status": "pass",
      "duration_ms": 185,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:33.357029+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 99,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 220,
      "write_warm_ms": 327,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/493_dv_cdc_optimize_dml",
      "num": 493,
      "name": "dv_cdc_optimize_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/493_dv_cdc_optimize_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_493_dv_cdc_optimize_dml.py",
      "description": "DV + CDC + OPTIMIZE + post-OPTIMIZE DML. Tests the full lifecycle: batched inserts, deletion vectors, OPTIMIZE compaction, then further DML. 1. INSERT 200 rows in 4 batches of 50 2. DELETE WHERE id % 5 = 0 (40 rows removed via DVs) 3. OPTIMIZE (materializes DVs, should not emit...",
      "status": "pass",
      "duration_ms": 181,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:33.538736+00:00",
      "read_cold_ms": 108,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 553,
      "write_warm_ms": 294,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/494_colmap_constraint_evolve",
      "num": 494,
      "name": "colmap_constraint_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/494_colmap_constraint_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_494_colmap_constraint_evolve.py",
      "description": "Three-way combo: column mapping (name mode) + constraint + schema evolution. Validates that column mapping interacts correctly with CHECK constraints and ADD COLUMN operations. 1. INSERT 100 rows 2. ALTER TABLE ADD CONSTRAINT score_ok CHECK (score >= 0) 3. ALTER TABLE ADD COLUMN...",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:33.769992+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 151,
      "write_warm_ms": 159,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/495_partition_optimize_merge",
      "num": 495,
      "name": "partition_optimize_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/495_partition_optimize_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_495_partition_optimize_merge.py",
      "description": "Three-way combo: partition + OPTIMIZE + MERGE. Tests that OPTIMIZE on a partitioned table produces correct file layout and that MERGE works correctly against optimized partitioned data. 1. INSERT 150 rows in 3 batches of 50 (3 regions) 2. OPTIMIZE 3. MERGE from 180-row CTE: WHEN...",
      "status": "pass",
      "duration_ms": 229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:33.999414+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 247,
      "write_warm_ms": 473,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/496_evolve_dv_cdc_merge",
      "num": 496,
      "name": "evolve_dv_cdc_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/496_evolve_dv_cdc_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_496_evolve_dv_cdc_merge.py",
      "description": "Four-way combo: schema evolution + DV + CDC + MERGE. Tests that MERGE works correctly after schema evolution with deletion vectors and CDC enabled. 1. INSERT 100 rows 2. ALTER TABLE ADD COLUMN priority INT 3. MERGE from 120-row CTE with priority: WHEN MATCHED UPDATE; WHEN NOT...",
      "status": "pass",
      "duration_ms": 151,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:34.151131+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 462,
      "write_warm_ms": 552,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/497_large_insert_delete",
      "num": 497,
      "name": "large_insert_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/497_large_insert_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_497_large_insert_delete.py",
      "description": "Large-scale test: 5000-row INSERT followed by bulk DELETE and UPDATE. Exercises deletion vectors and DML at scale to stress file management. 1. INSERT 5000 rows in 5 batches of 1000 2. DELETE WHERE id % 3 = 0 (~1667 rows removed) 3. UPDATE SET bucket = bucket + 10 WHERE id % 2 =...",
      "status": "pass",
      "duration_ms": 379,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:34.530476+00:00",
      "read_cold_ms": 91,
      "read_warm_ms": 102,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 207,
      "write_warm_ms": 354,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/498_merge_delete_insert_combo",
      "num": 498,
      "name": "merge_delete_insert_combo",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/498_merge_delete_insert_combo.sql",
      "read_script": "generator/spark-reads-iceberg/verify_498_merge_delete_insert_combo.py",
      "description": "MERGE with DELETE clause + separate INSERT after. Tests the interaction between MERGE's WHEN MATCHED THEN DELETE clause and subsequent INSERT. 1. INSERT 200 rows 2. MERGE from 100-row CTE (id 1-100): WHEN MATCHED AND score<10 THEN DELETE; WHEN MATCHED THEN UPDATE...",
      "status": "pass",
      "duration_ms": 185,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:34.716218+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 84,
      "write_warm_ms": 101,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/499_colmap_partition_merge_evolve",
      "num": 499,
      "name": "colmap_partition_merge_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/499_colmap_partition_merge_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_499_colmap_partition_merge_evolve.py",
      "description": "Four-way combo: column mapping + partition + MERGE + schema evolution. Validates that column mapping interacts correctly with partitioning, MERGE operations, and ADD COLUMN after initial data load. 1. INSERT 120 rows (3 regions, 40 each) 2. ALTER TABLE ADD COLUMN flag BOOLEAN 3...",
      "status": "pass",
      "duration_ms": 297,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:35.014084+00:00",
      "read_cold_ms": 116,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 370,
      "write_warm_ms": 243,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/49_writer_metadata_data_file_consistency",
      "num": 49,
      "name": "writer_metadata_data_file_consistency",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/49_writer_metadata_data_file_consistency.sql",
      "read_script": "generator/spark-reads-iceberg/verify_49_writer_metadata_data_file_consistency.py",
      "description": "Demonstrates consistency between table metadata and data files.",
      "status": "pass",
      "duration_ms": 1888,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:36.902324+00:00",
      "read_cold_ms": 134,
      "read_warm_ms": 128,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 892,
      "write_warm_ms": 424,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/500_comprehensive_all_features",
      "num": 500,
      "name": "comprehensive_all_features",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/500_comprehensive_all_features.sql",
      "read_script": "generator/spark-reads-iceberg/verify_500_comprehensive_all_features.py",
      "description": "Grand finale: the ultimate combo test exercising every major Delta feature in a single table lifecycle. DV + CDC + column mapping + partition + constraint + schema evolution + OPTIMIZE + MERGE. 1. INSERT 200 rows (4 regions: US/EU/AP/SA, 50 each) 2. ALTER TABLE ADD CONSTRAINT...",
      "status": "pass",
      "duration_ms": 341,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:37.243987+00:00",
      "read_cold_ms": 107,
      "read_warm_ms": 116,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 703,
      "write_warm_ms": 458,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/501_update_delete_same_rows",
      "num": 501,
      "name": "update_delete_same_rows",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/501_update_delete_same_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_501_update_delete_same_rows.py",
      "description": "UPDATE then DELETE targeting overlapping rows. Tests DV stacking on same row set -- the DELETE must correctly apply on top of already-modified rows. 1. INSERT 200 rows with status='active' 2. UPDATE SET status='flagged' WHERE id <= 80 3. DELETE WHERE status='flagged' AND value <...",
      "status": "pass",
      "duration_ms": 292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:37.536288+00:00",
      "read_cold_ms": 96,
      "read_warm_ms": 96,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 116,
      "write_warm_ms": 158
    },
    {
      "id": "df-writes/iceberg/502_delete_where_false",
      "num": 502,
      "name": "delete_where_false",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/502_delete_where_false.sql",
      "read_script": "generator/spark-reads-iceberg/verify_502_delete_where_false.py",
      "description": "DELETE WHERE false (no-op). Verifies the engine handles a zero-match DELETE correctly -- no rows should be removed, no DVs created, and the table state should remain identical to the post-INSERT state.",
      "status": "pass",
      "duration_ms": 96,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:37.633296+00:00",
      "read_cold_ms": 18,
      "read_warm_ms": 13,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 25,
      "write_warm_ms": 37
    },
    {
      "id": "df-writes/iceberg/503_update_noop",
      "num": 503,
      "name": "update_noop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/503_update_noop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_503_update_noop.py",
      "description": "UPDATE that matches zero rows. Tests no-op UPDATE behavior. The WHERE clause references a value that no row can have, so the engine must correctly handle the zero-match case.",
      "status": "pass",
      "duration_ms": 57,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:37.691254+00:00",
      "read_cold_ms": 14,
      "read_warm_ms": 17,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 26,
      "write_warm_ms": 28
    },
    {
      "id": "df-writes/iceberg/504_merge_zero_matches",
      "num": 504,
      "name": "merge_zero_matches",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/504_merge_zero_matches.sql",
      "read_script": "generator/spark-reads-iceberg/verify_504_merge_zero_matches.py",
      "description": "MERGE where source and target have zero key overlap. All source rows go through the NOT MATCHED path. Tests that MERGE correctly handles pure-insert scenarios with no matched rows.",
      "status": "pass",
      "duration_ms": 106,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:37.797855+00:00",
      "read_cold_ms": 29,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 90,
      "write_warm_ms": 205
    },
    {
      "id": "df-writes/iceberg/505_merge_full_match",
      "num": 505,
      "name": "merge_full_match",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/505_merge_full_match.sql",
      "read_script": "generator/spark-reads-iceberg/verify_505_merge_full_match.py",
      "description": "MERGE where every source row matches a target row. Zero NOT MATCHED rows. Tests that MERGE correctly handles a pure-update scenario.",
      "status": "pass",
      "duration_ms": 177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:37.975394+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 174,
      "write_warm_ms": 126
    },
    {
      "id": "df-writes/iceberg/506_cdc_optimize_partition",
      "num": 506,
      "name": "cdc_optimize_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/506_cdc_optimize_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_506_cdc_optimize_partition.py",
      "description": "CDC + OPTIMIZE + partition. Three-way feature combo. Tests that CDF correctly captures changes across partitioned data, and that OPTIMIZE compacts partitioned files without data loss. 1. INSERT 60 rows (batch 1: id 1-60, 3 regions) 2. INSERT 60 rows (batch 2: id 61-120, 3...",
      "status": "pass",
      "duration_ms": 136,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:38.112289+00:00",
      "read_cold_ms": 47,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 301,
      "write_warm_ms": 598
    },
    {
      "id": "df-writes/iceberg/507_constraint_partition_evolve",
      "num": 507,
      "name": "constraint_partition_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/507_constraint_partition_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_507_constraint_partition_evolve.py",
      "description": "Constraint + partition + schema evolution. Three-way feature combo. Tests that CHECK constraints are enforced after schema evolution (adding a column), and that DML operations respect both constraints and partition structure. 1. INSERT 120 rows (3 regions, 40 each) 2. ALTER...",
      "status": "pass",
      "duration_ms": 286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:38.398941+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 136,
      "write_warm_ms": 170
    },
    {
      "id": "df-writes/iceberg/508_colmap_cdc_merge_dml",
      "num": 508,
      "name": "colmap_cdc_merge_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/508_colmap_cdc_merge_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_508_colmap_cdc_merge_dml.py",
      "description": "Column mapping + CDC + MERGE + subsequent DML. Four-way feature combo. Tests that column mapping (name mode) works correctly with CDF tracking through a MERGE and a subsequent DELETE. 1. INSERT 100 rows with status='new' 2. MERGE from 120-row CTE: MATCHED->UPDATE...",
      "status": "pass",
      "duration_ms": 289,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:38.688530+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 308,
      "write_warm_ms": 296
    },
    {
      "id": "df-writes/iceberg/509_dv_partition_optimize_merge",
      "num": 509,
      "name": "dv_partition_optimize_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/509_dv_partition_optimize_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_509_dv_partition_optimize_merge.py",
      "description": "DV + partition + OPTIMIZE + MERGE. Four-way feature combo. Tests that deletion vectors survive OPTIMIZE compaction in a partitioned table, and that a subsequent MERGE correctly handles rows that were previously deleted and compacted away. 1. INSERT 50 rows batch 1 (id 1-50, 3...",
      "status": "pass",
      "duration_ms": 298,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:38.986910+00:00",
      "read_cold_ms": 128,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 291,
      "write_warm_ms": 424
    },
    {
      "id": "df-writes/iceberg/50_writer_checkpoint_metadata_cleanup",
      "num": 50,
      "name": "writer_checkpoint_metadata_cleanup",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/50_writer_checkpoint_metadata_cleanup.sql",
      "read_script": "generator/spark-reads-iceberg/verify_50_writer_checkpoint_metadata_cleanup.py",
      "description": "Demonstrates checkpoint creation and old log file cleanup. accumulates many Delta log files. Checkpoints consolidate the metadata state, allowing safe cleanup of old log entries.",
      "status": "pass",
      "duration_ms": 111,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:39.098215+00:00",
      "read_cold_ms": 30,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 523,
      "write_warm_ms": 393,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/510_cdc_colmap_constraint_evolve",
      "num": 510,
      "name": "cdc_colmap_constraint_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/510_cdc_colmap_constraint_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_510_cdc_colmap_constraint_evolve.py",
      "description": "CDC + column mapping + constraint + schema evolution. Four-way feature combo. Tests that CDF correctly tracks changes under column mapping mode=name, with a CHECK constraint enforced, after schema evolution adds a new column. 1. INSERT 100 rows with score=(i*53)%100 2. ALTER...",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:39.357875+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 106,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 301,
      "write_warm_ms": 292
    },
    {
      "id": "df-writes/iceberg/511_large_merge_update",
      "num": 511,
      "name": "large_merge_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/511_large_merge_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_511_large_merge_update.py",
      "description": "Large-scale MERGE with UPDATE only (2000 rows, full overlap). All source rows match target rows, so only the WHEN MATCHED branch fires. Tests DV performance under bulk UPDATE via MERGE.",
      "status": "pass",
      "duration_ms": 238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:39.596273+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 177,
      "write_warm_ms": 240
    },
    {
      "id": "df-writes/iceberg/512_large_delete",
      "num": 512,
      "name": "large_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/512_large_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_512_large_delete.py",
      "description": "Large-scale DELETE (10000 rows, 90% deleted). Tests mass deletion vector creation under heavy DELETE load. Only 1 in 10 rows survives.",
      "status": "pass",
      "duration_ms": 167,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:39.764162+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 90,
      "write_warm_ms": 102
    },
    {
      "id": "df-writes/iceberg/513_twenty_version_stress",
      "num": 513,
      "name": "twenty_version_stress",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/513_twenty_version_stress.sql",
      "read_script": "generator/spark-reads-iceberg/verify_513_twenty_version_stress.py",
      "description": "20+ versions of mixed DML. Tests long version chain with interleaved INSERT, UPDATE, and DELETE operations across many commits.",
      "status": "pass",
      "duration_ms": 249,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:40.013367+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 1853,
      "write_warm_ms": 1499
    },
    {
      "id": "df-writes/iceberg/514_date_column_dml",
      "num": 514,
      "name": "date_column_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/514_date_column_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_514_date_column_dml.py",
      "description": "DATE columns with DML using date predicates. Uses arrow_cast for Date32 to ensure deterministic date handling without relying on string parsing.",
      "status": "pass",
      "duration_ms": 364,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:40.377726+00:00",
      "read_cold_ms": 106,
      "read_warm_ms": 146,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 71,
      "write_warm_ms": 131
    },
    {
      "id": "df-writes/iceberg/515_multiple_type_columns",
      "num": 515,
      "name": "multiple_type_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/515_multiple_type_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_515_multiple_type_columns.py",
      "description": "Many column types in one table with DML operations. Tests that type diversity is preserved correctly through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:40.661224+00:00",
      "read_cold_ms": 116,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 221,
      "write_warm_ms": 238
    },
    {
      "id": "df-writes/iceberg/516_partition_three_col",
      "num": 516,
      "name": "partition_three_col",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/516_partition_three_col.sql",
      "read_script": "generator/spark-reads-iceberg/verify_516_partition_three_col.py",
      "description": "Three-column partitioning (year, month, region) with DML operations. Tests partition pruning and DV creation across a multi-level partition scheme.",
      "status": "pass",
      "duration_ms": 233,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:40.895131+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 303,
      "write_warm_ms": 491
    },
    {
      "id": "df-writes/iceberg/517_cdc_dv_delete_reinsert_merge",
      "num": 517,
      "name": "cdc_dv_delete_reinsert_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/517_cdc_dv_delete_reinsert_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_517_cdc_dv_delete_reinsert_merge.py",
      "description": "CDC + DV + delete-reinsert + MERGE. Complex lifecycle where rows are deleted and re-inserted with different generation markers, then a MERGE unifies everything. Tests CDF tracking across multiple lifecycle stages.",
      "status": "pass",
      "duration_ms": 307,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:41.202499+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 122,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 466,
      "write_warm_ms": 539
    },
    {
      "id": "df-writes/iceberg/518_evolve_three_adds",
      "num": 518,
      "name": "evolve_three_adds",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/518_evolve_three_adds.sql",
      "read_script": "generator/spark-reads-iceberg/verify_518_evolve_three_adds.py",
      "description": "Three sequential ALTER TABLE ADD COLUMN operations with DML after each. Tests schema evolution where new columns are added incrementally and existing rows gain values through UPDATE.",
      "status": "pass",
      "duration_ms": 274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:41.476922+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 316,
      "write_warm_ms": 222
    },
    {
      "id": "df-writes/iceberg/519_colmap_partition_evolve_merge",
      "num": 519,
      "name": "colmap_partition_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/519_colmap_partition_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_519_colmap_partition_evolve_merge.py",
      "description": "Column mapping (name mode) + partitioning + schema evolution + MERGE. Four-way feature interaction test. Column mapping with name mode requires minReaderVersion=2, minWriterVersion=5.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:41.736898+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 314,
      "write_warm_ms": 285
    },
    {
      "id": "df-writes/iceberg/51_writer_version_requirements_matrix",
      "num": 51,
      "name": "writer_version_requirements_matrix",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/51_writer_version_requirements_matrix.sql",
      "read_script": "generator/spark-reads-iceberg/verify_51_writer_version_requirements_matrix.py",
      "description": "Demonstrates writer version requirements for different features.",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:41.967732+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 398,
      "write_warm_ms": 1016,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/520_optimize_cdc_constraint",
      "num": 520,
      "name": "optimize_cdc_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/520_optimize_cdc_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_520_optimize_cdc_constraint.py",
      "description": "OPTIMIZE + CDC + constraint. Three-way feature interaction. Tests that OPTIMIZE compaction works correctly with CDC enabled and a CHECK constraint enforced.",
      "status": "pass",
      "duration_ms": 177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:42.145149+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 401,
      "write_warm_ms": 886
    },
    {
      "id": "df-writes/iceberg/521_merge_chain",
      "num": 521,
      "name": "merge_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/521_merge_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_521_merge_chain.py",
      "description": "Two sequential MERGEs on the same table. Tests MERGE-after-MERGE correctness where the second MERGE operates on data already modified by the first.",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:42.413506+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 271,
      "write_warm_ms": 300
    },
    {
      "id": "df-writes/iceberg/522_update_chain_different_cols",
      "num": 522,
      "name": "update_chain_different_cols",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/522_update_chain_different_cols.sql",
      "read_script": "generator/spark-reads-iceberg/verify_522_update_chain_different_cols.py",
      "description": "Three UPDATEs each modifying different columns on overlapping row ranges. Tests that independent column updates do not interfere with each other when applied via deletion vectors.",
      "status": "pass",
      "duration_ms": 266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:42.680706+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 306,
      "write_warm_ms": 386
    },
    {
      "id": "df-writes/iceberg/523_delete_then_merge",
      "num": 523,
      "name": "delete_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/523_delete_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_523_delete_then_merge.py",
      "description": "DELETE followed by MERGE on the same table. Tests that MERGE correctly operates on a table containing deletion vectors from a prior DELETE. The deleted rows are re-inserted by the MERGE's NOT MATCHED branch.",
      "status": "pass",
      "duration_ms": 243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:42.924110+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 97,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 218,
      "write_warm_ms": 279
    },
    {
      "id": "df-writes/iceberg/524_merge_then_delete",
      "num": 524,
      "name": "merge_then_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/524_merge_then_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_524_merge_then_delete.py",
      "description": "MERGE followed by DELETE. Tests that DELETE correctly operates on data that was just inserted/updated by a MERGE operation.",
      "status": "pass",
      "duration_ms": 266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:43.190947+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 258,
      "write_warm_ms": 131
    },
    {
      "id": "df-writes/iceberg/525_partition_delete_all_one",
      "num": 525,
      "name": "partition_delete_all_one",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/525_partition_delete_all_one.sql",
      "read_script": "generator/spark-reads-iceberg/verify_525_partition_delete_all_one.py",
      "description": "DELETE all rows from a single partition, then UPDATE a different partition. Tests partition-level deletion where an entire partition's data files are removed, followed by an UPDATE on surviving partitions.",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:43.405671+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 198,
      "write_warm_ms": 125
    },
    {
      "id": "df-writes/iceberg/526_cdc_dv_colmap_partition",
      "num": 526,
      "name": "cdc_dv_colmap_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/526_cdc_dv_colmap_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_526_cdc_dv_colmap_partition.py",
      "description": "Four-way feature combination: CDC (Change Data Feed) + Deletion Vectors + Column Mapping (name mode) + Partitioning. Tests that all four features coexist and produce correct data after UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 219,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:43.625663+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 156,
      "write_warm_ms": 295
    },
    {
      "id": "df-writes/iceberg/527_constraint_merge_update",
      "num": 527,
      "name": "constraint_merge_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/527_constraint_merge_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_527_constraint_merge_update.py",
      "description": "CHECK constraint + MERGE + subsequent UPDATE. Tests that the constraint (value > 0) is enforced across a DML chain: the MERGE inserts/updates only valid rows, and the UPDATE further modifies values while keeping them within the constraint.",
      "status": "pass",
      "duration_ms": 229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:43.854813+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 189,
      "write_warm_ms": 193
    },
    {
      "id": "df-writes/iceberg/528_evolve_partition_cdc",
      "num": 528,
      "name": "evolve_partition_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/528_evolve_partition_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_528_evolve_partition_cdc.py",
      "description": "Three-way combination: Schema evolution (ADD COLUMN) + Partitioning + CDC. Tests that a column added after initial data load works correctly with partitioned data and change data feed tracking.",
      "status": "pass",
      "duration_ms": 272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:44.127837+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 706,
      "write_warm_ms": 234
    },
    {
      "id": "df-writes/iceberg/529_dv_cdc_optimize_merge",
      "num": 529,
      "name": "dv_cdc_optimize_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/529_dv_cdc_optimize_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_529_dv_cdc_optimize_merge.py",
      "description": "Four-way combination: Deletion Vectors + CDC + OPTIMIZE + MERGE. Tests that MERGE works correctly after OPTIMIZE has materialized deletion vectors into compacted files. The OPTIMIZE step converts logical deletes (DVs) into physical deletes, then MERGE must correctly match...",
      "status": "pass",
      "duration_ms": 299,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:44.427183+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 103,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 1261,
      "write_warm_ms": 1431
    },
    {
      "id": "df-writes/iceberg/52_writer_append_only_enforcement",
      "num": 52,
      "name": "writer_append_only_enforcement",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/52_writer_append_only_enforcement.sql",
      "read_script": "generator/spark-reads-iceberg/verify_52_writer_append_only_enforcement.py",
      "description": "Demonstrates append-only table constraint where only INSERT operations are allowed. Writers MUST NOT perform DELETE, UPDATE, or MERGE operations on append-only tables.",
      "status": "pass",
      "duration_ms": 177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:44.605295+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 154,
      "write_warm_ms": 146,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/530_colmap_constraint_merge",
      "num": 530,
      "name": "colmap_constraint_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/530_colmap_constraint_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_530_colmap_constraint_merge.py",
      "description": "Three-way combination: Column Mapping (name mode) + CHECK constraint + MERGE. Tests that a CHECK constraint (score >= 0 AND score <= 100) is enforced when MERGE inserts and updates rows on a column-mapped table.",
      "status": "pass",
      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:44.809332+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 433,
      "write_warm_ms": 135
    },
    {
      "id": "df-writes/iceberg/531_update_set_from_id",
      "num": 531,
      "name": "update_set_from_id",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/531_update_set_from_id.sql",
      "read_script": "generator/spark-reads-iceberg/verify_531_update_set_from_id.py",
      "description": "UPDATE SET column values derived from id using mathematical expressions. Tests that UPDATE can compute multiple columns from existing column values.",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:45.034787+00:00",
      "read_cold_ms": 94,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 77,
      "write_warm_ms": 92
    },
    {
      "id": "df-writes/iceberg/532_delete_modular",
      "num": 532,
      "name": "delete_modular",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/532_delete_modular.sql",
      "read_script": "generator/spark-reads-iceberg/verify_532_delete_modular.py",
      "description": "DELETE with multiple modular predicates in sequence. Tests that each DELETE operates independently on the surviving rows from the previous DELETE.",
      "status": "pass",
      "duration_ms": 171,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:45.206398+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 173,
      "write_warm_ms": 120
    },
    {
      "id": "df-writes/iceberg/533_partition_insert_new",
      "num": 533,
      "name": "partition_insert_new",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/533_partition_insert_new.sql",
      "read_script": "generator/spark-reads-iceberg/verify_533_partition_insert_new.py",
      "description": "INSERT rows into a new partition value that did not exist at CREATE time. Tests dynamic partition creation and UPDATE within that new partition.",
      "status": "pass",
      "duration_ms": 227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:45.434009+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 172,
      "write_warm_ms": 287
    },
    {
      "id": "df-writes/iceberg/534_cdc_schema_evolve_merge",
      "num": 534,
      "name": "cdc_schema_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/534_cdc_schema_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_534_cdc_schema_evolve_merge.py",
      "description": "CDC + schema evolution + MERGE. Tests that Change Data Feed works correctly across a schema change (ADD COLUMN) combined with a MERGE operation.",
      "status": "pass",
      "duration_ms": 191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:45.625342+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 45,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 654,
      "write_warm_ms": 440
    },
    {
      "id": "df-writes/iceberg/535_optimize_delete_optimize",
      "num": 535,
      "name": "optimize_delete_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/535_optimize_delete_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_535_optimize_delete_optimize.py",
      "description": "OPTIMIZE then DELETE then OPTIMIZE again. Tests double compaction where the second OPTIMIZE materializes deletion vectors left by the DELETE on the already-compacted files from the first OPTIMIZE.",
      "status": "pass",
      "duration_ms": 132,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:45.758168+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 822,
      "write_warm_ms": 312
    },
    {
      "id": "df-writes/iceberg/536_merge_update_specific_cols",
      "num": 536,
      "name": "merge_update_specific_cols",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/536_merge_update_specific_cols.sql",
      "read_script": "generator/spark-reads-iceberg/verify_536_merge_update_specific_cols.py",
      "description": "MERGE that only updates specific columns (not all). Tests partial column update in MERGE where only score changes while name, category, and status are preserved from the original row.",
      "status": "pass",
      "duration_ms": 205,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:45.963299+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 141,
      "write_warm_ms": 68
    },
    {
      "id": "df-writes/iceberg/537_dv_cdc_partition_delete_update",
      "num": 537,
      "name": "dv_cdc_partition_delete_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/537_dv_cdc_partition_delete_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_537_dv_cdc_partition_delete_update.py",
      "description": "DV + CDC + partition + DELETE + UPDATE. Tests combined DML operations on a partitioned table with both deletion vectors and change data feed enabled.",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:46.224482+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 374,
      "write_warm_ms": 250
    },
    {
      "id": "df-writes/iceberg/538_constraint_two_checks",
      "num": 538,
      "name": "constraint_two_checks",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/538_constraint_two_checks.sql",
      "read_script": "generator/spark-reads-iceberg/verify_538_constraint_two_checks.py",
      "description": "Two independent CHECK constraints on the same table followed by DML. Tests that both constraints are enforced simultaneously and that valid UPDATE and DELETE operations succeed.",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:46.475074+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 122,
      "write_warm_ms": 154
    },
    {
      "id": "df-writes/iceberg/539_evolve_rename_add",
      "num": 539,
      "name": "evolve_rename_add",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/539_evolve_rename_add.sql",
      "read_script": "generator/spark-reads-iceberg/verify_539_evolve_rename_add.py",
      "description": "RENAME COLUMN then ADD COLUMN. Tests stacked schema evolution where a column is renamed first, then a new column is added, followed by DML that uses both the renamed and new columns.",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:46.736330+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 158,
      "write_warm_ms": 236
    },
    {
      "id": "df-writes/iceberg/53_writer_column_invariants_not_null",
      "num": 53,
      "name": "writer_column_invariants_not_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/53_writer_column_invariants_not_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_53_writer_column_invariants_not_null.py",
      "description": "Demonstrates CHECK constraints that enforce data integrity rules. Writers MUST validate all CHECK constraints before writing data.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:46.982007+00:00",
      "read_cold_ms": 91,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 626,
      "write_warm_ms": 343,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/540_insert_overwrite_then_merge",
      "num": 540,
      "name": "insert_overwrite_then_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/540_insert_overwrite_then_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_540_insert_overwrite_then_merge.py",
      "description": "INSERT OVERWRITE then MERGE. Tests that MERGE operates correctly on data seeded via INSERT OVERWRITE (which replaces all existing data).",
      "status": "pass",
      "duration_ms": 238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:47.220338+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 212,
      "write_warm_ms": 187
    },
    {
      "id": "df-writes/iceberg/541_merge_delete_reinsert",
      "num": 541,
      "name": "merge_delete_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/541_merge_delete_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_541_merge_delete_reinsert.py",
      "description": "MERGE that deletes rows then re-inserts them. Tests row lifecycle where rows are removed via MERGE WHEN MATCHED DELETE then re-added via INSERT.",
      "status": "pass",
      "duration_ms": 224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:47.444908+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 113,
      "write_warm_ms": 132
    },
    {
      "id": "df-writes/iceberg/542_cdc_partition_optimize_merge",
      "num": 542,
      "name": "cdc_partition_optimize_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/542_cdc_partition_optimize_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_542_cdc_partition_optimize_merge.py",
      "description": "CDC + partition + OPTIMIZE + MERGE. Four-way combination testing that Change Data Feed, partitioning, file compaction, and MERGE all cooperate.",
      "status": "pass",
      "duration_ms": 226,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:47.671937+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 481,
      "write_warm_ms": 591
    },
    {
      "id": "df-writes/iceberg/543_colmap_optimize_evolve",
      "num": 543,
      "name": "colmap_optimize_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/543_colmap_optimize_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_543_colmap_optimize_evolve.py",
      "description": "Column mapping + OPTIMIZE + schema evolution. Three-way combination testing that column mapping mode=name, file compaction, and ADD COLUMN cooperate.",
      "status": "pass",
      "duration_ms": 252,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:47.924301+00:00",
      "read_cold_ms": 104,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 255,
      "write_warm_ms": 223
    },
    {
      "id": "df-writes/iceberg/544_constraint_cdc_partition",
      "num": 544,
      "name": "constraint_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/544_constraint_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_544_constraint_cdc_partition.py",
      "description": "Constraint + CDC + partition. Three-way combination testing that CHECK constraints, Change Data Feed, and partitioning cooperate correctly.",
      "status": "pass",
      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:48.127012+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 104,
      "write_warm_ms": 331
    },
    {
      "id": "df-writes/iceberg/545_dv_constraint_evolve_merge",
      "num": 545,
      "name": "dv_constraint_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/545_dv_constraint_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_545_dv_constraint_evolve_merge.py",
      "description": "DV + constraint + schema evolution + MERGE. Four-way combination testing that deletion vectors, CHECK constraints, ADD COLUMN, and MERGE cooperate.",
      "status": "pass",
      "duration_ms": 341,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:48.468210+00:00",
      "read_cold_ms": 93,
      "read_warm_ms": 143,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 378,
      "write_warm_ms": 164
    },
    {
      "id": "df-writes/iceberg/546_partition_colmap_constraint",
      "num": 546,
      "name": "partition_colmap_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/546_partition_colmap_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_546_partition_colmap_constraint.py",
      "description": "Partition + column mapping + constraint. Three-way combination testing that partitioning, column mapping mode=name, and CHECK constraints cooperate.",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:48.669128+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 171,
      "write_warm_ms": 196
    },
    {
      "id": "df-writes/iceberg/547_merge_partition_cdc_dml",
      "num": 547,
      "name": "merge_partition_cdc_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/547_merge_partition_cdc_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_547_merge_partition_cdc_dml.py",
      "description": "MERGE + partition + CDC + additional DML. Complex lifecycle testing that MERGE, partitioning, Change Data Feed, UPDATE, and DELETE all cooperate.",
      "status": "pass",
      "duration_ms": 260,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:48.929799+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 104,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 687,
      "write_warm_ms": 520
    },
    {
      "id": "df-writes/iceberg/548_optimize_colmap_cdc",
      "num": 548,
      "name": "optimize_colmap_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/548_optimize_colmap_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_548_optimize_colmap_cdc.py",
      "description": "OPTIMIZE + column mapping + CDC. Three-way combination testing that file compaction, column mapping mode=name, and Change Data Feed cooperate.",
      "status": "pass",
      "duration_ms": 109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:49.039237+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 242,
      "write_warm_ms": 467
    },
    {
      "id": "df-writes/iceberg/549_evolve_optimize_delete",
      "num": 549,
      "name": "evolve_optimize_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/549_evolve_optimize_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_549_evolve_optimize_delete.py",
      "description": "Schema evolution + OPTIMIZE + DELETE. Three-way combination testing that ADD COLUMN, file compaction, and DELETE cooperate correctly.",
      "status": "pass",
      "duration_ms": 124,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:49.164231+00:00",
      "read_cold_ms": 29,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 410,
      "write_warm_ms": 723
    },
    {
      "id": "df-writes/iceberg/54_writer_check_constraints_validation",
      "num": 54,
      "name": "writer_check_constraints_validation",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/54_writer_check_constraints_validation.sql",
      "read_script": "generator/spark-reads-iceberg/verify_54_writer_check_constraints_validation.py",
      "description": "Demonstrates CHECK constraints that writers must validate before committing data.",
      "status": "pass",
      "duration_ms": 236,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:49.401005+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 236,
      "write_warm_ms": 380,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/550_cdc_constraint_merge_partition",
      "num": 550,
      "name": "cdc_constraint_merge_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/550_cdc_constraint_merge_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_550_cdc_constraint_merge_partition.py",
      "description": "CDC + constraint + MERGE + partition. Four-way combination testing that Change Data Feed, CHECK constraints, MERGE, and partitioning cooperate.",
      "status": "pass",
      "duration_ms": 262,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:49.663165+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 691,
      "write_warm_ms": 352
    },
    {
      "id": "df-writes/iceberg/551_delete_update_merge_chain",
      "num": 551,
      "name": "delete_update_merge_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/551_delete_update_merge_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_551_delete_update_merge_chain.py",
      "description": "DELETE then UPDATE then MERGE. Three sequential DML types in one script. Validates that the engine correctly chains heterogeneous DML operations.",
      "status": "pass",
      "duration_ms": 210,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:49.873448+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 443,
      "write_warm_ms": 216
    },
    {
      "id": "df-writes/iceberg/552_merge_with_aggregated_source",
      "num": 552,
      "name": "merge_with_aggregated_source",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/552_merge_with_aggregated_source.sql",
      "read_script": "generator/spark-reads-iceberg/verify_552_merge_with_aggregated_source.py",
      "description": "MERGE where the source is a CTE with computed totals. Tests that MERGE handles complex source expressions and additive UPDATE logic.",
      "status": "pass",
      "duration_ms": 239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:50.113497+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 110,
      "write_warm_ms": 141
    },
    {
      "id": "df-writes/iceberg/553_partition_five_insert_optimize",
      "num": 553,
      "name": "partition_five_insert_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/553_partition_five_insert_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_553_partition_five_insert_optimize.py",
      "description": "Five INSERT batches into a partitioned table then OPTIMIZE. Tests per-partition compaction after multiple small writes.",
      "status": "pass",
      "duration_ms": 320,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:50.433942+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 460,
      "write_warm_ms": 704
    },
    {
      "id": "df-writes/iceberg/554_colmap_rename_drop",
      "num": 554,
      "name": "colmap_rename_drop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/554_colmap_rename_drop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_554_colmap_rename_drop.py",
      "description": "Column mapping with RENAME COLUMN and DROP COLUMN in the same script. Tests stacked column mutations under columnMapping.mode=name.",
      "status": "pass",
      "duration_ms": 191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:50.625691+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 99,
      "write_warm_ms": 85
    },
    {
      "id": "df-writes/iceberg/555_cdc_dv_all_dml",
      "num": 555,
      "name": "cdc_dv_all_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/555_cdc_dv_all_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_555_cdc_dv_all_dml.py",
      "description": "CDC + DV with every DML type: INSERT, UPDATE, DELETE, MERGE. Validates that change data feed captures all operation types correctly while deletion vectors are active.",
      "status": "pass",
      "duration_ms": 224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:50.849843+00:00",
      "read_cold_ms": 91,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 190,
      "write_warm_ms": 363
    },
    {
      "id": "df-writes/iceberg/556_constraint_not_null_dml",
      "num": 556,
      "name": "constraint_not_null_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/556_constraint_not_null_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_556_constraint_not_null_dml.py",
      "description": "NOT NULL columns + CHECK constraints + DML. Validates that constraints are enforced correctly across UPDATE and DELETE operations, and that nullable columns accept NULL values.",
      "status": "pass",
      "duration_ms": 258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:51.108395+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 266,
      "write_warm_ms": 148
    },
    {
      "id": "df-writes/iceberg/557_partition_evolve_constraint_cdc",
      "num": 557,
      "name": "partition_evolve_constraint_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/557_partition_evolve_constraint_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_557_partition_evolve_constraint_cdc.py",
      "description": "Partition + schema evolution + constraint + CDC. Four-way feature combination test validating that ADD COLUMN, CHECK constraint, and CDC all work correctly on a partitioned table.",
      "status": "pass",
      "duration_ms": 248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:51.356816+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 193,
      "write_warm_ms": 293
    },
    {
      "id": "df-writes/iceberg/558_optimize_partition_merge_cdc",
      "num": 558,
      "name": "optimize_partition_merge_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/558_optimize_partition_merge_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_558_optimize_partition_merge_cdc.py",
      "description": "OPTIMIZE + partition + MERGE + CDC. Four-way combination test validating that MERGE operates correctly on an optimized partitioned table with change data feed enabled.",
      "status": "pass",
      "duration_ms": 256,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:51.613668+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 708,
      "write_warm_ms": 1111
    },
    {
      "id": "df-writes/iceberg/559_colmap_cdc_dv_evolve_partition",
      "num": 559,
      "name": "colmap_cdc_dv_evolve_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/559_colmap_cdc_dv_evolve_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_559_colmap_cdc_dv_evolve_partition.py",
      "description": "Five-way: column mapping + CDC + DV + schema evolution + partition. Validates that all five features coexist without conflicts when performing DELETE, ADD COLUMN, INSERT, and UPDATE operations.",
      "status": "pass",
      "duration_ms": 258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:51.872248+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 253,
      "write_warm_ms": 250
    },
    {
      "id": "df-writes/iceberg/55_writer_generated_columns_computed",
      "num": 55,
      "name": "writer_generated_columns_computed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/55_writer_generated_columns_computed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_55_writer_generated_columns_computed.py",
      "description": "Demonstrates generated columns that are automatically computed from other columns.",
      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:52.141707+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 188,
      "write_warm_ms": 222,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/560_all_features_stress",
      "num": 560,
      "name": "all_features_stress",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/560_all_features_stress.sql",
      "read_script": "generator/spark-reads-iceberg/verify_560_all_features_stress.py",
      "description": "Grand stress test: DV + CDC + colmap + partition + constraint + schema evolution + OPTIMIZE + MERGE + DELETE + UPDATE. Exercises nearly every Delta feature in a single script.",
      "status": "pass",
      "duration_ms": 293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:52.435029+00:00",
      "read_cold_ms": 110,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 1500,
      "write_warm_ms": 852
    },
    {
      "id": "df-writes/iceberg/561_update_multiple_where",
      "num": 561,
      "name": "update_multiple_where",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/561_update_multiple_where.sql",
      "read_script": "generator/spark-reads-iceberg/verify_561_update_multiple_where.py",
      "description": "UPDATE with complex compound WHERE (AND + OR). Tests that the engine correctly evaluates compound predicates with mixed boolean logic.",
      "status": "pass",
      "duration_ms": 263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:52.698971+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 121,
      "write_warm_ms": 89
    },
    {
      "id": "df-writes/iceberg/562_delete_in_list",
      "num": 562,
      "name": "delete_in_list",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/562_delete_in_list.sql",
      "read_script": "generator/spark-reads-iceberg/verify_562_delete_in_list.py",
      "description": "DELETE WHERE id IN (...). Tests IN-list predicate evaluation with deletion vectors.",
      "status": "pass",
      "duration_ms": 109,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:52.808227+00:00",
      "read_cold_ms": 43,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 63,
      "write_warm_ms": 55
    },
    {
      "id": "df-writes/iceberg/563_merge_with_case_update",
      "num": 563,
      "name": "merge_with_case_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/563_merge_with_case_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_563_merge_with_case_update.py",
      "description": "MERGE with CASE expression in UPDATE SET. Tests that the engine handles complex expressions within MERGE UPDATE clauses.",
      "status": "pass",
      "duration_ms": 191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:52.999554+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 51,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 143,
      "write_warm_ms": 141
    },
    {
      "id": "df-writes/iceberg/564_partition_evolve_delete_all",
      "num": 564,
      "name": "partition_evolve_delete_all",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/564_partition_evolve_delete_all.sql",
      "read_script": "generator/spark-reads-iceberg/verify_564_partition_evolve_delete_all.py",
      "description": "Partition + schema evolution + DELETE all from one partition. Tests that deleting an entire partition works correctly after schema evolution, and that re-inserted rows use the evolved schema.",
      "status": "pass",
      "duration_ms": 279,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:53.278675+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 99,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 160,
      "write_warm_ms": 214
    },
    {
      "id": "df-writes/iceberg/565_cdc_merge_delete_chain",
      "num": 565,
      "name": "cdc_merge_delete_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/565_cdc_merge_delete_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_565_cdc_merge_delete_chain.py",
      "description": "CDC + MERGE then DELETE. Tests that CDF captures both MERGE and DELETE operations in sequence.",
      "status": "pass",
      "duration_ms": 265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:53.544152+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 457,
      "write_warm_ms": 414
    },
    {
      "id": "df-writes/iceberg/566_constraint_evolve_cdc_dml",
      "num": 566,
      "name": "constraint_evolve_cdc_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/566_constraint_evolve_cdc_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_566_constraint_evolve_cdc_dml.py",
      "description": "Constraint + schema evolution + CDC + DML. Four-way feature combo. Tests that CHECK constraints, schema evolution, and CDF all interact correctly under DML operations.",
      "status": "pass",
      "duration_ms": 229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:53.774161+00:00",
      "read_cold_ms": 95,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 198,
      "write_warm_ms": 239
    },
    {
      "id": "df-writes/iceberg/567_colmap_dv_delete_update",
      "num": 567,
      "name": "colmap_dv_delete_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/567_colmap_dv_delete_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_567_colmap_dv_delete_update.py",
      "description": "Column mapping + DV + DELETE + UPDATE. Tests that deletion vectors and update operations work correctly with column mapping mode=name.",
      "status": "pass",
      "duration_ms": 270,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:54.045161+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 96,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 291,
      "write_warm_ms": 249
    },
    {
      "id": "df-writes/iceberg/568_optimize_evolve_merge",
      "num": 568,
      "name": "optimize_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/568_optimize_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_568_optimize_evolve_merge.py",
      "description": "OPTIMIZE + schema evolution + MERGE. Three-way feature combo. Tests that MERGE works correctly on a table that has been optimized and then schema-evolved.",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:54.260202+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 261,
      "write_warm_ms": 234
    },
    {
      "id": "df-writes/iceberg/569_partition_cdc_colmap_merge",
      "num": 569,
      "name": "partition_cdc_colmap_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/569_partition_cdc_colmap_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_569_partition_cdc_colmap_merge.py",
      "description": "Partition + CDC + column mapping + MERGE. Four-way feature combo. Tests that MERGE works on a partitioned table with CDF and column mapping all enabled simultaneously.",
      "status": "pass",
      "duration_ms": 217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:54.478269+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 475,
      "write_warm_ms": 284
    },
    {
      "id": "df-writes/iceberg/56_writer_default_columns_values",
      "num": 56,
      "name": "writer_default_columns_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/56_writer_default_columns_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_56_writer_default_columns_values.py",
      "description": "Demonstrates handling of default column values in Delta table writes. Tests writer behavior when columns have default values assigned.",
      "status": "pass",
      "duration_ms": 308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:54.787066+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 371,
      "write_warm_ms": 417,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/570_constraint_dv_optimize",
      "num": 570,
      "name": "constraint_dv_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/570_constraint_dv_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_570_constraint_dv_optimize.py",
      "description": "Constraint + DV + OPTIMIZE. Tests that constraint metadata survives deletion vector operations and file compaction.",
      "status": "pass",
      "duration_ms": 308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:55.095982+00:00",
      "read_cold_ms": 126,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 182,
      "write_warm_ms": 135
    },
    {
      "id": "df-writes/iceberg/571_merge_partition_delete_insert",
      "num": 571,
      "name": "merge_partition_delete_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/571_merge_partition_delete_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_571_merge_partition_delete_insert.py",
      "description": "MERGE with DELETE clause on partitioned table + subsequent INSERT. Tests that MERGE DELETE conditions work on partitioned tables and that subsequent inserts are correctly placed.",
      "status": "pass",
      "duration_ms": 281,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:55.377620+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 412,
      "write_warm_ms": 305
    },
    {
      "id": "df-writes/iceberg/572_cdc_constraint_evolve_merge",
      "num": 572,
      "name": "cdc_constraint_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/572_cdc_constraint_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_572_cdc_constraint_evolve_merge.py",
      "description": "CDC + constraint + schema evolution + MERGE. Four-way feature combo. Tests that MERGE respects constraints after schema evolution on a CDF-enabled table.",
      "status": "pass",
      "duration_ms": 189,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:55.567557+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 209,
      "write_warm_ms": 413
    },
    {
      "id": "df-writes/iceberg/573_dv_partition_evolve_delete",
      "num": 573,
      "name": "dv_partition_evolve_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/573_dv_partition_evolve_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_573_dv_partition_evolve_delete.py",
      "description": "DV + partition + schema evolution + DELETE. Tests that schema evolution on a partitioned DV table works correctly with DELETE and UPDATE on the new column.",
      "status": "pass",
      "duration_ms": 210,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:55.778262+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 369,
      "write_warm_ms": 165
    },
    {
      "id": "df-writes/iceberg/574_colmap_optimize_merge_dml",
      "num": 574,
      "name": "colmap_optimize_merge_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/574_colmap_optimize_merge_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_574_colmap_optimize_merge_dml.py",
      "description": "Column mapping + OPTIMIZE + MERGE + DML. Four-way feature combo. Tests that MERGE and DELETE work correctly on an optimized column-mapped table.",
      "status": "pass",
      "duration_ms": 1374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:57.153207+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 514,
      "write_warm_ms": 362
    },
    {
      "id": "df-writes/iceberg/575_cdc_dv_colmap_evolve",
      "num": 575,
      "name": "cdc_dv_colmap_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/575_cdc_dv_colmap_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_575_cdc_dv_colmap_evolve.py",
      "description": "CDC + DV + column mapping + schema evolution. Four-way feature combo. Tests that schema evolution works on a table with CDC, DVs, and column mapping all enabled.",
      "status": "pass",
      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:57.357013+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 403,
      "write_warm_ms": 326
    },
    {
      "id": "df-writes/iceberg/576_partition_constraint_optimize",
      "num": 576,
      "name": "partition_constraint_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/576_partition_constraint_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_576_partition_constraint_optimize.py",
      "description": "Partition + constraint + OPTIMIZE. Three-way feature combo. Tests that constraints survive OPTIMIZE on partitioned tables and that subsequent inserts still respect constraints.",
      "status": "pass",
      "duration_ms": 512,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:57.869566+00:00",
      "read_cold_ms": 138,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 415,
      "write_warm_ms": 311
    },
    {
      "id": "df-writes/iceberg/577_merge_evolve_cdc_dml",
      "num": 577,
      "name": "merge_evolve_cdc_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/577_merge_evolve_cdc_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_577_merge_evolve_cdc_dml.py",
      "description": "MERGE + schema evolution + CDC + DML. Four-way feature combo. Tests that MERGE correctly populates a new column added via schema evolution, and that subsequent DML operates correctly with CDF.",
      "status": "pass",
      "duration_ms": 228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:58.097869+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 492,
      "write_warm_ms": 655
    },
    {
      "id": "df-writes/iceberg/578_colmap_constraint_partition_dml",
      "num": 578,
      "name": "colmap_constraint_partition_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/578_colmap_constraint_partition_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_578_colmap_constraint_partition_dml.py",
      "description": "Column mapping + constraint + partition + DML. Four-way feature combo. Tests that constraints are enforced on a partitioned column-mapped table during UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 223,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:58.321002+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 137,
      "write_warm_ms": 149
    },
    {
      "id": "df-writes/iceberg/579_optimize_evolve_cdc_partition",
      "num": 579,
      "name": "optimize_evolve_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/579_optimize_evolve_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_579_optimize_evolve_cdc_partition.py",
      "description": "OPTIMIZE + schema evolution + CDC + partition. Four-way feature combo. Tests that OPTIMIZE on a partitioned CDC table does not break schema evolution, and that subsequent inserts and updates work correctly.",
      "status": "pass",
      "duration_ms": 523,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:58.844487+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 352,
      "write_warm_ms": 435
    },
    {
      "id": "df-writes/iceberg/57_writer_identity_columns_auto",
      "num": 57,
      "name": "writer_identity_columns_auto",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/57_writer_identity_columns_auto.sql",
      "read_script": "generator/spark-reads-iceberg/verify_57_writer_identity_columns_auto.py",
      "description": "Demonstrates handling of identity columns with auto-increment behavior.",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:59.005493+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 195,
      "write_warm_ms": 134,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/580_five_way_merge_stress",
      "num": 580,
      "name": "five_way_merge_stress",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/580_five_way_merge_stress.sql",
      "read_script": "generator/spark-reads-iceberg/verify_580_five_way_merge_stress.py",
      "description": "DV + CDC + partition + constraint + MERGE. Five-way combo with MERGE. The most complex feature interaction test: all major Delta features active simultaneously.",
      "status": "pass",
      "duration_ms": 280,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:59.286445+00:00",
      "read_cold_ms": 107,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 688,
      "write_warm_ms": 710
    },
    {
      "id": "df-writes/iceberg/581_merge_three_clause_partition",
      "num": 581,
      "name": "merge_three_clause_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/581_merge_three_clause_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_581_merge_three_clause_partition.py",
      "description": "MERGE with all 3 clauses (DELETE + UPDATE + INSERT) on a partitioned table. Validates that a single MERGE statement can delete low-scoring rows, update surviving matches, and insert new rows -- all in one pass on a partitioned table.",
      "status": "pass",
      "duration_ms": 237,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:59.524299+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 124,
      "write_warm_ms": 130
    },
    {
      "id": "df-writes/iceberg/582_cdc_dv_constraint_delete",
      "num": 582,
      "name": "cdc_dv_constraint_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/582_cdc_dv_constraint_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_582_cdc_dv_constraint_delete.py",
      "description": "CDC + DV + constraint + DELETE. Tests that constraint metadata is properly tracked in the CDF context, and that DELETE operations generate correct change data feed records when deletion vectors are enabled.",
      "status": "pass",
      "duration_ms": 229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:59.754502+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 269,
      "write_warm_ms": 103
    },
    {
      "id": "df-writes/iceberg/583_colmap_evolve_rename_merge",
      "num": 583,
      "name": "colmap_evolve_rename_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/583_colmap_evolve_rename_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_583_colmap_evolve_rename_merge.py",
      "description": "Column mapping + schema evolution (ADD + RENAME) + MERGE. Tests that column rename and column addition work correctly with column mapping mode, and that a subsequent MERGE properly resolves the renamed/added columns.",
      "status": "pass",
      "duration_ms": 233,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:26:59.987855+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 102,
      "write_warm_ms": 131
    },
    {
      "id": "df-writes/iceberg/584_partition_delete_update_insert",
      "num": 584,
      "name": "partition_delete_update_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/584_partition_delete_update_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_584_partition_delete_update_insert.py",
      "description": "Partition + DELETE + UPDATE + INSERT all targeting different partitions. Validates that DML operations correctly isolate to their target partitions and that cross-partition data remains untouched.",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:00.209557+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 360,
      "write_warm_ms": 174
    },
    {
      "id": "df-writes/iceberg/585_optimize_constraint_cdc_partition",
      "num": 585,
      "name": "optimize_constraint_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/585_optimize_constraint_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_585_optimize_constraint_cdc_partition.py",
      "description": "OPTIMIZE + constraint + CDC + partition. Four-way feature combination. Validates that OPTIMIZE compacts files correctly on a partitioned table with CDC enabled and a CHECK constraint, and that subsequent DML still produces correct CDF records.",
      "status": "pass",
      "duration_ms": 280,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:00.490546+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 330,
      "write_warm_ms": 375
    },
    {
      "id": "df-writes/iceberg/586_merge_cdc_partition_evolve",
      "num": 586,
      "name": "merge_cdc_partition_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/586_merge_cdc_partition_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_586_merge_cdc_partition_evolve.py",
      "description": "MERGE + CDC + partition + schema evolution. Four-way feature combination. Validates that MERGE works correctly after adding a new column to a partitioned table with CDC enabled, and that CDF records capture the evolved schema.",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:00.720820+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 412,
      "write_warm_ms": 362
    },
    {
      "id": "df-writes/iceberg/587_colmap_dv_cdc_constraint",
      "num": 587,
      "name": "colmap_dv_cdc_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/587_colmap_dv_cdc_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_587_colmap_dv_cdc_constraint.py",
      "description": "Column mapping + DV + CDC + constraint. Four-way feature combination. Validates that all four features coexist: column mapping tracks physical IDs, deletion vectors handle deletes, CDC captures change records, and the CHECK constraint is enforced throughout.",
      "status": "pass",
      "duration_ms": 188,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:00.909623+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 149,
      "write_warm_ms": 312
    },
    {
      "id": "df-writes/iceberg/588_large_multi_batch_insert",
      "num": 588,
      "name": "large_multi_batch_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/588_large_multi_batch_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_588_large_multi_batch_insert.py",
      "description": "Large table: 20 INSERT batches of 100 rows each (2000 total) + DML. Validates that the engine handles many small commits correctly, producing a large number of log entries, and that subsequent DELETE and UPDATE operations work on a table with many data files.",
      "status": "pass",
      "duration_ms": 471,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:01.381487+00:00",
      "read_cold_ms": 130,
      "read_warm_ms": 137,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:many-batches",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 1405,
      "write_warm_ms": 1340
    },
    {
      "id": "df-writes/iceberg/589_merge_delete_clause_cdc",
      "num": 589,
      "name": "merge_delete_clause_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/589_merge_delete_clause_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_589_merge_delete_clause_cdc.py",
      "description": "MERGE with DELETE clause + CDC. Tests that CDF records capture MERGE-DELETE events correctly -- when a MERGE statement deletes rows via its WHEN MATCHED AND ... THEN DELETE clause, the change data feed must record those as deletes.",
      "status": "pass",
      "duration_ms": 208,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:01.590279+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 297,
      "write_warm_ms": 239
    },
    {
      "id": "df-writes/iceberg/58_reader_version_requirements_matrix",
      "num": 58,
      "name": "reader_version_requirements_matrix",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/58_reader_version_requirements_matrix.sql",
      "read_script": "generator/spark-reads-iceberg/verify_58_reader_version_requirements_matrix.py",
      "description": "Demonstrates reader version requirements to read tables with various features.",
      "status": "pass",
      "duration_ms": 296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:01.887270+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 96,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 223,
      "write_warm_ms": 191,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/590_evolve_constraint_optimize_merge",
      "num": 590,
      "name": "evolve_constraint_optimize_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/590_evolve_constraint_optimize_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_590_evolve_constraint_optimize_merge.py",
      "description": "Schema evolution + constraint + OPTIMIZE + MERGE. Four-way feature combo. Validates that adding a column, adding a constraint, compacting files, and then running a MERGE all work together correctly.",
      "status": "pass",
      "duration_ms": 274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:02.161703+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 100,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 298,
      "write_warm_ms": 307
    },
    {
      "id": "df-writes/iceberg/591_partition_three_region_merge",
      "num": 591,
      "name": "partition_three_region_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/591_partition_three_region_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_591_partition_three_region_merge.py",
      "description": "3-region partition + MERGE that adds rows to each region. Validates that MERGE correctly handles both matched updates and unmatched inserts across all partitions evenly.",
      "status": "pass",
      "duration_ms": 301,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:02.462923+00:00",
      "read_cold_ms": 94,
      "read_warm_ms": 116,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 125,
      "write_warm_ms": 111
    },
    {
      "id": "df-writes/iceberg/592_cdc_dv_optimize_delete_update",
      "num": 592,
      "name": "cdc_dv_optimize_delete_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/592_cdc_dv_optimize_delete_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_592_cdc_dv_optimize_delete_update.py",
      "description": "CDC + DV + OPTIMIZE + DELETE + UPDATE. Full DML lifecycle with compaction. Validates that OPTIMIZE compacts DV-enabled files correctly, and that subsequent DELETE and UPDATE generate proper CDF records after compaction.",
      "status": "pass",
      "duration_ms": 236,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:02.699666+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 395,
      "write_warm_ms": 233
    },
    {
      "id": "df-writes/iceberg/593_colmap_partition_delete_merge",
      "num": 593,
      "name": "colmap_partition_delete_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/593_colmap_partition_delete_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_593_colmap_partition_delete_merge.py",
      "description": "Column mapping + partition + DELETE + MERGE. Validates that column mapping works correctly with partitioned data, and that DELETE followed by MERGE properly handles physical column IDs across partition boundaries.",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:02.909744+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 100,
      "write_warm_ms": 119
    },
    {
      "id": "df-writes/iceberg/594_constraint_two_evolve",
      "num": 594,
      "name": "constraint_two_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/594_constraint_two_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_594_constraint_two_evolve.py",
      "description": "Two constraints + two schema evolutions. Tests that multiple ADD CONSTRAINT and ADD COLUMN operations interleave correctly, and that constraints on newly-added columns are enforced properly (including NULL handling).",
      "status": "pass",
      "duration_ms": 199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:03.109697+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 103,
      "write_warm_ms": 92
    },
    {
      "id": "df-writes/iceberg/595_optimize_merge_delete_chain",
      "num": 595,
      "name": "optimize_merge_delete_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/595_optimize_merge_delete_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_595_optimize_merge_delete_chain.py",
      "description": "OPTIMIZE then MERGE then DELETE. Three-step DML chain after compaction. Validates that MERGE works correctly on optimized (compacted) files, and that a subsequent DELETE properly handles rows that were just inserted by the MERGE.",
      "status": "pass",
      "duration_ms": 273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:03.383794+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 376,
      "write_warm_ms": 375
    },
    {
      "id": "df-writes/iceberg/596_cdc_colmap_partition_merge_evolve",
      "num": 596,
      "name": "cdc_colmap_partition_merge_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/596_cdc_colmap_partition_merge_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_596_cdc_colmap_partition_merge_evolve.py",
      "description": "Five-way: CDC + column mapping + partition + MERGE + schema evolution. The most complex feature interaction test combining all major metadata features (CDC, colmap) with partitioning, schema evolution, and MERGE DML.",
      "status": "pass",
      "duration_ms": 306,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:03.690443+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 557,
      "write_warm_ms": 386
    },
    {
      "id": "df-writes/iceberg/597_dv_partition_constraint_cdc",
      "num": 597,
      "name": "dv_partition_constraint_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/597_dv_partition_constraint_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_597_dv_partition_constraint_cdc.py",
      "description": "DV + partition + constraint + CDC. Four-way feature combination. Validates that deletion vectors, partitioning, CHECK constraints, and change data feed all coexist correctly through DELETE and UPDATE operations.",
      "status": "pass",
      "duration_ms": 344,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:04.035011+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 125,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 130,
      "write_warm_ms": 111
    },
    {
      "id": "df-writes/iceberg/598_colmap_evolve_optimize_dml",
      "num": 598,
      "name": "colmap_evolve_optimize_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/598_colmap_evolve_optimize_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_598_colmap_evolve_optimize_dml.py",
      "description": "Column mapping + schema evolution + OPTIMIZE + DML. Four-way feature combo. Validates that OPTIMIZE correctly handles files with evolved schema under column mapping mode, and that subsequent DML (UPDATE + DELETE) works on the compacted, evolved table.",
      "status": "pass",
      "duration_ms": 129,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:04.164135+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 38,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 334,
      "write_warm_ms": 375
    },
    {
      "id": "df-writes/iceberg/599_merge_cdc_dv_colmap_constraint",
      "num": 599,
      "name": "merge_cdc_dv_colmap_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/599_merge_cdc_dv_colmap_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_599_merge_cdc_dv_colmap_constraint.py",
      "description": "Five-way: MERGE + CDC + DV + column mapping + constraint. Tests that all five major Delta features coexist: column mapping tracks physical IDs, DVs handle deletes, CDC captures changes, the constraint is enforced through MERGE inserts, and MERGE correctly resolves all of this.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:04.423378+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 326,
      "write_warm_ms": 409
    },
    {
      "id": "df-writes/iceberg/59_schema_all_primitive_types",
      "num": 59,
      "name": "schema_all_primitive_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/59_schema_all_primitive_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_59_schema_all_primitive_types.py",
      "description": "decimal, string, binary, boolean, date, timestamp. - Temperature (float/double), humidity (byte), pressure (short) - Device IDs (long), timestamps (timestamp), locations (string) - Binary sensor payloads, boolean status flags, date-based partitions",
      "status": "pass",
      "duration_ms": 156,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:04.579489+00:00",
      "read_cold_ms": 46,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 107,
      "write_warm_ms": 83,
      "tags": [
        "type:binary",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/600_ultimate_comprehensive",
      "num": 600,
      "name": "ultimate_comprehensive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/600_ultimate_comprehensive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_600_ultimate_comprehensive.py",
      "description": "ULTIMATE test: DV + CDC + colmap + partition + constraint + schema evolution + OPTIMIZE + MERGE + DELETE + UPDATE. All major Delta features and all DML types exercised in a single test. This is the most comprehensive integration test in the suite.",
      "status": "pass",
      "duration_ms": 286,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:04.865741+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 711,
      "write_warm_ms": 742
    },
    {
      "id": "df-writes/iceberg/601_struct_update",
      "num": 601,
      "name": "struct_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/601_struct_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_601_struct_update.py",
      "description": "STRUCT column + UPDATE on non-struct columns. Verifies that nested struct values are preserved unchanged through UPDATE and DELETE operations that only modify scalar columns.",
      "status": "pass",
      "duration_ms": 595,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:05.461565+00:00",
      "read_cold_ms": 141,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 210,
      "write_warm_ms": 137
    },
    {
      "id": "df-writes/iceberg/602_struct_merge",
      "num": 602,
      "name": "struct_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/602_struct_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_602_struct_merge.py",
      "description": "STRUCT column + MERGE. Tests that struct values survive MERGE UPDATE and that new struct values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:05.819124+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 91,
      "write_warm_ms": 65
    },
    {
      "id": "df-writes/iceberg/603_struct_cdc",
      "num": 603,
      "name": "struct_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/603_struct_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_603_struct_cdc.py",
      "description": "STRUCT column + CDC (Change Data Feed). Tests that CDF correctly captures pre/post images for rows containing nested struct data through UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 297,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:06.117010+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 62,
      "write_warm_ms": 89
    },
    {
      "id": "df-writes/iceberg/604_timestamp_merge",
      "num": 604,
      "name": "timestamp_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/604_timestamp_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_604_timestamp_merge.py",
      "description": "TIMESTAMP column + MERGE. Tests that timestamp values survive MERGE UPDATE and that new timestamp values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 215,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:06.332820+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 85,
      "write_warm_ms": 64
    },
    {
      "id": "df-writes/iceberg/605_timestamp_cdc",
      "num": 605,
      "name": "timestamp_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/605_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_605_timestamp_cdc.py",
      "description": "TIMESTAMP + CDC. Tests that CDF correctly captures pre/post images for rows containing TIMESTAMP values through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:06.563345+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 134,
      "write_warm_ms": 78
    },
    {
      "id": "df-writes/iceberg/606_date_merge",
      "num": 606,
      "name": "date_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/606_date_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_606_date_merge.py",
      "description": "DATE column + MERGE. Tests that DATE values survive MERGE UPDATE and that new DATE values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 254,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:06.817905+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 63,
      "write_warm_ms": 50
    },
    {
      "id": "df-writes/iceberg/607_date_partition",
      "num": 607,
      "name": "date_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/607_date_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_607_date_partition.py",
      "description": "DATE column + partitioning by a STRING month column. Tests DML operations (UPDATE, DELETE) on a partitioned table that also has a DATE-like partition key (event_month as STRING for partition directory naming).",
      "status": "pass",
      "duration_ms": 246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:07.064338+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 78,
      "write_warm_ms": 98
    },
    {
      "id": "df-writes/iceberg/608_decimal_merge",
      "num": 608,
      "name": "decimal_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/608_decimal_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_608_decimal_merge.py",
      "description": "DECIMAL columns + MERGE. Tests that DECIMAL precision is preserved through MERGE UPDATE and that new DECIMAL values are correctly inserted via MERGE NOT MATCHED.",
      "status": "pass",
      "duration_ms": 249,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:07.315145+00:00",
      "read_cold_ms": 101,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 82,
      "write_warm_ms": 62
    },
    {
      "id": "df-writes/iceberg/609_decimal_cdc",
      "num": 609,
      "name": "decimal_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/609_decimal_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_609_decimal_cdc.py",
      "description": "DECIMAL + CDC. Tests that CDF correctly captures pre/post images for DECIMAL(18,8) columns, including tiny fractional increments that exercise the full precision of the type.",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:07.547931+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 66,
      "write_warm_ms": 65
    },
    {
      "id": "df-writes/iceberg/60_schema_struct_type_nested",
      "num": 60,
      "name": "schema_struct_type_nested",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/60_schema_struct_type_nested.sql",
      "read_script": "generator/spark-reads-iceberg/verify_60_schema_struct_type_nested.py",
      "description": "Demonstrates nested struct types with fields. Orders contain nested customer info (name, contact) and shipping address (street, city, country). Struct types model hierarchical data naturally without flattening, maintaining data relationships and enabling efficient nested field...",
      "status": "pass",
      "duration_ms": 623,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:08.171894+00:00",
      "read_cold_ms": 23,
      "read_warm_ms": 17,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 49,
      "write_warm_ms": 50,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/610_decimal_partition",
      "num": 610,
      "name": "decimal_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/610_decimal_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_610_decimal_partition.py",
      "description": "DECIMAL columns + partition + DML. Tests DECIMAL precision through UPDATE and DELETE on a partitioned table with a STRING partition key.",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:08.402461+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 70,
      "write_warm_ms": 154
    },
    {
      "id": "df-writes/iceberg/611_no_dv_merge",
      "num": 611,
      "name": "no_dv_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/611_no_dv_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_611_no_dv_merge.py",
      "description": "No-DV table + MERGE + DELETE. Tests the full-rewrite code path for MERGE and DELETE when deletion vectors are disabled. All DML requires rewriting entire data files rather than using DVs.",
      "status": "pass",
      "duration_ms": 66,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:08.469028+00:00",
      "read_cold_ms": 24,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 225,
      "write_warm_ms": 121
    },
    {
      "id": "df-writes/iceberg/612_no_dv_cdc",
      "num": 612,
      "name": "no_dv_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/612_no_dv_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_612_no_dv_cdc.py",
      "description": "No-DV + CDC. Tests CDF recording with full-rewrite DML path (no deletion vectors). Both UPDATE and DELETE require full file rewrites, and CDF must still correctly capture pre/post images.",
      "status": "pass",
      "duration_ms": 78,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:08.547872+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 22,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 115,
      "write_warm_ms": 106
    },
    {
      "id": "df-writes/iceberg/613_no_dv_partition",
      "num": 613,
      "name": "no_dv_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/613_no_dv_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_613_no_dv_partition.py",
      "description": "No-DV + partition. Tests full-rewrite DML on a partitioned table with deletion vectors disabled. UPDATE and DELETE must rewrite entire partition files rather than using DVs.",
      "status": "pass",
      "duration_ms": 107,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:08.655319+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 128,
      "write_warm_ms": 267
    },
    {
      "id": "df-writes/iceberg/614_not_null_merge",
      "num": 614,
      "name": "not_null_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/614_not_null_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_614_not_null_merge.py",
      "description": "NOT NULL columns + MERGE + DELETE. Tests NOT NULL enforcement during MERGE INSERT (all source rows must provide non-null values for constrained columns) and NOT NULL metadata preservation through DML.",
      "status": "pass",
      "duration_ms": 264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:08.920044+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 103,
      "write_warm_ms": 89
    },
    {
      "id": "df-writes/iceberg/615_not_null_evolve",
      "num": 615,
      "name": "not_null_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/615_not_null_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_615_not_null_evolve.py",
      "description": "NOT NULL + schema evolution. Tests that NOT NULL constraints survive ALTER TABLE ADD COLUMN, and that the new nullable column works correctly alongside existing NOT NULL columns through INSERT and UPDATE.",
      "status": "pass",
      "duration_ms": 288,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:09.208996+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 125,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 109,
      "write_warm_ms": 169
    },
    {
      "id": "df-writes/iceberg/616_not_null_cdc",
      "num": 616,
      "name": "not_null_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/616_not_null_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_616_not_null_cdc.py",
      "description": "NOT NULL + CDC. Tests that CDF correctly captures pre/post images for tables with NOT NULL constraints through UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:09.421230+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 335,
      "write_warm_ms": 110
    },
    {
      "id": "df-writes/iceberg/617_insert_overwrite_merge",
      "num": 617,
      "name": "insert_overwrite_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/617_insert_overwrite_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_617_insert_overwrite_merge.py",
      "description": "INSERT OVERWRITE then MERGE then UPDATE. Tests a full DML chain after an OVERWRITE operation, which replaces all data. Verifies that MERGE and UPDATE work correctly on overwritten data.",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:09.683413+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 185,
      "write_warm_ms": 174
    },
    {
      "id": "df-writes/iceberg/618_insert_overwrite_cdc",
      "num": 618,
      "name": "insert_overwrite_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/618_insert_overwrite_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_618_insert_overwrite_cdc.py",
      "description": "INSERT OVERWRITE + CDC. Tests that CDF correctly records an OVERWRITE as delete+insert events, then verifies subsequent UPDATE is also captured in CDF.",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:09.897769+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 100,
      "write_warm_ms": 91
    },
    {
      "id": "df-writes/iceberg/619_rename_cdc",
      "num": 619,
      "name": "rename_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/619_rename_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_619_rename_cdc.py",
      "description": "RENAME COLUMN + CDC. Tests that CDF correctly captures changes after a column rename operation. Requires column mapping mode=name to support rename. Verifies that UPDATE and DELETE work on the renamed column and CDF images reference the new column name.",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:10.139544+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 126,
      "write_warm_ms": 248
    },
    {
      "id": "df-writes/iceberg/61_schema_struct_field_metadata",
      "num": 61,
      "name": "schema_struct_field_metadata",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/61_schema_struct_field_metadata.sql",
      "read_script": "generator/spark-reads-iceberg/verify_61_schema_struct_field_metadata.py",
      "description": "Demonstrates struct fields with metadata annotations. Trading records include nested position details with metadata for compliance tracking, data lineage, and schema evolution. Metadata annotations document column purposes, data sources, and transformations for regulatory audits.",
      "status": "pass",
      "duration_ms": 915,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:11.055000+00:00",
      "read_cold_ms": 37,
      "read_warm_ms": 17,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 59,
      "write_warm_ms": 79,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:struct",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/620_drop_column_dml",
      "num": 620,
      "name": "drop_column_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/620_drop_column_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_620_drop_column_dml.py",
      "description": "DROP COLUMN + subsequent DML. Tests that UPDATE and DELETE work correctly after a column has been dropped. Requires column mapping mode=name. Verifies the dropped column is absent from the final schema and that remaining columns are intact.",
      "status": "pass",
      "duration_ms": 334,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:11.389753+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 103,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 167,
      "write_warm_ms": 273
    },
    {
      "id": "df-writes/iceberg/621_rename_merge_cdc",
      "num": 621,
      "name": "rename_merge_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/621_rename_merge_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_621_rename_merge_cdc.py",
      "description": "RENAME COLUMN + MERGE + CDC (Change Data Feed). Tests that MERGE uses the renamed column name correctly under columnMapping.mode=name with CDC enabled.",
      "status": "pass",
      "duration_ms": 265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:11.655175+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 298,
      "write_warm_ms": 450
    },
    {
      "id": "df-writes/iceberg/622_drop_merge",
      "num": 622,
      "name": "drop_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/622_drop_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_622_drop_merge.py",
      "description": "DROP COLUMN + MERGE. Tests that MERGE operates correctly after a column has been dropped under columnMapping.mode=name.",
      "status": "pass",
      "duration_ms": 231,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:11.886663+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 251,
      "write_warm_ms": 77
    },
    {
      "id": "df-writes/iceberg/623_struct_partition",
      "num": 623,
      "name": "struct_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/623_struct_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_623_struct_partition.py",
      "description": "STRUCT column + partitioned table. Tests that nested struct values survive partition-aware UPDATE and DELETE operations correctly.",
      "status": "pass",
      "duration_ms": 664,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:12.551759+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 101,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 177,
      "write_warm_ms": 111
    },
    {
      "id": "df-writes/iceberg/624_struct_evolve",
      "num": 624,
      "name": "struct_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/624_struct_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_624_struct_evolve.py",
      "description": "STRUCT column + schema evolution (ADD COLUMN). Tests that existing struct values are preserved when a new scalar column is added, and that new inserts populate both struct and new column.",
      "status": "pass",
      "duration_ms": 428,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:12.980027+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 245,
      "write_warm_ms": 158
    },
    {
      "id": "df-writes/iceberg/625_timestamp_partition",
      "num": 625,
      "name": "timestamp_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/625_timestamp_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_625_timestamp_partition.py",
      "description": "TIMESTAMP column + partitioned table (partition by month STRING). Tests that timestamp values survive partition-aware UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 331,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:13.312037+00:00",
      "read_cold_ms": 159,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 133,
      "write_warm_ms": 105
    },
    {
      "id": "df-writes/iceberg/626_decimal_constraint",
      "num": 626,
      "name": "decimal_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/626_decimal_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_626_decimal_constraint.py",
      "description": "DECIMAL column + CHECK constraint (amount > 0). Tests that constraint enforcement works with DECIMAL precision types through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:13.559369+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 194,
      "write_warm_ms": 198
    },
    {
      "id": "df-writes/iceberg/627_decimal_evolve",
      "num": 627,
      "name": "decimal_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/627_decimal_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_627_decimal_evolve.py",
      "description": "DECIMAL column + schema evolution (ADD COLUMN of DECIMAL type). Tests that existing DECIMAL data is preserved after adding a new DECIMAL column, and that UPDATE and DELETE work on both old and new DECIMAL columns.",
      "status": "pass",
      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:13.761887+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 121,
      "write_warm_ms": 85
    },
    {
      "id": "df-writes/iceberg/628_not_null_partition",
      "num": 628,
      "name": "not_null_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/628_not_null_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_628_not_null_partition.py",
      "description": "NOT NULL constraint + partitioned table. Tests that NOT NULL columns are preserved correctly through partition-aware UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:13.953905+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 96,
      "write_warm_ms": 85
    },
    {
      "id": "df-writes/iceberg/629_not_null_constraint",
      "num": 629,
      "name": "not_null_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/629_not_null_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_629_not_null_constraint.py",
      "description": "NOT NULL columns + CHECK constraint on the same table. Tests that both NOT NULL invariants and CHECK constraints coexist through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:14.186660+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 181,
      "write_warm_ms": 113
    },
    {
      "id": "df-writes/iceberg/62_schema_array_type_elements",
      "num": 62,
      "name": "schema_array_type_elements",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/62_schema_array_type_elements.sql",
      "read_script": "generator/spark-reads-iceberg/verify_62_schema_array_type_elements.py",
      "description": "Demonstrates array type with element types for storing multi-valued attributes.",
      "status": "pass",
      "duration_ms": 561,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:14.748641+00:00",
      "read_cold_ms": 22,
      "read_warm_ms": 22,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 106,
      "write_warm_ms": 144,
      "tags": [
        "type:array",
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/630_insert_overwrite_partition",
      "num": 630,
      "name": "insert_overwrite_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/630_insert_overwrite_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_630_insert_overwrite_partition.py",
      "description": "INSERT OVERWRITE on a partitioned table followed by UPDATE and DELETE. Tests that OVERWRITE correctly replaces partition data and subsequent DML operates on the overwritten state.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:14.994741+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 261,
      "write_warm_ms": 353
    },
    {
      "id": "df-writes/iceberg/631_merge_update_delete_chain",
      "num": 631,
      "name": "merge_update_delete_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/631_merge_update_delete_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_631_merge_update_delete_chain.py",
      "description": "MERGE then UPDATE then DELETE chain. Different ordering from 524 (MERGE then DELETE) and 551 (DELETE+UPDATE+MERGE). Tests that UPDATE and DELETE operate correctly on data that includes both matched-updated and not-matched-inserted rows from a prior MERGE.",
      "status": "pass",
      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:15.198760+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 275,
      "write_warm_ms": 302
    },
    {
      "id": "df-writes/iceberg/632_update_merge_update",
      "num": 632,
      "name": "update_merge_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/632_update_merge_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_632_update_merge_update.py",
      "description": "UPDATE then MERGE then UPDATE (DML sandwich). Tests that a MERGE correctly reads data already modified by UPDATE, and that a subsequent UPDATE works on MERGE output (both matched and inserted rows).",
      "status": "pass",
      "duration_ms": 328,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:15.527357+00:00",
      "read_cold_ms": 117,
      "read_warm_ms": 106,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 393,
      "write_warm_ms": 176
    },
    {
      "id": "df-writes/iceberg/633_delete_merge_delete",
      "num": 633,
      "name": "delete_merge_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/633_delete_merge_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_633_delete_merge_delete.py",
      "description": "DELETE then MERGE then DELETE (DML sandwich). Tests that MERGE correctly handles a table with gaps from prior DELETE, and that a second DELETE works on the merged result.",
      "status": "pass",
      "duration_ms": 296,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:15.823974+00:00",
      "read_cold_ms": 107,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 137,
      "write_warm_ms": 206
    },
    {
      "id": "df-writes/iceberg/634_one_row_all_features",
      "num": 634,
      "name": "one_row_all_features",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/634_one_row_all_features.sql",
      "read_script": "generator/spark-reads-iceberg/verify_634_one_row_all_features.py",
      "description": "Single row with Deletion Vectors + CDC enabled. Tests all features at minimum scale: INSERT 1, UPDATE, DELETE, INSERT 1 new. Validates that DV and CDC work correctly even with a single row.",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:16.036483+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 261,
      "write_warm_ms": 162
    },
    {
      "id": "df-writes/iceberg/635_two_row_merge",
      "num": 635,
      "name": "two_row_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/635_two_row_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_635_two_row_merge.py",
      "description": "Two rows + MERGE. Minimum viable MERGE test with the smallest possible table. MERGE from a 3-row CTE: 2 matched updates + 1 new insert.",
      "status": "pass",
      "duration_ms": 198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:16.234900+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 89,
      "write_warm_ms": 99
    },
    {
      "id": "df-writes/iceberg/636_power_of_two_rows",
      "num": 636,
      "name": "power_of_two_rows",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/636_power_of_two_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_636_power_of_two_rows.py",
      "description": "Exactly 256 rows (2^8) + DML. Tests alignment boundaries that may affect Parquet row group sizing and deletion vector bitmaps.",
      "status": "pass",
      "duration_ms": 227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:16.462320+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:rowgroup-stats",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 130,
      "write_warm_ms": 209
    },
    {
      "id": "df-writes/iceberg/637_thousand_row_merge",
      "num": 637,
      "name": "thousand_row_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/637_thousand_row_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_637_thousand_row_merge.py",
      "description": "1000 rows + 1000-row MERGE with full overlap (all matched, no inserts). Scale test for MERGE correctness when every row matches.",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:16.697935+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 132,
      "write_warm_ms": 136
    },
    {
      "id": "df-writes/iceberg/638_no_dv_evolve_merge",
      "num": 638,
      "name": "no_dv_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/638_no_dv_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_638_no_dv_evolve_merge.py",
      "description": "No Deletion Vectors + schema evolution + MERGE. Tests the full-rewrite code path (no DV) combined with ADD COLUMN and MERGE that populates the new column.",
      "status": "pass",
      "duration_ms": 104,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:16.802077+00:00",
      "read_cold_ms": 19,
      "read_warm_ms": 12,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 118,
      "write_warm_ms": 122
    },
    {
      "id": "df-writes/iceberg/639_no_dv_constraint",
      "num": 639,
      "name": "no_dv_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/639_no_dv_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_639_no_dv_constraint.py",
      "description": "No Deletion Vectors + CHECK constraint + DML. Tests the full-rewrite code path (no DV) with constraint enforcement through UPDATE and DELETE.",
      "status": "pass",
      "duration_ms": 144,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:16.946260+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 13,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 268,
      "write_warm_ms": 295
    },
    {
      "id": "df-writes/iceberg/63_schema_map_type_key_value",
      "num": 63,
      "name": "schema_map_type_key_value",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/63_schema_map_type_key_value.sql",
      "read_script": "generator/spark-reads-iceberg/verify_63_schema_map_type_key_value.py",
      "description": "Demonstrates map type with key-value pairs for storing dynamic configurations.",
      "status": "pass",
      "duration_ms": 299,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:17.245525+00:00",
      "read_cold_ms": 23,
      "read_warm_ms": 15,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 234,
      "write_warm_ms": 246,
      "tags": [
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/640_rename_drop_merge",
      "num": 640,
      "name": "rename_drop_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/640_rename_drop_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_640_rename_drop_merge.py",
      "description": "RENAME COLUMN + DROP COLUMN + MERGE in one script. Tests stacked column mutations under columnMapping.mode=name followed by a MERGE that uses the renamed column and omits the dropped column.",
      "status": "pass",
      "duration_ms": 267,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:17.513079+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 166,
      "write_warm_ms": 268
    },
    {
      "id": "df-writes/iceberg/641_struct_colmap",
      "num": 641,
      "name": "struct_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/641_struct_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_641_struct_colmap.py",
      "description": "STRUCT column + column mapping (name mode). Verifies that nested struct values work correctly with physical-to-logical column name mapping through UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 431,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:17.944805+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 128,
      "write_warm_ms": 134
    },
    {
      "id": "df-writes/iceberg/642_struct_constraint",
      "num": 642,
      "name": "struct_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/642_struct_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_642_struct_constraint.py",
      "description": "STRUCT column + CHECK constraint on a non-struct scalar column. Verifies that constraints coexist with nested struct data and are enforced after being added mid-lifecycle.",
      "status": "pass",
      "duration_ms": 374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:18.319039+00:00",
      "read_cold_ms": 103,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 111,
      "write_warm_ms": 78
    },
    {
      "id": "df-writes/iceberg/643_timestamp_constraint",
      "num": 643,
      "name": "timestamp_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/643_timestamp_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_643_timestamp_constraint.py",
      "description": "TIMESTAMP column + CHECK constraint on a numeric column. Verifies that constraints work alongside timestamp data and that DML preserves both.",
      "status": "pass",
      "duration_ms": 272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:18.591223+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 198,
      "write_warm_ms": 194
    },
    {
      "id": "df-writes/iceberg/644_timestamp_evolve",
      "num": 644,
      "name": "timestamp_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/644_timestamp_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_644_timestamp_evolve.py",
      "description": "TIMESTAMP column + schema evolution (ADD COLUMN). Verifies that adding a new column works correctly when the table already contains timestamp data, and that subsequent DML operates on the evolved schema.",
      "status": "pass",
      "duration_ms": 483,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:19.075207+00:00",
      "read_cold_ms": 201,
      "read_warm_ms": 174,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 198,
      "write_warm_ms": 199
    },
    {
      "id": "df-writes/iceberg/645_decimal_colmap",
      "num": 645,
      "name": "decimal_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/645_decimal_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_645_decimal_colmap.py",
      "description": "DECIMAL(15,6) column + column mapping (name mode). Verifies that high- precision decimal values are preserved correctly through column mapping with UPDATE and DELETE operations.",
      "status": "pass",
      "duration_ms": 270,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:19.345547+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 93,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 109,
      "write_warm_ms": 139
    },
    {
      "id": "df-writes/iceberg/646_decimal_cdc_merge",
      "num": 646,
      "name": "decimal_cdc_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/646_decimal_cdc_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_646_decimal_cdc_merge.py",
      "description": "DECIMAL(15,6) + CDC + MERGE. Three-way combination testing that CDF correctly captures pre/post images for decimal columns through MERGE.",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:19.576618+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 285,
      "write_warm_ms": 351
    },
    {
      "id": "df-writes/iceberg/647_no_dv_optimize",
      "num": 647,
      "name": "no_dv_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/647_no_dv_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_647_no_dv_optimize.py",
      "description": "No deletion vectors + OPTIMIZE. Tests that OPTIMIZE compacts fragmented data files on a table without deletion vectors enabled. Data is inserted in 4 small batches to create file fragmentation, then an UPDATE creates more files, and OPTIMIZE compacts them.",
      "status": "pass",
      "duration_ms": 100,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:19.677562+00:00",
      "read_cold_ms": 38,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 309,
      "write_warm_ms": 457
    },
    {
      "id": "df-writes/iceberg/648_no_dv_evolve_cdc",
      "num": 648,
      "name": "no_dv_evolve_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/648_no_dv_evolve_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_648_no_dv_evolve_cdc.py",
      "description": "No deletion vectors + schema evolution (ADD COLUMN) + CDC. Three-way combination testing that CDF works correctly across schema changes without deletion vectors.",
      "status": "pass",
      "duration_ms": 75,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:19.753420+00:00",
      "read_cold_ms": 15,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 225,
      "write_warm_ms": 227
    },
    {
      "id": "df-writes/iceberg/649_insert_overwrite_evolve",
      "num": 649,
      "name": "insert_overwrite_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/649_insert_overwrite_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_649_insert_overwrite_evolve.py",
      "description": "INSERT OVERWRITE + schema evolution (ADD COLUMN). Tests that OVERWRITE replaces all data, then ADD COLUMN extends the schema, and a subsequent INSERT populates the new column.",
      "status": "pass",
      "duration_ms": 197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:19.951530+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 194,
      "write_warm_ms": 230
    },
    {
      "id": "df-writes/iceberg/64_schema_column_metadata_extended",
      "num": 64,
      "name": "schema_column_metadata_extended",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/64_schema_column_metadata_extended.sql",
      "read_script": "generator/spark-reads-iceberg/verify_64_schema_column_metadata_extended.py",
      "description": "Demonstrates extended column metadata for constraints and properties. Column metadata can store: - delta.columnMapping.* (physical/logical name mapping) - delta.identity.* (identity column properties) - delta.invariants (column-level constraints)",
      "status": "pass",
      "duration_ms": 1217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:21.169150+00:00",
      "read_cold_ms": 33,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 94,
      "write_warm_ms": 75,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:identity-columns",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/650_not_null_merge_cdc",
      "num": 650,
      "name": "not_null_merge_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/650_not_null_merge_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_650_not_null_merge_cdc.py",
      "description": "NOT NULL constraints + MERGE + CDC. Three-way combination testing that NOT NULL enforcement is maintained through MERGE operations while CDF correctly captures change records.",
      "status": "pass",
      "duration_ms": 292,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:21.461830+00:00",
      "read_cold_ms": 107,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 205,
      "write_warm_ms": 220
    },
    {
      "id": "df-writes/iceberg/651_rename_partition",
      "num": 651,
      "name": "rename_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/651_rename_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_651_rename_partition.py",
      "description": "RENAME COLUMN + partitioned table with column mapping. Verifies that renaming a column works correctly on a partitioned table and that subsequent DML uses the new column name.",
      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:21.744582+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 181,
      "write_warm_ms": 167
    },
    {
      "id": "df-writes/iceberg/652_rename_constraint",
      "num": 652,
      "name": "rename_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/652_rename_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_652_rename_constraint.py",
      "description": "RENAME COLUMN + CHECK constraint with column mapping. Verifies that renaming a column and then adding a constraint on a different column works correctly together.",
      "status": "pass",
      "duration_ms": 210,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:21.954888+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 72,
      "write_warm_ms": 77
    },
    {
      "id": "df-writes/iceberg/653_drop_partition",
      "num": 653,
      "name": "drop_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/653_drop_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_653_drop_partition.py",
      "description": "DROP COLUMN + partitioned table with column mapping. Verifies that dropping a non-partition column works correctly on a partitioned table and that subsequent DML operates on the reduced schema.",
      "status": "pass",
      "duration_ms": 208,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:22.163721+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 128,
      "write_warm_ms": 166
    },
    {
      "id": "df-writes/iceberg/654_drop_cdc",
      "num": 654,
      "name": "drop_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/654_drop_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_654_drop_cdc.py",
      "description": "DROP COLUMN + CDC with column mapping. Verifies that CDF correctly captures changes after a column has been dropped from the schema.",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:22.396750+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 161,
      "write_warm_ms": 96
    },
    {
      "id": "df-writes/iceberg/655_multi_rename",
      "num": 655,
      "name": "multi_rename",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/655_multi_rename.sql",
      "read_script": "generator/spark-reads-iceberg/verify_655_multi_rename.py",
      "description": "Two sequential RENAME COLUMN operations with column mapping. Verifies that multiple renames work correctly and that subsequent DML uses the final column names.",
      "status": "pass",
      "duration_ms": 304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:22.701345+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 101,
      "write_warm_ms": 298
    },
    {
      "id": "df-writes/iceberg/656_delete_not_in",
      "num": 656,
      "name": "delete_not_in",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/656_delete_not_in.sql",
      "read_script": "generator/spark-reads-iceberg/verify_656_delete_not_in.py",
      "description": "DELETE with compound range predicate simulating NOT IN behavior. Tests DELETE WHERE id<20 OR id>80, which removes rows outside the [20,80] range.",
      "status": "pass",
      "duration_ms": 123,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:22.824910+00:00",
      "read_cold_ms": 39,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 70,
      "write_warm_ms": 64
    },
    {
      "id": "df-writes/iceberg/657_update_between",
      "num": 657,
      "name": "update_between",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/657_update_between.sql",
      "read_script": "generator/spark-reads-iceberg/verify_657_update_between.py",
      "description": "UPDATE with BETWEEN predicate on different columns. Tests that BETWEEN range predicates work correctly for UPDATE operations on both id and score columns.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:23.070252+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 72,
      "write_warm_ms": 68
    },
    {
      "id": "df-writes/iceberg/658_merge_multiple_matched",
      "num": 658,
      "name": "merge_multiple_matched",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/658_merge_multiple_matched.sql",
      "read_script": "generator/spark-reads-iceberg/verify_658_merge_multiple_matched.py",
      "description": "MERGE with two WHEN MATCHED clauses using different conditions. Tests that conditional MERGE correctly applies different updates based on the matched row's data.",
      "status": "pass",
      "duration_ms": 308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:23.379249+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 110,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 56,
      "write_warm_ms": 86
    },
    {
      "id": "df-writes/iceberg/659_delete_multi_condition",
      "num": 659,
      "name": "delete_multi_condition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/659_delete_multi_condition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_659_delete_multi_condition.py",
      "description": "DELETE with complex multi-condition OR predicate on different columns. Tests that DELETE correctly evaluates compound predicates involving both score and category columns.",
      "status": "pass",
      "duration_ms": 105,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:23.484394+00:00",
      "read_cold_ms": 31,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 176,
      "write_warm_ms": 114
    },
    {
      "id": "df-writes/iceberg/65_schema_deeply_nested_complex",
      "num": 65,
      "name": "schema_deeply_nested_complex",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/65_schema_deeply_nested_complex.sql",
      "read_script": "generator/spark-reads-iceberg/verify_65_schema_deeply_nested_complex.py",
      "description": "This table is IGNORED in automated tests due to an Arrow 57.x limitation.",
      "status": "pass",
      "duration_ms": 626,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:24.110641+00:00",
      "read_cold_ms": 100,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 315,
      "write_warm_ms": 215,
      "tags": [
        "type:array",
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/660_merge_insert_overwrite",
      "num": 660,
      "name": "merge_insert_overwrite",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/660_merge_insert_overwrite.sql",
      "read_script": "generator/spark-reads-iceberg/verify_660_merge_insert_overwrite.py",
      "description": "MERGE then INSERT OVERWRITE. Tests that INSERT OVERWRITE completely replaces all data after a MERGE has been applied.",
      "status": "pass",
      "duration_ms": 64,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:24.175453+00:00",
      "read_cold_ms": 20,
      "read_warm_ms": 16,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 210,
      "write_warm_ms": 131
    },
    {
      "id": "df-writes/iceberg/661_struct_optimize",
      "num": 661,
      "name": "struct_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/661_struct_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_661_struct_optimize.py",
      "description": "STRUCT column + OPTIMIZE. INSERT 200 rows in 4 batches with named_struct, then OPTIMIZE to compact, followed by DELETE and UPDATE. Verifies struct values survive compaction and subsequent DML.",
      "status": "pass",
      "duration_ms": 899,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:25.074950+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 270,
      "write_warm_ms": 277
    },
    {
      "id": "df-writes/iceberg/662_struct_dv_cdc",
      "num": 662,
      "name": "struct_dv_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/662_struct_dv_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_662_struct_dv_cdc.py",
      "description": "STRUCT + Deletion Vectors + CDC. INSERT 100 rows with struct, DELETE using DVs, UPDATE. CDF tracks all changes. Verifies struct values survive DV-based deletes and CDC captures struct mutations correctly.",
      "status": "pass",
      "duration_ms": 434,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:25.509441+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 62,
      "write_warm_ms": 97
    },
    {
      "id": "df-writes/iceberg/663_timestamp_merge_cdc",
      "num": 663,
      "name": "timestamp_merge_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/663_timestamp_merge_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_663_timestamp_merge_cdc.py",
      "description": "TIMESTAMP + MERGE + CDC. INSERT 100 rows with timestamp columns, then MERGE from 120-row source. CDC enabled to track insert/update changes. Verifies timestamp precision is preserved through MERGE operations.",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:25.719160+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 51,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 254,
      "write_warm_ms": 505
    },
    {
      "id": "df-writes/iceberg/664_decimal_constraint_partition",
      "num": 664,
      "name": "decimal_constraint_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/664_decimal_constraint_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_664_decimal_constraint_partition.py",
      "description": "DECIMAL + CHECK constraint + partitioning. INSERT 120 rows across 3 partitions with DECIMAL columns, ADD CONSTRAINT, UPDATE, DELETE. Verifies that DECIMAL precision is preserved with constraints on partitioned data.",
      "status": "pass",
      "duration_ms": 242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:25.961744+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 178,
      "write_warm_ms": 168
    },
    {
      "id": "df-writes/iceberg/665_no_dv_colmap",
      "num": 665,
      "name": "no_dv_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/665_no_dv_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_665_no_dv_colmap.py",
      "description": "No Deletion Vectors + column mapping (name mode). enableDeletionVectors=false forces copy-on-write for DELETE/UPDATE. Column mapping=name tracks physical IDs. Verifies correct behavior without DVs under column mapping.",
      "status": "pass",
      "duration_ms": 119,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:26.080884+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 29,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 91,
      "write_warm_ms": 93
    },
    {
      "id": "df-writes/iceberg/666_insert_overwrite_cdc_partition",
      "num": 666,
      "name": "insert_overwrite_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/666_insert_overwrite_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_666_insert_overwrite_cdc_partition.py",
      "description": "INSERT OVERWRITE + CDC + partitioning. Partitioned table with CDC enabled. INSERT data, then INSERT OVERWRITE a single partition, then DML. Tests that CDF correctly captures overwrite events and partition-level replacement.",
      "status": "pass",
      "duration_ms": 295,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:26.376867+00:00",
      "read_cold_ms": 120,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 233,
      "write_warm_ms": 181
    },
    {
      "id": "df-writes/iceberg/667_not_null_constraint_evolve",
      "num": 667,
      "name": "not_null_constraint_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/667_not_null_constraint_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_667_not_null_constraint_evolve.py",
      "description": "NOT NULL constraint + CHECK constraint + schema evolution. Three-way combo: NOT NULL on existing column, CHECK constraint, then ADD COLUMN (schema evolution). Verifies constraints survive schema changes and new column defaults to NULL for existing rows.",
      "status": "pass",
      "duration_ms": 244,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:26.621068+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 170,
      "write_warm_ms": 151
    },
    {
      "id": "df-writes/iceberg/668_rename_cdc_merge",
      "num": 668,
      "name": "rename_cdc_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/668_rename_cdc_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_668_rename_cdc_merge.py",
      "description": "RENAME COLUMN + CDC + MERGE. Column mapping=name with CDC enabled. INSERT 100 rows, RENAME a column, then MERGE 120 rows using the new column name. Verifies MERGE resolves renamed columns correctly and CDF captures changes.",
      "status": "pass",
      "duration_ms": 293,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:26.914420+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 475,
      "write_warm_ms": 421
    },
    {
      "id": "df-writes/iceberg/669_drop_optimize",
      "num": 669,
      "name": "drop_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/669_drop_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_669_drop_optimize.py",
      "description": "DROP COLUMN + OPTIMIZE. Column mapping=name required for DROP. INSERT 200 rows in 4 batches, DROP a column, OPTIMIZE to compact, then DML. Verifies that OPTIMIZE correctly handles schema after column drop.",
      "status": "pass",
      "duration_ms": 412,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:27.327406+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 47,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 345,
      "write_warm_ms": 174
    },
    {
      "id": "df-writes/iceberg/66_statistics_per_file_minmax",
      "num": 66,
      "name": "statistics_per_file_minmax",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/66_statistics_per_file_minmax.sql",
      "read_script": "generator/spark-reads-iceberg/verify_66_statistics_per_file_minmax.py",
      "description": "Demonstrates per-file statistics with min/max/null counts. Statistics are stored in the add action's stats field as JSON.",
      "status": "pass",
      "duration_ms": 1812,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:29.140192+00:00",
      "read_cold_ms": 48,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 185,
      "write_warm_ms": 149,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/670_wide_mixed_types",
      "num": 670,
      "name": "wide_mixed_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/670_wide_mixed_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_670_wide_mixed_types.py",
      "description": "20 columns of mixed types + DML. Tests wide schema with diverse data types including STRING, INT, DOUBLE, BOOLEAN, DECIMAL, TIMESTAMP, DATE, BIGINT, FLOAT, SHORT. Verifies all types survive UPDATE and DELETE correctly.",
      "status": "pass",
      "duration_ms": 285,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:29.425744+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 137,
      "write_warm_ms": 100
    },
    {
      "id": "df-writes/iceberg/671_thirty_batch_insert",
      "num": 671,
      "name": "thirty_batch_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/671_thirty_batch_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_671_thirty_batch_insert.py",
      "description": "30 INSERT batches of 10 rows each (300 total) + DML. Tests extreme file fragmentation from many small commits. DELETE and UPDATE operate on the fragmented file set.",
      "status": "pass",
      "duration_ms": 384,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:29.810700+00:00",
      "read_cold_ms": 115,
      "read_warm_ms": 119,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 1771,
      "write_warm_ms": 2019
    },
    {
      "id": "df-writes/iceberg/672_merge_then_optimize",
      "num": 672,
      "name": "merge_then_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/672_merge_then_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_672_merge_then_optimize.py",
      "description": "MERGE then OPTIMIZE then DML. Tests that DML operations work correctly on data that has been through both MERGE and OPTIMIZE compaction.",
      "status": "pass",
      "duration_ms": 357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:30.168567+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 217,
      "write_warm_ms": 181
    },
    {
      "id": "df-writes/iceberg/673_struct_partition_cdc",
      "num": 673,
      "name": "struct_partition_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/673_struct_partition_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_673_struct_partition_cdc.py",
      "description": "STRUCT + partition + CDC. Three-way combination. INSERT 90 rows with struct across 3 region partitions, CDC enabled. DELETE and UPDATE test that struct values survive partitioned DML with change tracking.",
      "status": "pass",
      "duration_ms": 379,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:30.548415+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 145,
      "write_warm_ms": 113
    },
    {
      "id": "df-writes/iceberg/674_timestamp_colmap",
      "num": 674,
      "name": "timestamp_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/674_timestamp_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_674_timestamp_colmap.py",
      "description": "TIMESTAMP + column mapping (name mode). Tests that timestamp precision is preserved under column mapping, where physical column IDs differ from logical names. UPDATE and DELETE verify timestamp values survive.",
      "status": "pass",
      "duration_ms": 283,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:30.832045+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 70,
      "write_warm_ms": 185
    },
    {
      "id": "df-writes/iceberg/675_decimal_optimize",
      "num": 675,
      "name": "decimal_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/675_decimal_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_675_decimal_optimize.py",
      "description": "DECIMAL + OPTIMIZE. INSERT 200 rows in 4 batches with DECIMAL columns, OPTIMIZE to compact, then UPDATE and DELETE. Verifies DECIMAL precision survives OPTIMIZE compaction and subsequent DML.",
      "status": "pass",
      "duration_ms": 334,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:31.166978+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 281,
      "write_warm_ms": 277
    },
    {
      "id": "df-writes/iceberg/676_not_null_partition_merge",
      "num": 676,
      "name": "not_null_partition_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/676_not_null_partition_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_676_not_null_partition_merge.py",
      "description": "NOT NULL + partition + MERGE. Three-way combination. Partitioned table with NOT NULL constraints, then MERGE. Verifies NOT NULL enforcement through MERGE inserts on partitioned data.",
      "status": "pass",
      "duration_ms": 390,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:31.557205+00:00",
      "read_cold_ms": 94,
      "read_warm_ms": 154,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 131,
      "write_warm_ms": 135
    },
    {
      "id": "df-writes/iceberg/677_rename_partition_merge",
      "num": 677,
      "name": "rename_partition_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/677_rename_partition_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_677_rename_partition_merge.py",
      "description": "RENAME COLUMN + partition + MERGE. Column mapping=name with partitioning. INSERT 120 rows across 3 regions, RENAME a non-partition column, then MERGE 150 rows using the new name. Verifies MERGE handles renamed columns on partitioned tables.",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:31.808177+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 183,
      "write_warm_ms": 126
    },
    {
      "id": "df-writes/iceberg/678_drop_cdc_merge",
      "num": 678,
      "name": "drop_cdc_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/678_drop_cdc_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_678_drop_cdc_merge.py",
      "description": "DROP COLUMN + CDC + MERGE. Column mapping=name with CDC enabled. INSERT 100 rows, DROP a column, then MERGE 120 rows on the reduced schema. Verifies MERGE works correctly after column drop with CDF tracking.",
      "status": "pass",
      "duration_ms": 302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:32.110831+00:00",
      "read_cold_ms": 143,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 420,
      "write_warm_ms": 218
    },
    {
      "id": "df-writes/iceberg/679_insert_overwrite_constraint",
      "num": 679,
      "name": "insert_overwrite_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/679_insert_overwrite_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_679_insert_overwrite_constraint.py",
      "description": "INSERT OVERWRITE + CHECK constraint. INSERT initial rows, ADD CONSTRAINT requiring val>0, then INSERT OVERWRITE with all-valid data. Verifies that the constraint is enforced during overwrite and the table is fully replaced.",
      "status": "pass",
      "duration_ms": 114,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:32.225479+00:00",
      "read_cold_ms": 50,
      "read_warm_ms": 27,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 81,
      "write_warm_ms": 59
    },
    {
      "id": "df-writes/iceberg/67_partition_value_serialization_types",
      "num": 67,
      "name": "partition_value_serialization_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/67_partition_value_serialization_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_67_partition_value_serialization_types.py",
      "description": "Demonstrates partition value serialization for different data types. Partition values are serialized to strings in the add action. Rules: - null: empty string - boolean: \"true\"/\"false\" - numeric: string representation - date: \"yyyy-MM-dd\" - timestamp: \"yyyy-MM-dd HH:mm:ss.SSS",
      "status": "pass",
      "duration_ms": 3825,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:36.051556+00:00",
      "read_cold_ms": 203,
      "read_warm_ms": 294,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2163,
      "write_warm_ms": 2208,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/680_no_dv_partition_merge",
      "num": 680,
      "name": "no_dv_partition_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/680_no_dv_partition_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_680_no_dv_partition_merge.py",
      "description": "No Deletion Vectors + partition + MERGE. enableDeletionVectors=false forces copy-on-write for all DML. Partitioned table with MERGE. Verifies that MERGE correctly rewrites partition files without DVs.",
      "status": "pass",
      "duration_ms": 234,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:36.286299+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 23,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 180,
      "write_warm_ms": 104
    },
    {
      "id": "df-writes/iceberg/681_struct_rename",
      "num": 681,
      "name": "struct_rename",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/681_struct_rename.sql",
      "read_script": "generator/spark-reads-iceberg/verify_681_struct_rename.py",
      "description": "STRUCT column + RENAME non-struct column with column mapping. Verifies that struct values survive a column rename on a sibling column and that subsequent DML works correctly with the new column name.",
      "status": "pass",
      "duration_ms": 440,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:36.726826+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 59,
      "write_warm_ms": 61
    },
    {
      "id": "df-writes/iceberg/682_struct_drop",
      "num": 682,
      "name": "struct_drop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/682_struct_drop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_682_struct_drop.py",
      "description": "STRUCT column + DROP non-struct column with column mapping. Verifies that struct values survive a column drop on a sibling column and that subsequent DML works correctly on the reduced schema.",
      "status": "pass",
      "duration_ms": 386,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:37.114043+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 97,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 57,
      "write_warm_ms": 62
    },
    {
      "id": "df-writes/iceberg/683_timestamp_optimize",
      "num": 683,
      "name": "timestamp_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/683_timestamp_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_683_timestamp_optimize.py",
      "description": "TIMESTAMP column + OPTIMIZE. INSERT 200 rows in 4 batches with timestamp values, OPTIMIZE to compact, then DML. Verifies timestamp precision is preserved through compaction and subsequent operations.",
      "status": "pass",
      "duration_ms": 381,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:37.495405+00:00",
      "read_cold_ms": 125,
      "read_warm_ms": 138,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 213,
      "write_warm_ms": 133
    },
    {
      "id": "df-writes/iceberg/684_decimal_rename",
      "num": 684,
      "name": "decimal_rename",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/684_decimal_rename.sql",
      "read_script": "generator/spark-reads-iceberg/verify_684_decimal_rename.py",
      "description": "DECIMAL column + RENAME with column mapping. Verifies that DECIMAL precision is preserved after renaming a DECIMAL column and that subsequent reads use the new column name correctly.",
      "status": "pass",
      "duration_ms": 106,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:37.602449+00:00",
      "read_cold_ms": 35,
      "read_warm_ms": 28,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 37,
      "write_warm_ms": 86
    },
    {
      "id": "df-writes/iceberg/685_not_null_optimize",
      "num": 685,
      "name": "not_null_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/685_not_null_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_685_not_null_optimize.py",
      "description": "NOT NULL constraints + OPTIMIZE. INSERT 200 rows in 4 batches with NOT NULL columns, OPTIMIZE to compact, then DELETE. Verifies NOT NULL metadata survives compaction and that deleted rows are correctly removed.",
      "status": "pass",
      "duration_ms": 180,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:37.782584+00:00",
      "read_cold_ms": 54,
      "read_warm_ms": 33,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 115,
      "write_warm_ms": 254
    },
    {
      "id": "df-writes/iceberg/686_insert_overwrite_optimize",
      "num": 686,
      "name": "insert_overwrite_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/686_insert_overwrite_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_686_insert_overwrite_optimize.py",
      "description": "INSERT OVERWRITE + OPTIMIZE. Partitioned table with INSERT OVERWRITE followed by OPTIMIZE compaction, then UPDATE and DELETE. Verifies that OPTIMIZE correctly compacts files after an overwrite and DML works post-compact.",
      "status": "pass",
      "duration_ms": 271,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:38.054543+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 103,
      "write_warm_ms": 96
    },
    {
      "id": "df-writes/iceberg/687_rename_evolve",
      "num": 687,
      "name": "rename_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/687_rename_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_687_rename_evolve.py",
      "description": "RENAME COLUMN + ADD COLUMN (schema evolution) with column mapping. Verifies that a rename followed by schema evolution works correctly and that the new column defaults to NULL for existing rows.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:38.314558+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 147,
      "write_warm_ms": 249
    },
    {
      "id": "df-writes/iceberg/688_drop_evolve",
      "num": 688,
      "name": "drop_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/688_drop_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_688_drop_evolve.py",
      "description": "DROP COLUMN + ADD COLUMN (schema evolution) with column mapping. Verifies that dropping a column followed by adding a new column works correctly. The new column defaults to NULL for existing rows.",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:38.527625+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 94,
      "write_warm_ms": 66
    },
    {
      "id": "df-writes/iceberg/689_struct_decimal_mix",
      "num": 689,
      "name": "struct_decimal_mix",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/689_struct_decimal_mix.sql",
      "read_script": "generator/spark-reads-iceberg/verify_689_struct_decimal_mix.py",
      "description": "STRUCT + DECIMAL in same table. Verifies that both complex nested types (struct) and high-precision numeric types (decimal) coexist correctly through INSERT, UPDATE, and DELETE operations.",
      "status": "pass",
      "duration_ms": 371,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:38.899569+00:00",
      "read_cold_ms": 87,
      "read_warm_ms": 102,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 97,
      "write_warm_ms": 83
    },
    {
      "id": "df-writes/iceberg/68_feature_names_registry_validation",
      "num": 68,
      "name": "feature_names_registry_validation",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/68_feature_names_registry_validation.sql",
      "read_script": "generator/spark-reads-iceberg/verify_68_feature_names_registry_validation.py",
      "description": "Demonstrates valid feature names in table features registry. generatedColumns, allowColumnDefaults, changeDataFeed, columnMapping, identityColumns, deletionVectors, timestampNtz, v2Checkpoint, etc.",
      "status": "pass",
      "duration_ms": 1508,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:40.408186+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 41,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 162,
      "write_warm_ms": 192,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "dml:overwrite",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/690_timestamp_decimal_mix",
      "num": 690,
      "name": "timestamp_decimal_mix",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/690_timestamp_decimal_mix.sql",
      "read_script": "generator/spark-reads-iceberg/verify_690_timestamp_decimal_mix.py",
      "description": "TIMESTAMP + DECIMAL in same table. Verifies that timestamp precision and decimal precision coexist correctly through INSERT, UPDATE, and DELETE.",
      "status": "pass",
      "duration_ms": 294,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:40.702856+00:00",
      "read_cold_ms": 99,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 54,
      "write_warm_ms": 121
    },
    {
      "id": "df-writes/iceberg/691_five_col_partition",
      "num": 691,
      "name": "five_col_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/691_five_col_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_691_five_col_partition.py",
      "description": "Multi-column partition with 3 partition columns (region, year, quarter). Tests extreme partition fanout with DML across many partition combinations. Verifies correct file placement and data isolation per partition.",
      "status": "pass",
      "duration_ms": 298,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:41.001427+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 114,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 252,
      "write_warm_ms": 155
    },
    {
      "id": "df-writes/iceberg/692_merge_chain_three",
      "num": 692,
      "name": "merge_chain_three",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/692_merge_chain_three.sql",
      "read_script": "generator/spark-reads-iceberg/verify_692_merge_chain_three.py",
      "description": "Three sequential MERGE operations. Tests MERGE-MERGE-MERGE chain where each MERGE updates existing rows and inserts new ones. Verifies that multiple MERGE commits stack correctly in the transaction log.",
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:41.288935+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 99,
      "write_warm_ms": 195
    },
    {
      "id": "df-writes/iceberg/693_optimize_chain",
      "num": 693,
      "name": "optimize_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/693_optimize_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_693_optimize_chain.py",
      "description": "Three sequential OPTIMIZEs with DML between each. Tests repeated compaction to verify that OPTIMIZE is idempotent and that interleaved DML between compactions produces correct results.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:41.534483+00:00",
      "read_cold_ms": 30,
      "read_warm_ms": 164,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 325,
      "write_warm_ms": 313
    },
    {
      "id": "df-writes/iceberg/694_cdc_no_dv_merge",
      "num": 694,
      "name": "cdc_no_dv_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/694_cdc_no_dv_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_694_cdc_no_dv_merge.py",
      "description": "CDC + no Deletion Vectors + MERGE. Tests Change Data Feed capture through a MERGE operation when deletion vectors are disabled (copy-on-write mode). Verifies CDF events are correctly generated under copy-on-write semantics.",
      "status": "pass",
      "duration_ms": 95,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:41.630449+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 18,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 195,
      "write_warm_ms": 174
    },
    {
      "id": "df-writes/iceberg/695_struct_cdc_partition",
      "num": 695,
      "name": "struct_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/695_struct_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_695_struct_cdc_partition.py",
      "description": "STRUCT + CDC + partition. Three-way combination: struct column with Change Data Feed enabled on a partitioned table. Verifies CDF correctly captures struct values in change events across partitions.",
      "status": "pass",
      "duration_ms": 434,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:42.065217+00:00",
      "read_cold_ms": 93,
      "read_warm_ms": 113,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 130,
      "write_warm_ms": 74
    },
    {
      "id": "df-writes/iceberg/696_decimal_not_null",
      "num": 696,
      "name": "decimal_not_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/696_decimal_not_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_696_decimal_not_null.py",
      "description": "DECIMAL + NOT NULL constraints. Verifies that DECIMAL columns with NOT NULL constraints work correctly through INSERT, UPDATE, and DELETE operations.",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:42.334041+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 162,
      "write_warm_ms": 62
    },
    {
      "id": "df-writes/iceberg/697_timestamp_not_null",
      "num": 697,
      "name": "timestamp_not_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/697_timestamp_not_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_697_timestamp_not_null.py",
      "description": "TIMESTAMP + NOT NULL constraints. Verifies that TIMESTAMP columns with NOT NULL constraints work correctly through INSERT, UPDATE, and DELETE operations.",
      "status": "pass",
      "duration_ms": 309,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:42.644396+00:00",
      "read_cold_ms": 131,
      "read_warm_ms": 81,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 56,
      "write_warm_ms": 48
    },
    {
      "id": "df-writes/iceberg/698_colmap_no_dv_evolve",
      "num": 698,
      "name": "colmap_no_dv_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/698_colmap_no_dv_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_698_colmap_no_dv_evolve.py",
      "description": "Column mapping + no Deletion Vectors + schema evolution. Three-way combo: column mapping name mode with DVs disabled (copy-on-write) and ADD COLUMN. Verifies schema evolution works under copy-on-write with column mapping.",
      "status": "pass",
      "duration_ms": 132,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:42.776875+00:00",
      "read_cold_ms": 23,
      "read_warm_ms": 25,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 176,
      "write_warm_ms": 70
    },
    {
      "id": "df-writes/iceberg/699_partition_merge_delete_update",
      "num": 699,
      "name": "partition_merge_delete_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/699_partition_merge_delete_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_699_partition_merge_delete_update.py",
      "description": "Partition + MERGE + DELETE + UPDATE. All three DML types plus MERGE on a partitioned table. Verifies that all four operation types produce correct results when interleaved on a partitioned table.",
      "status": "pass",
      "duration_ms": 312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:43.089093+00:00",
      "read_cold_ms": 119,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 273,
      "write_warm_ms": 181
    },
    {
      "id": "df-writes/iceberg/69_checkpoint_schema_full_spec",
      "num": 69,
      "name": "checkpoint_schema_full_spec",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/69_checkpoint_schema_full_spec.sql",
      "read_script": "generator/spark-reads-iceberg/verify_69_checkpoint_schema_full_spec.py",
      "description": "Demonstrates full checkpoint schema specification with all action types.",
      "status": "pass",
      "duration_ms": 1364,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:44.453487+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 406,
      "write_warm_ms": 414,
      "tags": [
        "type:array",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/700_grand_finale",
      "num": 700,
      "name": "grand_finale",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/700_grand_finale.sql",
      "read_script": "generator/spark-reads-iceberg/verify_700_grand_finale.py",
      "description": "Test 700: the ultimate combination test. Combines all features that can coexist: Deletion Vectors + CDC + column mapping + partitioning + CHECK constraint + schema evolution (ADD COLUMN) + OPTIMIZE + MERGE + DELETE + UPDATE + RENAME COLUMN. Verifies that all features work...",
      "status": "pass",
      "duration_ms": 346,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:44.800490+00:00",
      "read_cold_ms": 95,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 557,
      "write_warm_ms": 764
    },
    {
      "id": "df-writes/iceberg/701_update_partition_key",
      "num": 701,
      "name": "update_partition_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/701_update_partition_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_701_update_partition_key.py",
      "description": "UPDATE that changes the partition column value, moving rows between partitions. This is a common production pattern where status changes cause rows to migrate between partitions (e.g., order status transitions). Engines must correctly handle cross-partition row movement in a...",
      "status": "pass",
      "duration_ms": 305,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:45.106068+00:00",
      "read_cold_ms": 136,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 101,
      "write_warm_ms": 178
    },
    {
      "id": "df-writes/iceberg/702_merge_composite_key",
      "num": 702,
      "name": "merge_composite_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/702_merge_composite_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_702_merge_composite_key.py",
      "description": "MERGE on a composite key (two columns). Production pattern where natural keys like (region, product_id) are used instead of a single surrogate key. The engine must correctly match on multiple join conditions in the MERGE ON clause.",
      "status": "pass",
      "duration_ms": 306,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:45.412290+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 65,
      "write_warm_ms": 159
    },
    {
      "id": "df-writes/iceberg/703_merge_scd_type2",
      "num": 703,
      "name": "merge_scd_type2",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/703_merge_scd_type2.sql",
      "read_script": "generator/spark-reads-iceberg/verify_703_merge_scd_type2.py",
      "description": "SCD Type 2 (Slowly Changing Dimension) pattern: close old records by setting is_current=false and effective_to to a cutoff date, then insert new versions of those records. This is a classic production pattern in data warehousing for maintaining history of dimension changes.",
      "status": "pass",
      "duration_ms": 247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:45.660306+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 71,
      "write_warm_ms": 133
    },
    {
      "id": "df-writes/iceberg/704_upsert_heavy",
      "num": 704,
      "name": "upsert_heavy",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/704_upsert_heavy.sql",
      "read_script": "generator/spark-reads-iceberg/verify_704_upsert_heavy.py",
      "description": "Upsert-heavy pattern: 5 sequential MERGEs simulating incremental ETL loads. Each batch overlaps partially with existing data. This stresses the engine's ability to handle many small MERGE transactions with varying overlap ratios, a common pattern in production incremental...",
      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:45.942682+00:00",
      "read_cold_ms": 94,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 141,
      "write_warm_ms": 263
    },
    {
      "id": "df-writes/iceberg/705_append_compact_cycle",
      "num": 705,
      "name": "append_compact_cycle",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/705_append_compact_cycle.sql",
      "read_script": "generator/spark-reads-iceberg/verify_705_append_compact_cycle.py",
      "description": "Append-then-compact cycle: many small INSERTs (simulating streaming micro-batches) followed by OPTIMIZE. This is the standard production pattern for streaming ingestion where micro-batches create many small files that must be compacted. Tests that OPTIMIZE correctly rewrites...",
      "status": "pass",
      "duration_ms": 64,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:46.007202+00:00",
      "read_cold_ms": 18,
      "read_warm_ms": 15,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 541,
      "write_warm_ms": 794
    },
    {
      "id": "df-writes/iceberg/706_late_arriving_data",
      "num": 706,
      "name": "late_arriving_data",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/706_late_arriving_data.sql",
      "read_script": "generator/spark-reads-iceberg/verify_706_late_arriving_data.py",
      "description": "Late-arriving data: INSERTs into older partitions after newer data already exists. This is a common production pattern for delayed event processing, where events arrive out of order due to network delays, batch reprocessing, or timezone issues. Tests that the engine correctly...",
      "status": "pass",
      "duration_ms": 314,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:46.321925+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 104,
      "write_warm_ms": 161
    },
    {
      "id": "df-writes/iceberg/707_bulk_delete_reinsert",
      "num": 707,
      "name": "bulk_delete_reinsert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/707_bulk_delete_reinsert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_707_bulk_delete_reinsert.py",
      "description": "Bulk delete + re-insert: production data correction pattern. A batch of rows is discovered to be incorrect, deleted by range, and replaced with corrected data. This tests the engine's handling of large contiguous deletes followed by inserts that reuse the same id space.",
      "status": "pass",
      "duration_ms": 493,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:46.815190+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 151,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 82,
      "write_warm_ms": 52
    },
    {
      "id": "df-writes/iceberg/708_skewed_partition",
      "num": 708,
      "name": "skewed_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/708_skewed_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_708_skewed_partition.py",
      "description": "Heavily skewed partition distribution: 95% of data in one partition, 5% in others. Tests partition pruning efficiency and DML operations on unbalanced data. Production pattern: most events are \"default\" category with rare exceptions. Engine must handle tiny-partition UPDATE and...",
      "status": "pass",
      "duration_ms": 297,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:47.113050+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 124,
      "write_warm_ms": 60
    },
    {
      "id": "df-writes/iceberg/709_sparse_columns",
      "num": 709,
      "name": "sparse_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/709_sparse_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_709_sparse_columns.py",
      "description": "Sparse table where most columns are NULL for most rows. Production pattern for wide event tables where different event types populate different columns. Tests Parquet encoding efficiency, NULL statistics, and DML operations that filter on NULL/non-NULL combinations.",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:47.313241+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:parquet-encoding",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 68,
      "write_warm_ms": 185
    },
    {
      "id": "df-writes/iceberg/70_last_checkpoint_schema_checksum",
      "num": 70,
      "name": "last_checkpoint_schema_checksum",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/70_last_checkpoint_schema_checksum.sql",
      "read_script": "generator/spark-reads-iceberg/verify_70_last_checkpoint_schema_checksum.py",
      "description": "Demonstrates last checkpoint file schema with checksum validation.",
      "status": "pass",
      "duration_ms": 1298,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:48.611711+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 167,
      "write_warm_ms": 141,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/710_all_null_columns_dml",
      "num": 710,
      "name": "all_null_columns_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/710_all_null_columns_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_710_all_null_columns_dml.py",
      "description": "Table where some columns start as ALL NULL (no non-null values in any row), then DML populates them. Production pattern: pre-allocated schema where columns are added in advance but only populated later. Tests Parquet column statistics when min/max are both null, and correct...",
      "status": "pass",
      "duration_ms": 375,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:48.987549+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:statistics",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 214,
      "write_warm_ms": 74
    },
    {
      "id": "df-writes/iceberg/711_single_column_table",
      "num": 711,
      "name": "single_column_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/711_single_column_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_711_single_column_table.py",
      "description": "Minimal table with only a single column (id). Tests that the engine handles the minimum possible schema correctly for INSERT, DELETE, and Parquet file generation. Edge case: no non-key columns to update, statistics cover only one column.",
      "status": "pass",
      "duration_ms": 142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:49.130507+00:00",
      "read_cold_ms": 37,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 88,
      "write_warm_ms": 26
    },
    {
      "id": "df-writes/iceberg/712_all_same_values",
      "num": 712,
      "name": "all_same_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/712_all_same_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_712_all_same_values.py",
      "description": "Every row has identical values (except id). This creates a statistics edge case where min=max for all non-id columns. Tests that the engine correctly writes and reads Parquet statistics when there is zero variance, and that DML operations work when predicate evaluation sees...",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:49.360725+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 69,
      "write_warm_ms": 136
    },
    {
      "id": "df-writes/iceberg/713_negative_values",
      "num": 713,
      "name": "negative_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/713_negative_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_713_negative_values.py",
      "description": "All numeric values are negative. Tests sign handling through DML operations, Parquet min/max statistics with negative ranges, and predicate evaluation with negative comparisons. Production pattern: financial systems with debit/loss columns, temperature readings below zero.",
      "status": "pass",
      "duration_ms": 285,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:49.646693+00:00",
      "read_cold_ms": 109,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 92,
      "write_warm_ms": 47
    },
    {
      "id": "df-writes/iceberg/714_zero_values",
      "num": 714,
      "name": "zero_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/714_zero_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_714_zero_values.py",
      "description": "All numeric values start as zero, then DML changes some. Tests zero-value handling, Parquet statistics where min=max=0 initially, and correct transition from all-zero to mixed values. Edge case for engines that may confuse zero with NULL or skip zero-value encoding.",
      "status": "pass",
      "duration_ms": 248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:49.895670+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 108,
      "write_warm_ms": 51
    },
    {
      "id": "df-writes/iceberg/715_update_same_value",
      "num": 715,
      "name": "update_same_value",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/715_update_same_value.sql",
      "read_script": "generator/spark-reads-iceberg/verify_715_update_same_value.py",
      "description": "UPDATE SET col=col (no actual change to the value). Tests that the engine handles no-op value assignment correctly. The UPDATE must still create a new transaction version even though the data is unchanged. Production pattern: conditional updates where the SET clause does not...",
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:50.182984+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 86,
      "write_warm_ms": 204
    },
    {
      "id": "df-writes/iceberg/716_merge_all_delete",
      "num": 716,
      "name": "merge_all_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/716_merge_all_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_716_merge_all_delete.py",
      "description": "MERGE where every matched row hits the DELETE clause (no UPDATE, no NOT MATCHED INSERT in the MERGE itself). Tests the engine's handling of MERGE-as-delete, which is a production pattern for deduplication or purge operations driven by a \"delete list\" table.",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:50.408623+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 82,
      "write_warm_ms": 83
    },
    {
      "id": "df-writes/iceberg/717_merge_source_duplicates",
      "num": 717,
      "name": "merge_source_duplicates",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/717_merge_source_duplicates.sql",
      "read_script": "generator/spark-reads-iceberg/verify_717_merge_source_duplicates.py",
      "description": "MERGE where the source CTE deduplicates via GROUP BY before merging. Production pattern: source data has duplicate keys (e.g., multiple events for the same entity) and must be deduplicated before upsert. This is the safe production pattern vs. letting the engine encounter...",
      "status": "pass",
      "duration_ms": 258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:50.669261+00:00",
      "read_cold_ms": 99,
      "read_warm_ms": 89,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 126,
      "write_warm_ms": 101
    },
    {
      "id": "df-writes/iceberg/718_incremental_schema_migration",
      "num": 718,
      "name": "incremental_schema_migration",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/718_incremental_schema_migration.sql",
      "read_script": "generator/spark-reads-iceberg/verify_718_incremental_schema_migration.py",
      "description": "Incremental schema migration: 4 schema versions over time. Production pattern for data pipeline evolution where new columns are added across releases. Each INSERT uses the schema available at that point. Earlier rows have NULL for later-added columns. Tests that the engine...",
      "status": "pass",
      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:50.872822+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 172,
      "write_warm_ms": 310
    },
    {
      "id": "df-writes/iceberg/719_delete_to_empty_then_evolve",
      "num": 719,
      "name": "delete_to_empty_then_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/719_delete_to_empty_then_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_719_delete_to_empty_then_evolve.py",
      "description": "DELETE all rows (table becomes empty), then schema evolution (ADD COLUMN), then INSERT with the new schema. Tests that the engine correctly handles an empty table state, schema changes on empty tables, and subsequent inserts after the empty+evolve sequence. Production pattern...",
      "status": "pass",
      "duration_ms": 71,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:50.944048+00:00",
      "read_cold_ms": 16,
      "read_warm_ms": 14,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 98,
      "write_warm_ms": 104
    },
    {
      "id": "df-writes/iceberg/71_parquet_type_mappings_complete",
      "num": 71,
      "name": "parquet_type_mappings_complete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/71_parquet_type_mappings_complete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_71_parquet_type_mappings_complete.py",
      "description": "Complete Delta to Parquet type mappings.",
      "status": "pass",
      "duration_ms": 944,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:51.888776+00:00",
      "read_cold_ms": 266,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 137,
      "write_warm_ms": 214,
      "tags": [
        "type:array",
        "type:binary",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/720_concurrent_style_dml",
      "num": 720,
      "name": "concurrent_style_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/720_concurrent_style_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_720_concurrent_style_dml.py",
      "description": "Simulates a concurrent-writer pattern: alternating INSERT and UPDATE on overlapping ranges. Common in multi-writer production setups where one writer appends new data while another updates existing data. Tests that the engine correctly handles interleaved DML that touches...",
      "status": "pass",
      "duration_ms": 304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:52.193770+00:00",
      "read_cold_ms": 92,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "robust:concurrent-writes",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 269,
      "write_warm_ms": 216
    },
    {
      "id": "df-writes/iceberg/721_long_string_values",
      "num": 721,
      "name": "long_string_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/721_long_string_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_721_long_string_values.py",
      "description": "Very long string values (~200+ chars per row). Tests Parquet page handling, dictionary encoding thresholds, and string column statistics when values exceed typical inline sizes.",
      "status": "pass",
      "duration_ms": 304,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:52.498577+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 123,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:parquet-encoding",
        "storage:statistics",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 140,
      "write_warm_ms": 68
    },
    {
      "id": "df-writes/iceberg/722_empty_string_partition_dml",
      "num": 722,
      "name": "empty_string_partition_dml",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/722_empty_string_partition_dml.sql",
      "read_script": "generator/spark-reads-iceberg/verify_722_empty_string_partition_dml.py",
      "description": "Empty string '' as a partition value combined with DML operations. Tests that the engine correctly handles empty-string partition values through UPDATE and DELETE, including predicates that target the '' partition specifically. Different from test 425 which tests empty strings...",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:52.760232+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:partition-spec",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 87,
      "write_warm_ms": 221
    },
    {
      "id": "df-writes/iceberg/723_null_in_all_predicates",
      "num": 723,
      "name": "null_in_all_predicates",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/723_null_in_all_predicates.sql",
      "read_script": "generator/spark-reads-iceberg/verify_723_null_in_all_predicates.py",
      "description": "DML operations using IS NULL and IS NOT NULL in every predicate. Tests null-aware predicate evaluation chains where the engine must correctly handle three-valued logic across DELETE and UPDATE operations on columns with mixed NULL/non-NULL values.",
      "status": "pass",
      "duration_ms": 291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:53.051770+00:00",
      "read_cold_ms": 125,
      "read_warm_ms": 88,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 73,
      "write_warm_ms": 61
    },
    {
      "id": "df-writes/iceberg/724_update_to_null",
      "num": 724,
      "name": "update_to_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/724_update_to_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_724_update_to_null.py",
      "description": "UPDATE that introduces NULLs into previously non-NULL columns. Tests that the engine correctly writes NULL values through UPDATE when the original data had no NULLs. This is a common production pattern when \"clearing\" fields or soft-resetting data.",
      "status": "pass",
      "duration_ms": 274,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:53.326081+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 177,
      "write_warm_ms": 65
    },
    {
      "id": "df-writes/iceberg/725_delete_leaves_one_row",
      "num": 725,
      "name": "delete_leaves_one_row",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/725_delete_leaves_one_row.sql",
      "read_script": "generator/spark-reads-iceberg/verify_725_delete_leaves_one_row.py",
      "description": "DELETE that leaves exactly 1 row in the table. Tests statistics and metadata at minimum cardinality (single-row table after bulk delete). The engine must correctly produce min/max stats, row counts, and deletion vectors when nearly all rows are removed.",
      "status": "pass",
      "duration_ms": 127,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:53.453317+00:00",
      "read_cold_ms": 44,
      "read_warm_ms": 30,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 30,
      "write_warm_ms": 27
    },
    {
      "id": "df-writes/iceberg/726_delete_leaves_zero_rows",
      "num": 726,
      "name": "delete_leaves_zero_rows",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/726_delete_leaves_zero_rows.sql",
      "read_script": "generator/spark-reads-iceberg/verify_726_delete_leaves_zero_rows.py",
      "description": "DELETE all rows without reinserting, leaving a completely empty table. Tests empty table state: zero-row Parquet files, statistics on empty data, and correct handling of a table that has data files but all rows are logically deleted via deletion vectors.",
      "status": "pass",
      "duration_ms": 247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:53.700747+00:00",
      "read_cold_ms": 27,
      "read_warm_ms": 34,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 67,
      "write_warm_ms": 28
    },
    {
      "id": "df-writes/iceberg/727_merge_doubles_table",
      "num": 727,
      "name": "merge_doubles_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/727_merge_doubles_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_727_merge_doubles_table.py",
      "description": "MERGE that doubles table size because all source rows are NOT MATCHED. Tests the pure-insert path of MERGE when no rows in the source match the target. The engine must correctly handle a MERGE where the MATCHED clause is never triggered and all rows flow through NOT MATCHED...",
      "status": "pass",
      "duration_ms": 157,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:53.858489+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 44,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 88,
      "write_warm_ms": 61
    },
    {
      "id": "df-writes/iceberg/728_merge_halves_table",
      "num": 728,
      "name": "merge_halves_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/728_merge_halves_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_728_merge_halves_table.py",
      "description": "MERGE with a DELETE clause that halves the table. Tests the WHEN MATCHED AND <condition> THEN DELETE path where half the matched rows are deleted and the other half are updated. The engine must handle conditional branching within MATCHED clauses correctly.",
      "status": "pass",
      "duration_ms": 199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:54.058470+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 46,
      "write_warm_ms": 45
    },
    {
      "id": "df-writes/iceberg/729_update_swap_columns",
      "num": 729,
      "name": "update_swap_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/729_update_swap_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_729_update_swap_columns.py",
      "description": "UPDATE SET a=b, b=a (column swap). Tests that the engine evaluates SET expressions from the pre-update row snapshot per SQL standard. If the engine incorrectly evaluates SET sequentially (a=b first, then b=a uses the new a), both columns would end up with the same value. Correct...",
      "status": "pass",
      "duration_ms": 203,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:54.262467+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 154,
      "write_warm_ms": 56
    },
    {
      "id": "df-writes/iceberg/72_schema_serialization_complete_example",
      "num": 72,
      "name": "schema_serialization_complete_example",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/72_schema_serialization_complete_example.sql",
      "read_script": "generator/spark-reads-iceberg/verify_72_schema_serialization_complete_example.py",
      "description": "Demonstrates complete schema serialization example from the Delta protocol specification. This creates the exact example schema from protocol.md: |-- a: integer (nullable = true) |-- b: struct (nullable = true) | |-- d: integer (nullable = true)",
      "status": "pass",
      "duration_ms": 430,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:54.693358+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 19,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 79,
      "write_warm_ms": 100,
      "tags": [
        "type:array",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/730_timestamp_epoch_boundary",
      "num": 730,
      "name": "timestamp_epoch_boundary",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/730_timestamp_epoch_boundary.sql",
      "read_script": "generator/spark-reads-iceberg/verify_730_timestamp_epoch_boundary.py",
      "description": "Timestamps at epoch (1970-01-01), near the 2038 boundary, and in the far future. Tests timestamp boundary handling in Parquet encoding, Delta statistics, and predicate evaluation. These are production edge cases that surface in ETL pipelines processing historical or far-future...",
      "status": "pass",
      "duration_ms": 234,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:54.928137+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:parquet-encoding",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 126,
      "write_warm_ms": 74
    },
    {
      "id": "df-writes/iceberg/731_decimal_max_precision",
      "num": 731,
      "name": "decimal_max_precision",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/731_decimal_max_precision.sql",
      "read_script": "generator/spark-reads-iceberg/verify_731_decimal_max_precision.py",
      "description": "DECIMAL(38,0) and DECIMAL(38,18) at maximum precision limits. Tests that the engine handles the largest Decimal128 values without precision loss through INSERT, UPDATE, and DELETE. These edge cases surface in financial and scientific data pipelines.",
      "status": "pass",
      "duration_ms": 228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:55.157297+00:00",
      "read_cold_ms": 95,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 97,
      "write_warm_ms": 106
    },
    {
      "id": "df-writes/iceberg/732_partition_many_values",
      "num": 732,
      "name": "partition_many_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/732_partition_many_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_732_partition_many_values.py",
      "description": "50 distinct partition values (high-cardinality partitioning). Tests that the engine handles many partitions correctly: directory layout, per-partition statistics, and targeted DELETE across specific partition subsets. This is a common production pattern with date or bucket...",
      "status": "pass",
      "duration_ms": 179,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:55.337203+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 295,
      "write_warm_ms": 290
    },
    {
      "id": "df-writes/iceberg/733_many_small_deletes",
      "num": 733,
      "name": "many_small_deletes",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/733_many_small_deletes.sql",
      "read_script": "generator/spark-reads-iceberg/verify_733_many_small_deletes.py",
      "description": "20 sequential small DELETEs (1 row each). Tests deletion vector accumulation from many individual operations. Each DELETE creates a new DV entry, and the engine must correctly compose them. This pattern occurs in production when individual record deletions (e.g., GDPR...",
      "status": "pass",
      "duration_ms": 142,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:55.479605+00:00",
      "read_cold_ms": 32,
      "read_warm_ms": 42,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 810,
      "write_warm_ms": 743
    },
    {
      "id": "df-writes/iceberg/734_many_small_updates",
      "num": 734,
      "name": "many_small_updates",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/734_many_small_updates.sql",
      "read_script": "generator/spark-reads-iceberg/verify_734_many_small_updates.py",
      "description": "20 sequential small UPDATEs (1 row each). Tests deletion vector stacking from individual UPDATE operations, where each UPDATE rewrites one row (delete old + add new). The engine must correctly stack 20 DV entries from update-style rewrites.",
      "status": "pass",
      "duration_ms": 273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:55.753477+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 86,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 1169,
      "write_warm_ms": 974
    },
    {
      "id": "df-writes/iceberg/735_merge_then_merge_then_delete",
      "num": 735,
      "name": "merge_then_merge_then_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/735_merge_then_merge_then_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_735_merge_then_merge_then_delete.py",
      "description": "Production ETL pattern: initial load, two reconciliation MERGEs, then a cleanup DELETE. Tests multi-step DML pipelines where MERGE operations stack on top of each other and a final DELETE prunes obsolete records. This is the standard load-reconcile-cleanup cycle.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:56.013601+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 93,
      "write_warm_ms": 163
    },
    {
      "id": "df-writes/iceberg/736_backfill_pattern",
      "num": 736,
      "name": "backfill_pattern",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/736_backfill_pattern.sql",
      "read_script": "generator/spark-reads-iceberg/verify_736_backfill_pattern.py",
      "description": "Production backfill: old data inserted after newer data already exists, then reconciled via MERGE. Tests that the engine handles out-of-order inserts followed by a normalizing MERGE. This pattern occurs when historical data is backfilled into a table that already has current...",
      "status": "pass",
      "duration_ms": 294,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:56.308660+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 75,
      "write_warm_ms": 191
    },
    {
      "id": "df-writes/iceberg/737_dedup_after_insert",
      "num": 737,
      "name": "dedup_after_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/737_dedup_after_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_737_dedup_after_insert.py",
      "description": "Production dedup: INSERT creates duplicates, then DELETE removes the older generation. Tests deduplication via generational markers, a common ETL pattern where duplicate records are cleaned up after a reload or retry.",
      "status": "pass",
      "duration_ms": 239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:56.548065+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 78,
      "write_warm_ms": 60
    },
    {
      "id": "df-writes/iceberg/738_partition_rebalance",
      "num": 738,
      "name": "partition_rebalance",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/738_partition_rebalance.sql",
      "read_script": "generator/spark-reads-iceberg/verify_738_partition_rebalance.py",
      "description": "Move data between partitions to rebalance. Production pattern where UPDATE changes partition column values, causing rows to migrate across partitions. Tests cross-partition row movement through multiple UPDATE operations that redistribute data.",
      "status": "pass",
      "duration_ms": 191,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:56.739216+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 155,
      "write_warm_ms": 94
    },
    {
      "id": "df-writes/iceberg/739_cdc_production_etl",
      "num": 739,
      "name": "cdc_production_etl",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/739_cdc_production_etl.sql",
      "read_script": "generator/spark-reads-iceberg/verify_739_cdc_production_etl.py",
      "description": "CDC-enabled table through a realistic ETL cycle: load, transform, correct, add late-arriving data, and optimize. Tests that Change Data Capture metadata (_change_type, _commit_version, _commit_timestamp) is correctly maintained through multiple DML operations on a CDF- enabled...",
      "status": "pass",
      "duration_ms": 394,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:57.134118+00:00",
      "read_cold_ms": 160,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 83,
      "write_warm_ms": 72
    },
    {
      "id": "df-writes/iceberg/73_schema_serialization_complete_example_v2",
      "num": 73,
      "name": "schema_serialization_complete_example_v2",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/73_schema_serialization_complete_example_v2.sql",
      "read_script": "generator/spark-reads-iceberg/verify_73_schema_serialization_complete_example_v2.py",
      "description": "Validates the simplified v2 of the schema serialization example. 2 sensor records (1001, 1002) with 5 columns: a, b, c, e, f. This is a minimal test with reduced data.",
      "status": "pass",
      "duration_ms": 332,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:57.467299+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 110,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 127,
      "write_warm_ms": 39,
      "tags": [
        "type:array",
        "type:integer",
        "type:map",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/740_evolve_production_migration",
      "num": 740,
      "name": "evolve_production_migration",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/740_evolve_production_migration.sql",
      "read_script": "generator/spark-reads-iceberg/verify_740_evolve_production_migration.py",
      "description": "Schema migration pattern: add columns, backfill them, then continue normal DML. Tests schema evolution through ALTER TABLE ADD COLUMN followed by UPDATE to backfill existing rows. This is the standard production migration where new columns are added and populated retroactively.",
      "status": "pass",
      "duration_ms": 291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:57.759190+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 181,
      "write_warm_ms": 231
    },
    {
      "id": "df-writes/iceberg/741_merge_not_matched_by_source_delete",
      "num": 741,
      "name": "merge_not_matched_by_source_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/741_merge_not_matched_by_source_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_741_merge_not_matched_by_source_delete.py",
      "description": "MERGE with WHEN NOT MATCHED BY SOURCE THEN DELETE. Verifies that target rows with no matching source row are deleted by the NOT MATCHED BY SOURCE clause, while matched rows are updated normally.",
      "status": "pass",
      "duration_ms": 186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:57.945355+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 167,
      "write_warm_ms": 64
    },
    {
      "id": "df-writes/iceberg/742_merge_not_matched_by_source_update",
      "num": 742,
      "name": "merge_not_matched_by_source_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/742_merge_not_matched_by_source_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_742_merge_not_matched_by_source_update.py",
      "description": "MERGE with WHEN NOT MATCHED BY SOURCE THEN UPDATE. Marks unmatched target rows as 'orphaned' instead of deleting them.",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:58.185565+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 53,
      "write_warm_ms": 80
    },
    {
      "id": "df-writes/iceberg/743_merge_all_four_clauses",
      "num": 743,
      "name": "merge_all_four_clauses",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/743_merge_all_four_clauses.sql",
      "read_script": "generator/spark-reads-iceberg/verify_743_merge_all_four_clauses.py",
      "description": "MERGE with all four clause types: MATCHED UPDATE, MATCHED DELETE, NOT MATCHED INSERT, and NOT MATCHED BY SOURCE DELETE. This exercises the full MERGE capability in a single statement.",
      "status": "pass",
      "duration_ms": 199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:58.384763+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 57,
      "write_warm_ms": 135
    },
    {
      "id": "df-writes/iceberg/744_merge_not_matched_by_source_conditional",
      "num": 744,
      "name": "merge_not_matched_by_source_conditional",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/744_merge_not_matched_by_source_conditional.sql",
      "read_script": "generator/spark-reads-iceberg/verify_744_merge_not_matched_by_source_conditional.py",
      "description": "MERGE with conditional WHEN NOT MATCHED BY SOURCE. Low-score orphan rows are deleted; remaining orphan rows are deactivated. Tests that conditions on the NOT MATCHED BY SOURCE clause filter correctly.",
      "status": "pass",
      "duration_ms": 259,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:58.644867+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 66,
      "write_warm_ms": 122
    },
    {
      "id": "df-writes/iceberg/745_merge_nmbys_partition",
      "num": 745,
      "name": "merge_nmbys_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/745_merge_nmbys_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_745_merge_nmbys_partition.py",
      "description": "WHEN NOT MATCHED BY SOURCE on a partitioned table. Tests that the NM-BY-SOURCE clause correctly deletes rows across multiple partitions.",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:58.914004+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 151,
      "write_warm_ms": 83
    },
    {
      "id": "df-writes/iceberg/746_merge_nmbys_cdc",
      "num": 746,
      "name": "merge_nmbys_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/746_merge_nmbys_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_746_merge_nmbys_cdc.py",
      "description": "WHEN NOT MATCHED BY SOURCE + CDC enabled. Tests that Change Data Feed correctly captures NM-BY-SOURCE delete events alongside matched updates.",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:59.154492+00:00",
      "read_cold_ms": 90,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 127,
      "write_warm_ms": 222
    },
    {
      "id": "df-writes/iceberg/747_constraint_violation_insert",
      "num": 747,
      "name": "constraint_violation_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/747_constraint_violation_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_747_constraint_violation_insert.py",
      "description": "Constraint lifecycle: ADD CONSTRAINT, insert valid data, DROP CONSTRAINT, then insert data that would have violated the old constraint. Verifies that the dropped constraint no longer blocks inserts.",
      "status": "pass",
      "duration_ms": 115,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:59.270628+00:00",
      "read_cold_ms": 41,
      "read_warm_ms": 31,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 112,
      "write_warm_ms": 120
    },
    {
      "id": "df-writes/iceberg/748_constraint_violation_update",
      "num": 748,
      "name": "constraint_violation_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/748_constraint_violation_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_748_constraint_violation_update.py",
      "description": "Constraint DROP then UPDATE to previously-violating values. Verifies that a dropped constraint no longer blocks UPDATE operations that would have produced invalid data.",
      "status": "pass",
      "duration_ms": 237,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:59.508453+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 93,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 130,
      "write_warm_ms": 111
    },
    {
      "id": "df-writes/iceberg/749_constraint_violation_merge",
      "num": 749,
      "name": "constraint_violation_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/749_constraint_violation_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_749_constraint_violation_merge.py",
      "description": "Constraint DROP then MERGE with data that would have violated the old constraint. Verifies that MERGE can insert violating rows after the constraint is removed.",
      "status": "pass",
      "duration_ms": 227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:27:59.736418+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 244,
      "write_warm_ms": 212
    },
    {
      "id": "df-writes/iceberg/74_type_widening_safe_column_promotion",
      "num": 74,
      "name": "type_widening_safe_column_promotion",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/74_type_widening_safe_column_promotion.sql",
      "read_script": "generator/spark-reads-iceberg/verify_74_type_widening_safe_column_promotion.py",
      "description": "Type widening for safe column type promotion.",
      "status": "pass",
      "duration_ms": 2356,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:02.092626+00:00",
      "read_cold_ms": 51,
      "read_warm_ms": 26,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 149,
      "write_warm_ms": 78,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "delta:column-mapping",
        "iceberg:uniform",
        "schema:type-widening",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/750_rename_multiple_columns",
      "num": 750,
      "name": "rename_multiple_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/750_rename_multiple_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_750_rename_multiple_columns.py",
      "description": "Renaming 3 columns in sequence with column mapping (name mode). Tests multiple column mapping mutations followed by DML that uses the new column names.",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:02.343806+00:00",
      "read_cold_ms": 92,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 221,
      "write_warm_ms": 144,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/751_drop_multiple_columns",
      "num": 751,
      "name": "drop_multiple_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/751_drop_multiple_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_751_drop_multiple_columns.py",
      "description": "Dropping 2 columns in sequence with column mapping. Tests that multiple column drops are correctly tracked and that subsequent DML operates on the reduced schema.",
      "status": "pass",
      "duration_ms": 239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:02.583377+00:00",
      "write_cold_ms": 94,
      "write_warm_ms": 117,
      "read_cold_ms": 80,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/752_rename_then_drop",
      "num": 752,
      "name": "rename_then_drop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/752_rename_then_drop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_752_rename_then_drop.py",
      "description": "RENAME one column then DROP another, followed by DML and MERGE. Tests stacked column mapping mutations with subsequent complex DML.",
      "status": "pass",
      "duration_ms": 229,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:02.813524+00:00",
      "write_cold_ms": 113,
      "write_warm_ms": 195,
      "read_cold_ms": 64,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/753_drop_then_rename",
      "num": 753,
      "name": "drop_then_rename",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/753_drop_then_rename.sql",
      "read_script": "generator/spark-reads-iceberg/verify_753_drop_then_rename.py",
      "description": "DROP a column then RENAME another. Reverse order from test 752. Tests that column mapping tracks drops and renames correctly regardless of operation order.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:03.066996+00:00",
      "write_cold_ms": 84,
      "write_warm_ms": 219,
      "read_cold_ms": 88,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/754_rename_drop_add",
      "num": 754,
      "name": "rename_drop_add",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/754_rename_drop_add.sql",
      "read_script": "generator/spark-reads-iceberg/verify_754_rename_drop_add.py",
      "description": "RENAME + DROP + ADD COLUMN in sequence. Tests the full column mutation lifecycle: rename an existing column, drop another, then add a new one.",
      "status": "pass",
      "duration_ms": 211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:03.278833+00:00",
      "write_cold_ms": 155,
      "write_warm_ms": 192,
      "read_cold_ms": 61,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/755_cdc_exact_counts",
      "num": 755,
      "name": "cdc_exact_counts",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/755_cdc_exact_counts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_755_cdc_exact_counts.py",
      "description": "CDC test designed for exact CDF row count verification. Each DML version produces a precise, predictable number of Change Data Feed rows, enabling verify scripts to assert exact CDF counts per change type.",
      "status": "pass",
      "duration_ms": 238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:03.517406+00:00",
      "write_cold_ms": 170,
      "write_warm_ms": 99,
      "read_cold_ms": 60,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/756_cdc_merge_exact_counts",
      "num": 756,
      "name": "cdc_merge_exact_counts",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/756_cdc_merge_exact_counts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_756_cdc_merge_exact_counts.py",
      "description": "CDC + MERGE with exact CDF count verification. The MERGE produces a precise mix of matched updates and not-matched inserts, enabling exact CDF row count assertions.",
      "status": "pass",
      "duration_ms": 190,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:03.708097+00:00",
      "write_cold_ms": 127,
      "write_warm_ms": 212,
      "read_cold_ms": 71,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/757_cdc_nmbys_exact_counts",
      "num": 757,
      "name": "cdc_nmbys_exact_counts",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/757_cdc_nmbys_exact_counts.sql",
      "read_script": "generator/spark-reads-iceberg/verify_757_cdc_nmbys_exact_counts.py",
      "description": "CDC + WHEN NOT MATCHED BY SOURCE with exact CDF counts. Tests that Change Data Feed correctly captures NM-BY-SOURCE delete events with precise row counts.",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:03.917668+00:00",
      "write_cold_ms": 132,
      "write_warm_ms": 177,
      "read_cold_ms": 64,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/758_colmap_rename_merge_cdc",
      "num": 758,
      "name": "colmap_rename_merge_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/758_colmap_rename_merge_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_758_colmap_rename_merge_cdc.py",
      "description": "Column mapping + RENAME COLUMN + MERGE + CDC. Tests that Change Data Feed uses the renamed column name (not the original) in CDF output.",
      "status": "pass",
      "duration_ms": 490,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:04.408405+00:00",
      "write_cold_ms": 359,
      "write_warm_ms": 410,
      "read_cold_ms": 313,
      "read_warm_ms": 75,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/759_colmap_drop_evolve_merge",
      "num": 759,
      "name": "colmap_drop_evolve_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/759_colmap_drop_evolve_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_759_colmap_drop_evolve_merge.py",
      "description": "Column mapping + DROP COLUMN + ADD COLUMN + MERGE. Tests MERGE across combined column mutation (drop) and schema evolution (add).",
      "status": "pass",
      "duration_ms": 308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:04.716766+00:00",
      "write_cold_ms": 151,
      "write_warm_ms": 143,
      "read_cold_ms": 75,
      "read_warm_ms": 135,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/75_coordinated_commits_managed_transactions",
      "num": 75,
      "name": "coordinated_commits_managed_transactions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/75_coordinated_commits_managed_transactions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_75_coordinated_commits_managed_transactions.py",
      "description": "Coordinated commits via external commit coordinator.",
      "status": "pass",
      "duration_ms": 2392,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:07.109727+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 50,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 117,
      "write_warm_ms": 142,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/760_merge_nmbys_evolve_cdc",
      "num": 760,
      "name": "merge_nmbys_evolve_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/760_merge_nmbys_evolve_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_760_merge_nmbys_evolve_cdc.py",
      "description": "MERGE with NOT MATCHED BY SOURCE + schema evolution + CDC. Complex combination test: add a new column, then use MERGE with NM-BY-SOURCE to populate it differently for matched vs unmatched rows.",
      "status": "pass",
      "duration_ms": 197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:07.307583+00:00",
      "write_cold_ms": 268,
      "write_warm_ms": 173,
      "read_cold_ms": 68,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/761_merge_nmbys_update_delete",
      "num": 761,
      "name": "merge_nmbys_update_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/761_merge_nmbys_update_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_761_merge_nmbys_update_delete.py",
      "description": "MERGE with WHEN NOT MATCHED BY SOURCE having both UPDATE and DELETE conditions. Tests that the engine correctly branches NM-BY-SOURCE rows into UPDATE or DELETE based on conditional predicates.",
      "status": "pass",
      "duration_ms": 400,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:07.708020+00:00",
      "write_cold_ms": 63,
      "write_warm_ms": 162,
      "read_cold_ms": 96,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/762_merge_nmbys_colmap",
      "num": 762,
      "name": "merge_nmbys_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/762_merge_nmbys_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_762_merge_nmbys_colmap.py",
      "description": "NM-BY-SOURCE + column mapping (name mode). Tests that WHEN NOT MATCHED BY SOURCE DELETE works correctly when column mapping is enabled, ensuring physical/logical name indirection does not break source row identification.",
      "status": "pass",
      "duration_ms": 195,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:07.903870+00:00",
      "write_cold_ms": 92,
      "write_warm_ms": 81,
      "read_cold_ms": 60,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/763_merge_nmbys_constraint",
      "num": 763,
      "name": "merge_nmbys_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/763_merge_nmbys_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_763_merge_nmbys_constraint.py",
      "description": "NM-BY-SOURCE + CHECK constraint. Tests that the WHEN NOT MATCHED BY SOURCE UPDATE clause respects active CHECK constraints. The NM-BY-SOURCE UPDATE sets score=0, which must satisfy the constraint score>=0.",
      "status": "pass",
      "duration_ms": 186,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:08.090472+00:00",
      "write_cold_ms": 105,
      "write_warm_ms": 97,
      "read_cold_ms": 66,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/764_merge_nmbys_optimize",
      "num": 764,
      "name": "merge_nmbys_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/764_merge_nmbys_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_764_merge_nmbys_optimize.py",
      "description": "NM-BY-SOURCE MERGE after OPTIMIZE. Tests that MERGE correctly identifies not-matched-by-source rows when operating on compacted Parquet files (post-OPTIMIZE), which changes the physical file layout.",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:08.331842+00:00",
      "write_cold_ms": 250,
      "write_warm_ms": 359,
      "read_cold_ms": 61,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/765_constraint_add_valid_drop_violate_add",
      "num": 765,
      "name": "constraint_add_valid_drop_violate_add",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/765_constraint_add_valid_drop_violate_add.sql",
      "read_script": "generator/spark-reads-iceberg/verify_765_constraint_add_valid_drop_violate_add.py",
      "description": "Constraint lifecycle: add -> valid data -> drop -> violating data -> re-add a different constraint. Tests that dropping a constraint truly removes enforcement, and that a new constraint only validates current data.",
      "status": "pass",
      "duration_ms": 197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:08.528980+00:00",
      "write_cold_ms": 228,
      "write_warm_ms": 165,
      "read_cold_ms": 54,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/766_constraint_multiple_add_drop",
      "num": 766,
      "name": "constraint_multiple_add_drop",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/766_constraint_multiple_add_drop.sql",
      "read_script": "generator/spark-reads-iceberg/verify_766_constraint_multiple_add_drop.py",
      "description": "Complex constraint metadata evolution: add 3 constraints, drop 2, add 1 new. Tests that the metadata correctly tracks which constraints are active after multiple add/drop operations, and that new data only needs to satisfy the remaining active constraints.",
      "status": "pass",
      "duration_ms": 130,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:08.659882+00:00",
      "write_cold_ms": 265,
      "write_warm_ms": 249,
      "read_cold_ms": 32,
      "read_warm_ms": 36,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/767_cdc_delete_exact",
      "num": 767,
      "name": "cdc_delete_exact",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/767_cdc_delete_exact.sql",
      "read_script": "generator/spark-reads-iceberg/verify_767_cdc_delete_exact.py",
      "description": "CDC with exactly N deletes. Verifies that the exact delete count appears in the Change Data Feed output. Uses explicit id list for precise control over which rows are deleted.",
      "status": "pass",
      "duration_ms": 160,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:08.820771+00:00",
      "write_cold_ms": 96,
      "write_warm_ms": 74,
      "read_cold_ms": 42,
      "read_warm_ms": 48,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/768_cdc_update_exact",
      "num": 768,
      "name": "cdc_update_exact",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/768_cdc_update_exact.sql",
      "read_script": "generator/spark-reads-iceberg/verify_768_cdc_update_exact.py",
      "description": "CDC with exactly N updates. Verifies that the exact update preimage and postimage counts appear in the Change Data Feed output.",
      "status": "pass",
      "duration_ms": 302,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:09.123749+00:00",
      "write_cold_ms": 87,
      "write_warm_ms": 84,
      "read_cold_ms": 76,
      "read_warm_ms": 152,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/769_cdc_mixed_exact",
      "num": 769,
      "name": "cdc_mixed_exact",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/769_cdc_mixed_exact.sql",
      "read_script": "generator/spark-reads-iceberg/verify_769_cdc_mixed_exact.py",
      "description": "CDC with mixed DML operations, each producing an exact predictable number of CDF rows. Tests INSERT + INSERT + UPDATE + DELETE in sequence, verifying every CDF change type count is precise.",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:09.331881+00:00",
      "write_cold_ms": 203,
      "write_warm_ms": 287,
      "read_cold_ms": 64,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/76_coordinated_commits_managed_transactions_simple",
      "num": 76,
      "name": "coordinated_commits_managed_transactions_simple",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/76_coordinated_commits_managed_transactions_simple.sql",
      "read_script": "generator/spark-reads-iceberg/verify_76_coordinated_commits_managed_transactions_simple.py",
      "description": "Proof 75 (simple): coordinated_commits_managed_transactions_simple -- Iceberg Data Verification (PySpark+Iceberg)",
      "status": "pass",
      "duration_ms": 945,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:10.277181+00:00",
      "read_cold_ms": 40,
      "read_warm_ms": 37,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 78,
      "write_warm_ms": 65,
      "tags": [
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/770_rename_nmbys_merge",
      "num": 770,
      "name": "rename_nmbys_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/770_rename_nmbys_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_770_rename_nmbys_merge.py",
      "description": "RENAME COLUMN + NM-BY-SOURCE MERGE under column mapping. Tests that NM-BY-SOURCE correctly uses the renamed column name after ALTER TABLE RENAME COLUMN, ensuring physical/logical name indirection works.",
      "status": "pass",
      "duration_ms": 227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:10.504367+00:00",
      "write_cold_ms": 194,
      "write_warm_ms": 138,
      "read_cold_ms": 76,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/771_drop_nmbys_merge",
      "num": 771,
      "name": "drop_nmbys_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/771_drop_nmbys_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_771_drop_nmbys_merge.py",
      "description": "DROP COLUMN + NM-BY-SOURCE MERGE under column mapping. Tests that NM-BY-SOURCE correctly operates after a column has been dropped, ensuring the remaining columns are correctly matched and written.",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:10.746450+00:00",
      "write_cold_ms": 133,
      "write_warm_ms": 170,
      "read_cold_ms": 78,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/772_evolve_nmbys_merge",
      "num": 772,
      "name": "evolve_nmbys_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/772_evolve_nmbys_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_772_evolve_nmbys_merge.py",
      "description": "ADD COLUMN + NM-BY-SOURCE MERGE. Tests that NM-BY-SOURCE correctly handles rows that were written before the column was added (those rows have NULL for the new column), and that the MERGE UPDATE populates the new column for matched rows.",
      "status": "pass",
      "duration_ms": 276,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:11.023512+00:00",
      "write_cold_ms": 120,
      "write_warm_ms": 130,
      "read_cold_ms": 109,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/773_partition_nmbys_cdc",
      "num": 773,
      "name": "partition_nmbys_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/773_partition_nmbys_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_773_partition_nmbys_cdc.py",
      "description": "Partition + NM-BY-SOURCE + CDC. Three-way feature combination. Tests that NM-BY-SOURCE DELETE correctly generates CDF delete events across multiple partitions, and that partition pruning does not skip unmatched source rows.",
      "status": "pass",
      "duration_ms": 427,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:11.451387+00:00",
      "write_cold_ms": 348,
      "write_warm_ms": 393,
      "read_cold_ms": 75,
      "read_warm_ms": 78,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/774_constraint_nmbys_merge",
      "num": 774,
      "name": "constraint_nmbys_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/774_constraint_nmbys_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_774_constraint_nmbys_merge.py",
      "description": "Constraint + NM-BY-SOURCE MERGE. Tests that NM-BY-SOURCE UPDATE must respect active CHECK constraints. The NM-BY-SOURCE UPDATE sets value=1.0, which satisfies the constraint value > 0.",
      "status": "pass",
      "duration_ms": 245,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:11.697366+00:00",
      "write_cold_ms": 124,
      "write_warm_ms": 257,
      "read_cold_ms": 72,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/775_cdc_partition_nmbys",
      "num": 775,
      "name": "cdc_partition_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/775_cdc_partition_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_775_cdc_partition_nmbys.py",
      "description": "CDC + partition + NM-BY-SOURCE UPDATE. Tests that CDF correctly records update_preimage and update_postimage events for NM-BY-SOURCE UPDATE across multiple partitions. Unlike 773 which deletes unmatched rows, this test updates them to status='stale'.",
      "status": "pass",
      "duration_ms": 369,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:12.067451+00:00",
      "write_cold_ms": 521,
      "write_warm_ms": 615,
      "read_cold_ms": 176,
      "read_warm_ms": 100,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/776_large_nmbys",
      "num": 776,
      "name": "large_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/776_large_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_776_large_nmbys.py",
      "description": "Large-scale NM-BY-SOURCE: 1000-row target, 200-row source. 800 rows deleted by NM-BY-SOURCE. Stress-tests the engine's ability to handle large NM-BY-SOURCE DELETE batches efficiently.",
      "status": "pass",
      "duration_ms": 218,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:12.285925+00:00",
      "write_cold_ms": 131,
      "write_warm_ms": 105,
      "read_cold_ms": 74,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/777_constraint_enforcement_chain",
      "num": 777,
      "name": "constraint_enforcement_chain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/777_constraint_enforcement_chain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_777_constraint_enforcement_chain.py",
      "description": "Chain of DML operations all respecting a CHECK constraint. Tests that UPDATE, DELETE, MERGE, and a second UPDATE all enforce the constraint across multiple versions. Ensures constraint is validated on every write.",
      "status": "pass",
      "duration_ms": 303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:12.589581+00:00",
      "write_cold_ms": 323,
      "write_warm_ms": 361,
      "read_cold_ms": 84,
      "read_warm_ms": 113,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/778_colmap_rename_partition_cdc",
      "num": 778,
      "name": "colmap_rename_partition_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/778_colmap_rename_partition_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_778_colmap_rename_partition_cdc.py",
      "description": "Column mapping + RENAME COLUMN + partition + CDC. Four-way feature combination with rename. Tests that CDF events use the renamed column name, and that partition-aware operations still work after rename under column mapping.",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:12.814951+00:00",
      "write_cold_ms": 155,
      "write_warm_ms": 267,
      "read_cold_ms": 60,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/779_colmap_drop_constraint_merge",
      "num": 779,
      "name": "colmap_drop_constraint_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/779_colmap_drop_constraint_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_779_colmap_drop_constraint_merge.py",
      "description": "Column mapping + DROP COLUMN + constraint + MERGE. Four-way feature combination. Tests that MERGE respects CHECK constraints after a column has been dropped under column mapping, and that the dropped column does not interfere with constraint evaluation or merge logic.",
      "status": "pass",
      "duration_ms": 211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:13.027024+00:00",
      "write_cold_ms": 194,
      "write_warm_ms": 81,
      "read_cold_ms": 63,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/77_in_commit_timestamps_reliable_time_travel",
      "num": 77,
      "name": "in_commit_timestamps_reliable_time_travel",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/77_in_commit_timestamps_reliable_time_travel.sql",
      "read_script": "generator/spark-reads-iceberg/verify_77_in_commit_timestamps_reliable_time_travel.py",
      "description": "In-commit timestamps for reliable time travel queries.",
      "status": "pass",
      "duration_ms": 3660,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:16.687991+00:00",
      "read_cold_ms": 121,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 284,
      "write_warm_ms": 447,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/780_nmbys_all_features",
      "num": 780,
      "name": "nmbys_all_features",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/780_nmbys_all_features.sql",
      "read_script": "generator/spark-reads-iceberg/verify_780_nmbys_all_features.py",
      "description": "NM-BY-SOURCE + DV + CDC + partition + constraint. Five-way stress test with conditional NM-BY-SOURCE (both UPDATE and DELETE branches). Tests the most complex feature interaction: partitioned CDC table with active constraint, deletion vectors, and conditional NM-BY-SOURCE logic.",
      "status": "pass",
      "duration_ms": 324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:17.012562+00:00",
      "write_cold_ms": 445,
      "write_warm_ms": 436,
      "read_cold_ms": 102,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/781_cdc_nmbys_colmap",
      "num": 781,
      "name": "cdc_nmbys_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/781_cdc_nmbys_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_781_cdc_nmbys_colmap.py",
      "description": "CDC + NOT MATCHED BY SOURCE + column mapping (name mode). Three-way combination: CDF must use logical column names from the column mapping, and NM-BY-SOURCE DELETE must produce correct CDF delete events.",
      "status": "pass",
      "duration_ms": 263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:17.275895+00:00",
      "write_cold_ms": 235,
      "write_warm_ms": 235,
      "read_cold_ms": 97,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/782_nmbys_evolve_partition",
      "num": 782,
      "name": "nmbys_evolve_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/782_nmbys_evolve_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_782_nmbys_evolve_partition.py",
      "description": "NOT MATCHED BY SOURCE + schema evolution + partitioning. Three-way combination: partitioned table gets a new column via ALTER TABLE, then MERGE with NM-BY-SOURCE uses the new column. Verifies that schema evolution interacts correctly with partitioned NM-BY-SOURCE.",
      "status": "pass",
      "duration_ms": 362,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:17.638749+00:00",
      "read_cold_ms": 114,
      "read_warm_ms": 89,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 162,
      "write_warm_ms": 151
    },
    {
      "id": "df-writes/iceberg/783_constraint_two_violations",
      "num": 783,
      "name": "constraint_two_violations",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/783_constraint_two_violations.sql",
      "read_script": "generator/spark-reads-iceberg/verify_783_constraint_two_violations.py",
      "description": "Two CHECK constraints added, one dropped, then data inserted that violates the dropped constraint but satisfies the remaining one. Verifies that DROP CONSTRAINT only removes the targeted constraint and the other remains enforced.",
      "status": "pass",
      "duration_ms": 171,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:17.811144+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 131,
      "write_warm_ms": 205
    },
    {
      "id": "df-writes/iceberg/784_rename_three_plus_merge",
      "num": 784,
      "name": "rename_three_plus_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/784_rename_three_plus_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_784_rename_three_plus_merge.py",
      "description": "Three consecutive RENAME COLUMN operations followed by a MERGE that uses all the new column names. Verifies that column mapping tracks multiple renames correctly and MERGE references the final names.",
      "status": "pass",
      "duration_ms": 258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:18.069900+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 153,
      "write_warm_ms": 134
    },
    {
      "id": "df-writes/iceberg/785_drop_two_plus_merge",
      "num": 785,
      "name": "drop_two_plus_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/785_drop_two_plus_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_785_drop_two_plus_merge.py",
      "description": "Two DROP COLUMN operations followed by a MERGE on the reduced schema. Verifies that column mapping correctly handles multiple column drops and that MERGE operates on the surviving columns only.",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:18.310365+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:drop-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 136,
      "write_warm_ms": 195
    },
    {
      "id": "df-writes/iceberg/786_cdc_every_dml_type",
      "num": 786,
      "name": "cdc_every_dml_type",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/786_cdc_every_dml_type.sql",
      "read_script": "generator/spark-reads-iceberg/verify_786_cdc_every_dml_type.py",
      "description": "CDC table exercising all 4 DML types: INSERT, UPDATE, DELETE, MERGE. Each operation produces exact, verifiable CDF row counts. This is the most comprehensive single-table CDF test.",
      "status": "pass",
      "duration_ms": 228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:18.539236+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 433,
      "write_warm_ms": 238
    },
    {
      "id": "df-writes/iceberg/787_merge_nmbys_delete_then_insert",
      "num": 787,
      "name": "merge_nmbys_delete_then_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/787_merge_nmbys_delete_then_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_787_merge_nmbys_delete_then_insert.py",
      "description": "NM-BY-SOURCE DELETE followed by re-INSERT of the same ID range. Tests that rows purged by NOT MATCHED BY SOURCE can be re-inserted in a subsequent operation with new generation markers.",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:18.759782+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 90,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 219,
      "write_warm_ms": 98
    },
    {
      "id": "df-writes/iceberg/788_partition_key_update_cdc",
      "num": 788,
      "name": "partition_key_update_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/788_partition_key_update_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_788_partition_key_update_cdc.py",
      "description": "UPDATE on a partition key column with CDC enabled. Tests that CDF correctly captures cross-partition row moves (delete from old partition + insert into new partition in CDF terms).",
      "status": "pass",
      "duration_ms": 242,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:19.002582+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 187,
      "write_warm_ms": 146
    },
    {
      "id": "df-writes/iceberg/789_sparse_wide_merge",
      "num": 789,
      "name": "sparse_wide_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/789_sparse_wide_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_789_sparse_wide_merge.py",
      "description": "Wide table with 10 columns, mostly NULL at insert time, then a MERGE that partially fills in sparse columns. Tests that NULL-heavy Parquet files and partial column updates work correctly.",
      "status": "pass",
      "duration_ms": 350,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:19.353282+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 116,
      "write_warm_ms": 148
    },
    {
      "id": "df-writes/iceberg/78_domain_metadata_row_tracking_domain",
      "num": 78,
      "name": "domain_metadata_row_tracking_domain",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/78_domain_metadata_row_tracking_domain.sql",
      "read_script": "generator/spark-reads-iceberg/verify_78_domain_metadata_row_tracking_domain.py",
      "description": "Demonstrates domain metadata for row tracking feature. The row tracking feature stores its configuration and state in domain metadata.",
      "status": "pass",
      "duration_ms": 4562,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:23.916567+00:00",
      "read_cold_ms": 148,
      "read_warm_ms": 232,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 607,
      "write_warm_ms": 403,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:row-tracking",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/790_monotonic_id_delete_gap",
      "num": 790,
      "name": "monotonic_id_delete_gap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/790_monotonic_id_delete_gap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_790_monotonic_id_delete_gap.py",
      "description": "Sequential IDs with a gap created by DELETE, then re-INSERT into the gap with a different generation marker. Tests that deletion vectors handle mid-range deletes and that re-inserted rows with the same IDs coexist correctly with surviving rows.",
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:24.204537+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 110,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 139,
      "write_warm_ms": 52
    },
    {
      "id": "df-writes/iceberg/791_merge_nmbys_partition_cdc_constraint",
      "num": 791,
      "name": "merge_nmbys_partition_cdc_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/791_merge_nmbys_partition_cdc_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_791_merge_nmbys_partition_cdc_constraint.py",
      "description": "Four-way combination: NM-BY-SOURCE + partition + CDC + CHECK constraint. Tests that CDF captures NM-BY-SOURCE events across partitions, and that the constraint is enforced during NM-BY-SOURCE UPDATE operations.",
      "status": "pass",
      "duration_ms": 357,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:24.562250+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 433,
      "write_warm_ms": 431
    },
    {
      "id": "df-writes/iceberg/792_colmap_rename_drop_evolve",
      "num": 792,
      "name": "colmap_rename_drop_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/792_colmap_rename_drop_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_792_colmap_rename_drop_evolve.py",
      "description": "Full column mutation trilogy: RENAME + DROP + ADD COLUMN, all with column mapping enabled. Tests that the column mapping metadata correctly tracks all three mutation types in sequence.",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:24.797998+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "schema:drop-column",
        "schema:rename-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 189,
      "write_warm_ms": 103
    },
    {
      "id": "df-writes/iceberg/793_constraint_across_merge_nmbys",
      "num": 793,
      "name": "constraint_across_merge_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/793_constraint_across_merge_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_793_constraint_across_merge_nmbys.py",
      "description": "CHECK constraint must be enforced during a MERGE with NOT MATCHED BY SOURCE UPDATE. The NM-BY-SOURCE clause updates value using an expression that keeps it positive, satisfying the constraint. Verifies that constraint validation applies to NM-BY-SOURCE UPDATE paths.",
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:25.085371+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 86,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 54,
      "write_warm_ms": 49
    },
    {
      "id": "df-writes/iceberg/794_cdc_schema_evolve_nmbys",
      "num": 794,
      "name": "cdc_schema_evolve_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/794_cdc_schema_evolve_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_794_cdc_schema_evolve_nmbys.py",
      "description": "CDC + schema evolution + NM-BY-SOURCE UPDATE. CDF must correctly capture events across a schema change (ADD COLUMN) with NM-BY-SOURCE populating the new column differently for matched vs unmatched rows.",
      "status": "pass",
      "duration_ms": 273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:25.359382+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 97,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 262,
      "write_warm_ms": 248
    },
    {
      "id": "df-writes/iceberg/795_large_partition_nmbys",
      "num": 795,
      "name": "large_partition_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/795_large_partition_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_795_large_partition_nmbys.py",
      "description": "Large-scale partitioned table: 500 rows across 5 partitions, then NM-BY-SOURCE DELETE removes 300 rows. Tests that NM-BY-SOURCE DELETE works correctly at scale across multiple partitions.",
      "status": "pass",
      "duration_ms": 308,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:25.668500+00:00",
      "read_cold_ms": 88,
      "read_warm_ms": 101,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 169,
      "write_warm_ms": 251
    },
    {
      "id": "df-writes/iceberg/796_merge_nmbys_optimize_cdc",
      "num": 796,
      "name": "merge_nmbys_optimize_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/796_merge_nmbys_optimize_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_796_merge_nmbys_optimize_cdc.py",
      "description": "NM-BY-SOURCE + OPTIMIZE + CDC. Tests that CDF is correct after file compaction (OPTIMIZE), and that NM-BY-SOURCE DELETE after OPTIMIZE produces valid CDF events from compacted files.",
      "status": "pass",
      "duration_ms": 291,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:25.960091+00:00",
      "read_cold_ms": 132,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 667,
      "write_warm_ms": 781
    },
    {
      "id": "df-writes/iceberg/797_constraint_survive_optimize_merge",
      "num": 797,
      "name": "constraint_survive_optimize_merge",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/797_constraint_survive_optimize_merge.sql",
      "read_script": "generator/spark-reads-iceberg/verify_797_constraint_survive_optimize_merge.py",
      "description": "CHECK constraint added after multi-batch inserts, survives OPTIMIZE, then enforced during a MERGE (all data valid throughout). Verifies that OPTIMIZE does not lose constraint metadata and that MERGE respects the constraint post-compaction.",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:26.174254+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 537,
      "write_warm_ms": 644
    },
    {
      "id": "df-writes/iceberg/798_colmap_nmbys_cdc_partition",
      "num": 798,
      "name": "colmap_nmbys_cdc_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/798_colmap_nmbys_cdc_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_798_colmap_nmbys_cdc_partition.py",
      "description": "Four-way combination: column mapping + NM-BY-SOURCE + CDC + partition. Tests that CDF uses logical column names from colmap, NM-BY-SOURCE DELETE works across partitions, and all features interoperate.",
      "status": "pass",
      "duration_ms": 278,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:26.453131+00:00",
      "read_cold_ms": 91,
      "read_warm_ms": 80,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
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      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "reader": "spark",
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      "extras": {},
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      "skip_feature": null,
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        "iceberg:uniform",
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        "format:iceberg"
      ],
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      "concern_note": null,
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      "write_warm_ms": 189
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      "reader": "spark",
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      "notes": null,
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 137
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    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:34.634993+00:00",
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      "skip_feature": null,
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      "tags": [
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        "format:iceberg"
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      "concern_note": null,
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      "write_warm_ms": 101
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      "duration_ms": 247,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:34.883066+00:00",
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        "format:iceberg"
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    {
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      "error": null,
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    {
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      "error": null,
      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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      "format": "iceberg",
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      "manual_tags": [],
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "skip_feature": null,
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "concern_note": null,
      "write_cold_ms": 161,
      "write_warm_ms": 134
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      "num": 811,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/811_merge_decimal_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_811_merge_decimal_key.py",
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      "status": "pass",
      "duration_ms": 193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:37.040582+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 56,
      "skip_feature": null,
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        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 104,
      "write_warm_ms": 90
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      "num": 812,
      "name": "merge_string_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_812_merge_string_key.py",
      "description": "MERGE where join key is STRING. Tests STRING equality in join predicate.",
      "status": "pass",
      "duration_ms": 272,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:37.313522+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
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        "delta:deletion-vectors",
        "iceberg:uniform",
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 98,
      "write_warm_ms": 116
    },
    {
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      "num": 813,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/813_merge_boolean_key.sql",
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      "description": "MERGE where join key includes BOOLEAN. Tests BOOLEAN in join predicate. Unusual but valid -- compound key with id + is_active.",
      "status": "pass",
      "duration_ms": 312,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:37.626625+00:00",
      "read_cold_ms": 98,
      "read_warm_ms": 83,
      "skip_feature": null,
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        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "concern_note": null,
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    {
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      "num": 814,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/814_merge_timestamp_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_814_merge_timestamp_key.py",
      "description": "MERGE where join key includes TIMESTAMP. Tests TIMESTAMP equality in join predicate.",
      "status": "pass",
      "duration_ms": 281,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:37.908430+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 156,
      "write_warm_ms": 181
    },
    {
      "id": "df-writes/iceberg/815_merge_update_decimal_arithmetic",
      "num": 815,
      "name": "merge_update_decimal_arithmetic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/815_merge_update_decimal_arithmetic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_815_merge_update_decimal_arithmetic.py",
      "description": "MERGE UPDATE SET with DECIMAL arithmetic. Tests that DECIMAL arithmetic in SET clause preserves precision through pre-computed source values.",
      "status": "pass",
      "duration_ms": 295,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:38.203629+00:00",
      "read_cold_ms": 75,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 77,
      "write_warm_ms": 73
    },
    {
      "id": "df-writes/iceberg/816_merge_update_all_types_at_once",
      "num": 816,
      "name": "merge_update_all_types_at_once",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/816_merge_update_all_types_at_once.sql",
      "read_script": "generator/spark-reads-iceberg/verify_816_merge_update_all_types_at_once.py",
      "description": "MERGE that updates every column type in a single UPDATE SET clause. \"Kitchen sink\" type test: STRING, INT, DOUBLE, BOOLEAN, DECIMAL, TIMESTAMP all updated at once.",
      "status": "pass",
      "duration_ms": 279,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:38.483879+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 91,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 80,
      "write_warm_ms": 83
    },
    {
      "id": "df-writes/iceberg/817_merge_insert_all_types",
      "num": 817,
      "name": "merge_insert_all_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/817_merge_insert_all_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_817_merge_insert_all_types.py",
      "description": "MERGE NOT MATCHED INSERT with every column type. Tests that the INSERT path handles all types correctly. Zero overlap between target and source -- all rows go through NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 167,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:38.651981+00:00",
      "read_cold_ms": 34,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 159,
      "write_warm_ms": 91
    },
    {
      "id": "df-writes/iceberg/818_merge_int_to_bigint_coerce",
      "num": 818,
      "name": "merge_int_to_bigint_coerce",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/818_merge_int_to_bigint_coerce.sql",
      "read_script": "generator/spark-reads-iceberg/verify_818_merge_int_to_bigint_coerce.py",
      "description": "MERGE where source provides INT-range values into a BIGINT target column. Tests implicit widening coercion (INT -> BIGINT) through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:38.917224+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:type-widening",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 74,
      "write_warm_ms": 77
    },
    {
      "id": "df-writes/iceberg/819_merge_float_to_double_coerce",
      "num": 819,
      "name": "merge_float_to_double_coerce",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/819_merge_float_to_double_coerce.sql",
      "read_script": "generator/spark-reads-iceberg/verify_819_merge_float_to_double_coerce.py",
      "description": "MERGE where source FLOAT column updates target DOUBLE column. Tests widening coercion (FLOAT -> DOUBLE) through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 335,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:39.252352+00:00",
      "read_cold_ms": 124,
      "read_warm_ms": 72,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 181,
      "write_warm_ms": 60
    },
    {
      "id": "df-writes/iceberg/81_v2_checkpoint_sidecar_json_pointers",
      "num": 81,
      "name": "v2_checkpoint_sidecar_json_pointers",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/81_v2_checkpoint_sidecar_json_pointers.sql",
      "read_script": "generator/spark-reads-iceberg/verify_81_v2_checkpoint_sidecar_json_pointers.py",
      "description": "- V2 checkpoint format with sidecar files and JSON pointers - Large table (65k records) triggers multiple sidecar files - Deletion vectors enabled - Complex social media content management schema (28 columns) - Date32 and Timestamp(Microsecond) types - Multiple UPDATE and DELETE...",
      "status": "pass",
      "duration_ms": 14696,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:53.949585+00:00",
      "read_cold_ms": 1467,
      "read_warm_ms": 1376,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 12365,
      "write_warm_ms": 11882,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-sidecar",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/820_merge_decimal_scale_mismatch",
      "num": 820,
      "name": "merge_decimal_scale_mismatch",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/820_merge_decimal_scale_mismatch.sql",
      "read_script": "generator/spark-reads-iceberg/verify_820_merge_decimal_scale_mismatch.py",
      "description": "MERGE where source DECIMAL(10,2) updates target DECIMAL(10,4). Tests decimal scale widening in MERGE SET -- source has 2 decimal places, target expects 4. Updated amounts should show .XX00 pattern.",
      "status": "pass",
      "duration_ms": 273,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:54.223869+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 70,
      "write_warm_ms": 162
    },
    {
      "id": "df-writes/iceberg/821_merge_decimal_negative",
      "num": 821,
      "name": "merge_decimal_negative",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/821_merge_decimal_negative.sql",
      "read_script": "generator/spark-reads-iceberg/verify_821_merge_decimal_negative.py",
      "description": "MERGE with negative DECIMAL values. Tests sign preservation and sign-flipping through MERGE UPDATE SET.",
      "status": "pass",
      "duration_ms": 374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:54.598355+00:00",
      "read_cold_ms": 99,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 105,
      "write_warm_ms": 59
    },
    {
      "id": "df-writes/iceberg/822_merge_decimal_zero",
      "num": 822,
      "name": "merge_decimal_zero",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/822_merge_decimal_zero.sql",
      "read_script": "generator/spark-reads-iceberg/verify_822_merge_decimal_zero.py",
      "description": "MERGE updating DECIMAL columns to zero. Tests zero-value DECIMAL handling with conditional MERGE clauses targeting different columns.",
      "status": "pass",
      "duration_ms": 261,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:54.860629+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 67,
      "write_warm_ms": 204
    },
    {
      "id": "df-writes/iceberg/823_merge_timestamp_microsecond",
      "num": 823,
      "name": "merge_timestamp_microsecond",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/823_merge_timestamp_microsecond.sql",
      "read_script": "generator/spark-reads-iceberg/verify_823_merge_timestamp_microsecond.py",
      "description": "MERGE with timestamps differing by 1 microsecond. Tests microsecond precision in MERGE UPDATE SET. Join is on id, not timestamp.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:55.114306+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 106,
      "write_warm_ms": 85
    },
    {
      "id": "df-writes/iceberg/824_merge_boolean_flip",
      "num": 824,
      "name": "merge_boolean_flip",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/824_merge_boolean_flip.sql",
      "read_script": "generator/spark-reads-iceberg/verify_824_merge_boolean_flip.py",
      "description": "MERGE that flips all BOOLEAN values using CASE expressions. Tests boolean negation through MERGE UPDATE SET across multiple boolean columns.",
      "status": "pass",
      "duration_ms": 307,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:55.421972+00:00",
      "read_cold_ms": 97,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 100,
      "write_warm_ms": 95
    },
    {
      "id": "df-writes/iceberg/825_merge_string_empty",
      "num": 825,
      "name": "merge_string_empty",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/825_merge_string_empty.sql",
      "read_script": "generator/spark-reads-iceberg/verify_825_merge_string_empty.py",
      "description": "MERGE with empty string values. Tests '' (empty string) handling and detection through MERGE UPDATE SET with CASE expressions.",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:55.672092+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 72,
      "write_warm_ms": 49
    },
    {
      "id": "df-writes/iceberg/826_merge_null_key",
      "num": 826,
      "name": "merge_null_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/826_merge_null_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_826_merge_null_key.py",
      "description": "MERGE where some source rows have NULL join key. Tests NULL!=NULL semantics in MERGE ON clause -- NULL-keyed source rows never match, always go through NOT MATCHED INSERT.",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:55.872345+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 130,
      "write_warm_ms": 70
    },
    {
      "id": "df-writes/iceberg/827_merge_delete_typed_predicate",
      "num": 827,
      "name": "merge_delete_typed_predicate",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/827_merge_delete_typed_predicate.sql",
      "read_script": "generator/spark-reads-iceberg/verify_827_merge_delete_typed_predicate.py",
      "description": "MERGE with DELETE clause using typed column predicates. Tests type evaluation (DECIMAL comparison, BOOLEAN equality) in MATCHED DELETE and conditional UPDATE.",
      "status": "pass",
      "duration_ms": 196,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:56.068760+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 70,
      "write_warm_ms": 87
    },
    {
      "id": "df-writes/iceberg/828_merge_nmbys_typed_update",
      "num": 828,
      "name": "merge_nmbys_typed_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/828_merge_nmbys_typed_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_828_merge_nmbys_typed_update.py",
      "description": "WHEN NOT MATCHED BY SOURCE with typed UPDATE SET. Tests type handling in the NOT MATCHED BY SOURCE path -- zeroing DECIMAL, INT, and setting BOOLEAN.",
      "status": "pass",
      "duration_ms": 230,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:56.299213+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 106,
      "write_warm_ms": 66
    },
    {
      "id": "df-writes/iceberg/829_merge_struct_nested_update",
      "num": 829,
      "name": "merge_struct_nested_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/829_merge_struct_nested_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_829_merge_struct_nested_update.py",
      "description": "MERGE that updates a STRUCT column with different field values. Tests full struct replacement through MERGE UPDATE and INSERT with named_struct.",
      "status": "pass",
      "duration_ms": 361,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:28:56.660483+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 54,
      "write_warm_ms": 140
    },
    {
      "id": "df-writes/iceberg/82_multipart_checkpoint_recovery_edge_cases",
      "num": 82,
      "name": "multipart_checkpoint_recovery_edge_cases",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/82_multipart_checkpoint_recovery_edge_cases.sql",
      "read_script": "generator/spark-reads-iceberg/verify_82_multipart_checkpoint_recovery_edge_cases.py",
      "description": "- Multi-part checkpoint edge cases and recovery scenarios - Large table (85,000+ rows) with wide schema (31 columns) - Partitioned by partition_region (6 regions) - Deletion vectors enabled - Multiple UPDATE operations and batch INSERTs - Date32 and Timestamp(Microsecond) types...",
      "status": "pass",
      "duration_ms": 4612,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:01.273753+00:00",
      "read_cold_ms": 212,
      "read_warm_ms": 157,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 2292,
      "write_warm_ms": 2275,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:checkpoint-multipart",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/830_merge_decimal_boundary",
      "num": 830,
      "name": "merge_decimal_boundary",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/830_merge_decimal_boundary.sql",
      "read_script": "generator/spark-reads-iceberg/verify_830_merge_decimal_boundary.py",
      "description": "MERGE with DECIMAL values at precision boundaries. Tests max/min DECIMAL values through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 310,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:01.584846+00:00",
      "read_cold_ms": 111,
      "read_warm_ms": 111,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 65,
      "write_warm_ms": 67
    },
    {
      "id": "df-writes/iceberg/831_merge_update_to_null",
      "num": 831,
      "name": "merge_update_to_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/831_merge_update_to_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_831_merge_update_to_null.py",
      "description": "MERGE UPDATE SET that explicitly sets typed columns to NULL. Tests NULL introduction per type (STRING, INT, DOUBLE, BOOLEAN, DECIMAL, TIMESTAMP) through MERGE conditional clauses.",
      "status": "pass",
      "duration_ms": 217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:01.802380+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 179,
      "write_warm_ms": 84
    },
    {
      "id": "df-writes/iceberg/832_merge_conditional_type_cast",
      "num": 832,
      "name": "merge_conditional_type_cast",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/832_merge_conditional_type_cast.sql",
      "read_script": "generator/spark-reads-iceberg/verify_832_merge_conditional_type_cast.py",
      "description": "MERGE with CASE expression producing STRING values computed from different typed columns. Tests type unification (DOUBLE->STRING casts) in MERGE SET through CASE branches.",
      "status": "pass",
      "duration_ms": 264,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:02.067478+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 68,
      "write_warm_ms": 183
    },
    {
      "id": "df-writes/iceberg/833_merge_int_overflow_safe",
      "num": 833,
      "name": "merge_int_overflow_safe",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/833_merge_int_overflow_safe.sql",
      "read_script": "generator/spark-reads-iceberg/verify_833_merge_int_overflow_safe.py",
      "description": "MERGE with INT values near the overflow boundary. Tests INT range safety with values close to INT max (2147483647) through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 197,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:02.264774+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 86,
      "write_warm_ms": 97
    },
    {
      "id": "df-writes/iceberg/834_merge_multi_decimal_update",
      "num": 834,
      "name": "merge_multi_decimal_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/834_merge_multi_decimal_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_834_merge_multi_decimal_update.py",
      "description": "MERGE updating 4 DECIMAL columns simultaneously with different precision/scale. Tests that each DECIMAL column maintains independent precision through MERGE.",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:02.515375+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 100,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 198,
      "write_warm_ms": 85
    },
    {
      "id": "df-writes/iceberg/835_merge_decimal_cdc",
      "num": 835,
      "name": "merge_decimal_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/835_merge_decimal_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_835_merge_decimal_cdc.py",
      "description": "MERGE with DECIMAL columns + CDC enabled. Tests that CDF records preserve DECIMAL precision through MERGE UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:02.736596+00:00",
      "read_cold_ms": 80,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 248,
      "write_warm_ms": 308
    },
    {
      "id": "df-writes/iceberg/836_merge_timestamp_cdc",
      "num": 836,
      "name": "merge_timestamp_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/836_merge_timestamp_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_836_merge_timestamp_cdc.py",
      "description": "MERGE with TIMESTAMP + CDC enabled. Tests that CDF records preserve TIMESTAMP values through MERGE UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:02.949548+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 188,
      "write_warm_ms": 304
    },
    {
      "id": "df-writes/iceberg/837_merge_decimal_partition",
      "num": 837,
      "name": "merge_decimal_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/837_merge_decimal_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_837_merge_decimal_partition.py",
      "description": "MERGE with DECIMAL columns + partitioned table. Tests that DECIMAL precision is preserved through MERGE UPDATE and INSERT across multiple partitions.",
      "status": "pass",
      "duration_ms": 243,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:03.193495+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 158,
      "write_warm_ms": 176
    },
    {
      "id": "df-writes/iceberg/838_merge_timestamp_partition",
      "num": 838,
      "name": "merge_timestamp_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/838_merge_timestamp_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_838_merge_timestamp_partition.py",
      "description": "MERGE with TIMESTAMP + partitioned table. Tests that TIMESTAMP values are preserved through MERGE UPDATE and INSERT across multiple partitions.",
      "status": "pass",
      "duration_ms": 256,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:03.450202+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 172,
      "write_warm_ms": 225
    },
    {
      "id": "df-writes/iceberg/839_merge_types_constraint",
      "num": 839,
      "name": "merge_types_constraint",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/839_merge_types_constraint.sql",
      "read_script": "generator/spark-reads-iceberg/verify_839_merge_types_constraint.py",
      "description": "MERGE where a CHECK constraint applies to a typed DECIMAL column. Tests that constraint enforcement works correctly on MERGE-updated DECIMAL values.",
      "status": "pass",
      "duration_ms": 212,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:03.662803+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 110,
      "write_warm_ms": 147
    },
    {
      "id": "df-writes/iceberg/83_writer_feature_combinations_complex",
      "num": 83,
      "name": "writer_feature_combinations_complex",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/83_writer_feature_combinations_complex.sql",
      "read_script": "generator/spark-reads-iceberg/verify_83_writer_feature_combinations_complex.py",
      "description": "- Complex combinations of multiple writer features interacting - Row Tracking + Deletion Vectors + Change Data Feed - Column Mapping + Check Constraints + Generated Columns - 40 columns with timestamp_ntz fields requiring metadata - Multiple UPDATE operations tracking flight...",
      "status": "pass",
      "duration_ms": 2358,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:06.021661+00:00",
      "read_cold_ms": 142,
      "read_warm_ms": 160,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 1105,
      "write_warm_ms": 1040,
      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "type:timestamp-ntz",
        "dml:cdc-write",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:generated-columns",
        "delta:row-tracking",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/840_merge_types_optimize",
      "num": 840,
      "name": "merge_types_optimize",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/840_merge_types_optimize.sql",
      "read_script": "generator/spark-reads-iceberg/verify_840_merge_types_optimize.py",
      "description": "MERGE on typed columns after OPTIMIZE. Tests that MERGE reads correct DECIMAL, TIMESTAMP, and BOOLEAN types from compacted Parquet files.",
      "status": "pass",
      "duration_ms": 208,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:06.230745+00:00",
      "read_cold_ms": 76,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 344,
      "write_warm_ms": 238
    },
    {
      "id": "df-writes/iceberg/841_merge_types_evolve",
      "num": 841,
      "name": "merge_types_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/841_merge_types_evolve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_841_merge_types_evolve.py",
      "description": "MERGE after schema evolution adds a DECIMAL column. Tests that MERGE correctly handles the evolved DECIMAL(10,2) column for both matched rows (which had NULL before evolution) and newly inserted rows.",
      "status": "pass",
      "duration_ms": 222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:06.453850+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 103,
      "write_warm_ms": 59
    },
    {
      "id": "df-writes/iceberg/842_merge_decimal_null_mixed",
      "num": 842,
      "name": "merge_decimal_null_mixed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/842_merge_decimal_null_mixed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_842_merge_decimal_null_mixed.py",
      "description": "MERGE where source DECIMAL is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL DECIMAL(10,2) through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 347,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:06.801324+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 44,
      "write_warm_ms": 61
    },
    {
      "id": "df-writes/iceberg/843_merge_timestamp_null_mixed",
      "num": 843,
      "name": "merge_timestamp_null_mixed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/843_merge_timestamp_null_mixed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_843_merge_timestamp_null_mixed.py",
      "description": "MERGE where source TIMESTAMP is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL TIMESTAMP through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:07.054909+00:00",
      "read_cold_ms": 92,
      "read_warm_ms": 73,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 49,
      "write_warm_ms": 192
    },
    {
      "id": "df-writes/iceberg/844_merge_boolean_null_mixed",
      "num": 844,
      "name": "merge_boolean_null_mixed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/844_merge_boolean_null_mixed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_844_merge_boolean_null_mixed.py",
      "description": "MERGE where source BOOLEAN is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL BOOLEAN through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 217,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:07.272913+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 93,
      "write_warm_ms": 64
    },
    {
      "id": "df-writes/iceberg/845_merge_int_null_mixed",
      "num": 845,
      "name": "merge_int_null_mixed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/845_merge_int_null_mixed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_845_merge_int_null_mixed.py",
      "description": "MERGE where source INT is NULL for some rows and non-NULL for others. Tests mixed NULL/non-NULL INT through MERGE UPDATE path.",
      "status": "pass",
      "duration_ms": 218,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:07.491999+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 190,
      "write_warm_ms": 84
    },
    {
      "id": "df-writes/iceberg/846_merge_double_nan_like",
      "num": 846,
      "name": "merge_double_nan_like",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/846_merge_double_nan_like.sql",
      "read_script": "generator/spark-reads-iceberg/verify_846_merge_double_nan_like.py",
      "description": "MERGE with DOUBLE values at floating-point extremes (very small approaching zero, very large). Tests floating-point edge cases through MERGE UPDATE/INSERT.",
      "status": "pass",
      "duration_ms": 218,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:07.710509+00:00",
      "read_cold_ms": 93,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 87,
      "write_warm_ms": 89
    },
    {
      "id": "df-writes/iceberg/847_merge_struct_with_types",
      "num": 847,
      "name": "merge_struct_with_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/847_merge_struct_with_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_847_merge_struct_with_types.py",
      "description": "STRUCT containing STRING, INT, and BOOLEAN fields. Tests struct with mixed typed fields through MERGE UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:07.964342+00:00",
      "read_cold_ms": 62,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 78,
      "write_warm_ms": 71
    },
    {
      "id": "df-writes/iceberg/848_merge_all_types_nmbys",
      "num": 848,
      "name": "merge_all_types_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/848_merge_all_types_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_848_merge_all_types_nmbys.py",
      "description": "MERGE NOT MATCHED BY SOURCE updating all typed columns. Tests type handling in the NM-BY-SOURCE UPDATE path across STRING, INT, DOUBLE, BOOLEAN, and DECIMAL simultaneously.",
      "status": "pass",
      "duration_ms": 193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:08.157817+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 152,
      "write_warm_ms": 69
    },
    {
      "id": "df-writes/iceberg/849_merge_typed_delete_insert",
      "num": 849,
      "name": "merge_typed_delete_insert",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/849_merge_typed_delete_insert.sql",
      "read_script": "generator/spark-reads-iceberg/verify_849_merge_typed_delete_insert.py",
      "description": "MERGE with typed DELETE predicate (DECIMAL range) + NOT MATCHED INSERT. Tests DELETE based on DECIMAL comparison combined with INSERT of full type set in a single MERGE statement.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:08.410972+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 106,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 60,
      "write_warm_ms": 163
    },
    {
      "id": "df-writes/iceberg/84_variant_with_column_mapping",
      "num": 84,
      "name": "variant_with_column_mapping",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/84_variant_with_column_mapping.sql",
      "read_script": "generator/spark-reads-iceberg/verify_84_variant_with_column_mapping.py",
      "description": "Demonstrates Variant data type combined with column mapping feature.",
      "status": "pass",
      "duration_ms": 2671,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:11.082738+00:00",
      "read_cold_ms": 101,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 273,
      "write_warm_ms": 305,
      "tags": [
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/850_merge_type_comprehensive",
      "num": 850,
      "name": "merge_type_comprehensive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/850_merge_type_comprehensive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_850_merge_type_comprehensive.py",
      "description": "per-type assertions. Exercises all types through MERGE UPDATE, DELETE, and INSERT paths simultaneously.",
      "status": "pass",
      "duration_ms": 226,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:11.309810+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 317,
      "write_warm_ms": 352
    },
    {
      "id": "df-writes/iceberg/851_merge_multi_type_update_set",
      "num": 851,
      "name": "merge_multi_type_update_set",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/851_merge_multi_type_update_set.sql",
      "read_script": "generator/spark-reads-iceberg/verify_851_merge_multi_type_update_set.py",
      "description": "MERGE that updates 5 different typed columns in a single UPDATE SET, each with a different transformation. Tests that the engine correctly applies heterogeneous per-column transformations within one MERGE UPDATE SET clause.",
      "status": "pass",
      "duration_ms": 215,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:11.524981+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 279,
      "write_warm_ms": 111
    },
    {
      "id": "df-writes/iceberg/852_merge_chain_typed",
      "num": 852,
      "name": "merge_chain_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/852_merge_chain_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_852_merge_chain_typed.py",
      "description": "Two sequential MERGEs on a table with DECIMAL+TIMESTAMP+BOOLEAN. Each MERGE transforms the typed columns. Tests type stability across MERGE chains -- ensures that writing typed values in one MERGE does not corrupt them for the next MERGE read.",
      "status": "pass",
      "duration_ms": 269,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:11.794857+00:00",
      "read_cold_ms": 120,
      "read_warm_ms": 81,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 211,
      "write_warm_ms": 126
    },
    {
      "id": "df-writes/iceberg/853_merge_mixed_predicate_types",
      "num": 853,
      "name": "merge_mixed_predicate_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/853_merge_mixed_predicate_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_853_merge_mixed_predicate_types.py",
      "description": "MERGE with WHEN MATCHED conditions using different typed predicates: DECIMAL comparison + BOOLEAN check + INT range. Four WHEN MATCHED clauses with progressively broader predicates form a priority cascade.",
      "status": "pass",
      "duration_ms": 253,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:12.048314+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 98,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 50,
      "write_warm_ms": 46
    },
    {
      "id": "df-writes/iceberg/854_merge_partial_column_update",
      "num": 854,
      "name": "merge_partial_column_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/854_merge_partial_column_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_854_merge_partial_column_update.py",
      "description": "MERGE where UPDATE SET only touches 2 of 8 columns. Tests that untouched columns survive the MERGE write cycle without corruption or zeroing.",
      "status": "pass",
      "duration_ms": 216,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:12.265154+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 51,
      "write_warm_ms": 48
    },
    {
      "id": "df-writes/iceberg/855_merge_source_subset_columns",
      "num": 855,
      "name": "merge_source_subset_columns",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/855_merge_source_subset_columns.sql",
      "read_script": "generator/spark-reads-iceberg/verify_855_merge_source_subset_columns.py",
      "description": "MERGE where source CTE has fewer columns than target. Source only provides id + the 2 columns being updated. Tests column projection in MERGE -- the engine must not expect source to have all target columns.",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:12.486543+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 46,
      "write_warm_ms": 48
    },
    {
      "id": "df-writes/iceberg/856_merge_decimal_across_partitions",
      "num": 856,
      "name": "merge_decimal_across_partitions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/856_merge_decimal_across_partitions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_856_merge_decimal_across_partitions.py",
      "description": "MERGE on partitioned table where each partition has different DECIMAL value ranges (different magnitudes). Tests DECIMAL handling across partition boundaries -- each partition's Parquet files have different value ranges.",
      "status": "pass",
      "duration_ms": 263,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:12.750013+00:00",
      "read_cold_ms": 69,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 82,
      "write_warm_ms": 292
    },
    {
      "id": "df-writes/iceberg/857_merge_after_dv_delete_typed",
      "num": 857,
      "name": "merge_after_dv_delete_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/857_merge_after_dv_delete_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_857_merge_after_dv_delete_typed.py",
      "description": "DELETE on typed columns (creates deletion vectors) then MERGE on same table. Tests that MERGE correctly reads DV-containing files with typed data -- the MERGE must skip DV-deleted rows when scanning for matches.",
      "status": "pass",
      "duration_ms": 198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:12.948324+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 85,
      "write_warm_ms": 66
    },
    {
      "id": "df-writes/iceberg/858_merge_typed_expressions",
      "num": 858,
      "name": "merge_typed_expressions",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/858_merge_typed_expressions.sql",
      "read_script": "generator/spark-reads-iceberg/verify_858_merge_typed_expressions.py",
      "description": "MERGE SET with computed expressions combining multiple typed columns. Source CTE pre-computes subtotal, tax_amount, total from quantity * unit_price. Tests that DECIMAL columns written from computed expressions maintain precision.",
      "status": "pass",
      "duration_ms": 282,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:13.230610+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 83,
      "write_warm_ms": 87
    },
    {
      "id": "df-writes/iceberg/859_merge_timestamp_range_delete",
      "num": 859,
      "name": "merge_timestamp_range_delete",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/859_merge_timestamp_range_delete.sql",
      "read_script": "generator/spark-reads-iceberg/verify_859_merge_timestamp_range_delete.py",
      "description": "MERGE with DELETE predicate based on TIMESTAMP range. Tests temporal predicate evaluation in the MERGE DELETE clause -- the engine must correctly compare microsecond timestamps in the WHEN MATCHED AND condition.",
      "status": "pass",
      "duration_ms": 190,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:13.421350+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 50,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 86,
      "write_warm_ms": 80
    },
    {
      "id": "df-writes/iceberg/85_optimize_zorder_file_layout",
      "num": 85,
      "name": "optimize_zorder_file_layout",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/85_optimize_zorder_file_layout.sql",
      "read_script": "generator/spark-reads-iceberg/verify_85_optimize_zorder_file_layout.py",
      "description": "- OPTIMIZE command with Z-ORDER BY for multi-dimensional file layout - E-commerce customer analytics with 30 columns - 100,000 initial events + 10,000 new December events - Final logical row count: 111,516 (after deletes) - Decimal(10,2) for cart_value, transaction_value...",
      "status": "pass",
      "duration_ms": 5350,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:18.772289+00:00",
      "read_cold_ms": 292,
      "read_warm_ms": 222,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 912,
      "write_warm_ms": 854,
      "tags": [
        "type:date",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:z-order",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/860_merge_boolean_multi_clause",
      "num": 860,
      "name": "merge_boolean_multi_clause",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/860_merge_boolean_multi_clause.sql",
      "read_script": "generator/spark-reads-iceberg/verify_860_merge_boolean_multi_clause.py",
      "description": "MERGE with BOOLEAN values driving 3 different WHEN MATCHED clauses plus a fallback. Tests BOOLEAN literal comparison (= true / = false) in MERGE predicate cascades.",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:18.973298+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 80,
      "write_warm_ms": 52
    },
    {
      "id": "df-writes/iceberg/861_merge_decimal_int_cross_type",
      "num": 861,
      "name": "merge_decimal_int_cross_type",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/861_merge_decimal_int_cross_type.sql",
      "read_script": "generator/spark-reads-iceberg/verify_861_merge_decimal_int_cross_type.py",
      "description": "MERGE where SET expression uses CAST across types: INT column feeds DECIMAL calculation, DECIMAL feeds final DECIMAL. Tests cross-type arithmetic in MERGE source CTE with CAST chains.",
      "status": "pass",
      "duration_ms": 207,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:19.180904+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 66,
      "write_warm_ms": 70
    },
    {
      "id": "df-writes/iceberg/862_merge_struct_and_scalar",
      "num": 862,
      "name": "merge_struct_and_scalar",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/862_merge_struct_and_scalar.sql",
      "read_script": "generator/spark-reads-iceberg/verify_862_merge_struct_and_scalar.py",
      "description": "MERGE that updates both STRUCT and scalar columns in the same UPDATE SET. Tests mixed struct+scalar write -- the engine must handle nested Parquet encoding (struct) alongside flat columns (INT, DECIMAL) in a single MERGE UPDATE row write.",
      "status": "pass",
      "duration_ms": 398,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:19.579711+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:parquet-encoding",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 99,
      "write_warm_ms": 63
    },
    {
      "id": "df-writes/iceberg/863_merge_cdc_all_types_exact",
      "num": 863,
      "name": "merge_cdc_all_types_exact",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/863_merge_cdc_all_types_exact.sql",
      "read_script": "generator/spark-reads-iceberg/verify_863_merge_cdc_all_types_exact.py",
      "description": "MERGE with all types + CDC (Change Data Feed), designed for exact CDF count verification per change type. Every row's fate is deterministic.",
      "status": "pass",
      "duration_ms": 222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:19.802652+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 242,
      "write_warm_ms": 360
    },
    {
      "id": "df-writes/iceberg/864_merge_nmbys_typed_complex",
      "num": 864,
      "name": "merge_nmbys_typed_complex",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/864_merge_nmbys_typed_complex.sql",
      "read_script": "generator/spark-reads-iceberg/verify_864_merge_nmbys_typed_complex.py",
      "description": "WHEN NOT MATCHED BY SOURCE with complex typed update: sets DECIMAL to zero, BOOLEAN to false, STRING to 'orphaned', INT to -1. Tests that NOT MATCHED BY SOURCE correctly applies typed default values to orphaned rows.",
      "status": "pass",
      "duration_ms": 227,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:20.030250+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 170,
      "write_warm_ms": 119
    },
    {
      "id": "df-writes/iceberg/865_merge_delete_on_composite_typed_key",
      "num": 865,
      "name": "merge_delete_on_composite_typed_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/865_merge_delete_on_composite_typed_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_865_merge_delete_on_composite_typed_key.py",
      "description": "MERGE with DELETE clause on composite typed key (STRING + INT). The ON clause joins on two columns of different types. Tests that the engine correctly evaluates composite key equality across types in MERGE.",
      "status": "pass",
      "duration_ms": 327,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:20.357735+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 89,
      "write_warm_ms": 83
    },
    {
      "id": "df-writes/iceberg/866_merge_three_clause_all_types",
      "num": 866,
      "name": "merge_three_clause_all_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/866_merge_three_clause_all_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_866_merge_three_clause_all_types.py",
      "description": "MERGE with 3 clauses (UPDATE, DELETE, INSERT) where each clause touches all column types. Every type goes through all three MERGE paths in one statement.",
      "status": "pass",
      "duration_ms": 192,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:20.550201+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 55,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 119,
      "write_warm_ms": 196
    },
    {
      "id": "df-writes/iceberg/867_merge_update_then_merge_typed",
      "num": 867,
      "name": "merge_update_then_merge_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/867_merge_update_then_merge_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_867_merge_update_then_merge_typed.py",
      "description": "UPDATE on typed columns then MERGE on same table. Tests that MERGE reads correct post-UPDATE types -- the MERGE must see the UPDATE's modified DECIMAL and flipped BOOLEAN values, not the original V0 values.",
      "status": "pass",
      "duration_ms": 214,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:20.765016+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 144,
      "write_warm_ms": 148
    },
    {
      "id": "df-writes/iceberg/868_merge_decimal_string_key",
      "num": 868,
      "name": "merge_decimal_string_key",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/868_merge_decimal_string_key.sql",
      "read_script": "generator/spark-reads-iceberg/verify_868_merge_decimal_string_key.py",
      "description": "equality where one key column is DECIMAL (integer-valued but stored as DECIMAL) and the other is STRING.",
      "status": "pass",
      "duration_ms": 223,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:20.988506+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 91,
      "write_warm_ms": 90
    },
    {
      "id": "df-writes/iceberg/869_merge_five_clause",
      "num": 869,
      "name": "merge_five_clause",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/869_merge_five_clause.sql",
      "read_script": "generator/spark-reads-iceberg/verify_869_merge_five_clause.py",
      "description": "1 NOT MATCHED INSERT, 1 NOT MATCHED BY SOURCE UPDATE. Maximum clause complexity with typed columns. Tests the engine's ability to handle the full MERGE clause repertoire in a single statement.",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:21.188767+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 66,
      "write_warm_ms": 52
    },
    {
      "id": "df-writes/iceberg/86_time_travel_timestamp_queries",
      "num": 86,
      "name": "time_travel_timestamp_queries",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/86_time_travel_timestamp_queries.sql",
      "read_script": "generator/spark-reads-iceberg/verify_86_time_travel_timestamp_queries.py",
      "description": "Time travel queries using timestamps and versions.",
      "status": "pass",
      "duration_ms": 1899,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:23.088383+00:00",
      "read_cold_ms": 115,
      "read_warm_ms": 87,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 591,
      "write_warm_ms": 521,
      "tags": [
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:time-travel",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/870_merge_partition_typed_nmbys",
      "num": 870,
      "name": "merge_partition_typed_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/870_merge_partition_typed_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_870_merge_partition_typed_nmbys.py",
      "description": "Partitioned MERGE with typed NOT MATCHED BY SOURCE + DECIMAL + BOOLEAN. Tests per-partition NOT MATCHED BY SOURCE with typed columns -- the AP partition has zero source rows, so all AP rows must be detected as orphaned and updated with typed defaults.",
      "status": "pass",
      "duration_ms": 352,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:23.440638+00:00",
      "read_cold_ms": 99,
      "read_warm_ms": 147,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 109,
      "write_warm_ms": 185
    },
    {
      "id": "df-writes/iceberg/871_merge_optimize_decimal",
      "num": 871,
      "name": "merge_optimize_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/871_merge_optimize_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_871_merge_optimize_decimal.py",
      "description": "MERGE on DECIMAL columns after OPTIMIZE. Tests DECIMAL reads from compacted files. OPTIMIZE rewrites small files into larger ones; the MERGE must then correctly read DECIMAL(10,2) values from the compacted Parquet files.",
      "status": "pass",
      "duration_ms": 374,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:23.815403+00:00",
      "read_cold_ms": 74,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 272,
      "write_warm_ms": 325
    },
    {
      "id": "df-writes/iceberg/872_merge_optimize_timestamp",
      "num": 872,
      "name": "merge_optimize_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/872_merge_optimize_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_872_merge_optimize_timestamp.py",
      "description": "MERGE on TIMESTAMP after OPTIMIZE. Tests that TIMESTAMP values survive file compaction and are correctly read during MERGE predicate evaluation.",
      "status": "pass",
      "duration_ms": 326,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:24.141767+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 199,
      "write_warm_ms": 180
    },
    {
      "id": "df-writes/iceberg/873_merge_optimize_boolean_int",
      "num": 873,
      "name": "merge_optimize_boolean_int",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/873_merge_optimize_boolean_int.sql",
      "read_script": "generator/spark-reads-iceberg/verify_873_merge_optimize_boolean_int.py",
      "description": "MERGE on BOOLEAN+INT after OPTIMIZE. Tests that BOOLEAN and INT values survive file compaction and are correctly updated during MERGE.",
      "status": "pass",
      "duration_ms": 341,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:24.482980+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 71,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 117,
      "write_warm_ms": 301
    },
    {
      "id": "df-writes/iceberg/874_merge_evolve_decimal",
      "num": 874,
      "name": "merge_evolve_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/874_merge_evolve_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_874_merge_evolve_decimal.py",
      "description": "Schema evolution adds DECIMAL column, then MERGE populates it. Tests that a newly added DECIMAL(10,2) column can be written through MERGE UPDATE SET and INSERT, even though existing rows have NULL for the new column.",
      "status": "pass",
      "duration_ms": 222,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:24.705437+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 70,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 56,
      "write_warm_ms": 173
    },
    {
      "id": "df-writes/iceberg/875_merge_evolve_timestamp",
      "num": 875,
      "name": "merge_evolve_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/875_merge_evolve_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_875_merge_evolve_timestamp.py",
      "description": "Schema evolution adds TIMESTAMP column, then MERGE populates it. Tests that a newly added TIMESTAMP column can be written through MERGE even when old Parquet files do not contain the column.",
      "status": "pass",
      "duration_ms": 303,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:25.008840+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 129,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 116,
      "write_warm_ms": 82
    },
    {
      "id": "df-writes/iceberg/876_merge_evolve_boolean",
      "num": 876,
      "name": "merge_evolve_boolean",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/876_merge_evolve_boolean.sql",
      "read_script": "generator/spark-reads-iceberg/verify_876_merge_evolve_boolean.py",
      "description": "Schema evolution adds BOOLEAN column, then MERGE populates it. Tests that a newly added BOOLEAN column can be set through MERGE UPDATE and INSERT.",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:25.234161+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 90,
      "write_warm_ms": 121
    },
    {
      "id": "df-writes/iceberg/877_merge_constraint_decimal_range",
      "num": 877,
      "name": "merge_constraint_decimal_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/877_merge_constraint_decimal_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_877_merge_constraint_decimal_range.py",
      "description": "CHECK constraint on DECIMAL range + MERGE must respect it. Tests that MERGE-written DECIMAL values satisfy the constraint boundaries.",
      "status": "pass",
      "duration_ms": 250,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:25.484515+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 205,
      "write_warm_ms": 77
    },
    {
      "id": "df-writes/iceberg/878_merge_constraint_int_positive",
      "num": 878,
      "name": "merge_constraint_int_positive",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/878_merge_constraint_int_positive.sql",
      "read_script": "generator/spark-reads-iceberg/verify_878_merge_constraint_int_positive.py",
      "description": "CHECK constraint on INT positivity + MERGE. Tests that MERGE-written INT values pass the >= 0 constraint for both UPDATE and INSERT paths.",
      "status": "pass",
      "duration_ms": 228,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:25.713534+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 62,
      "write_warm_ms": 157
    },
    {
      "id": "df-writes/iceberg/879_merge_cdc_typed_predicates",
      "num": 879,
      "name": "merge_cdc_typed_predicates",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/879_merge_cdc_typed_predicates.sql",
      "read_script": "generator/spark-reads-iceberg/verify_879_merge_cdc_typed_predicates.py",
      "description": "MERGE with typed predicates in CDC context. The WHEN MATCHED clause uses a typed predicate (amount > CAST(500 AS DECIMAL(10,2))) to route rows to different UPDATE paths. CDF must capture type-correct pre/post images.",
      "status": "pass",
      "duration_ms": 238,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:25.951939+00:00",
      "read_cold_ms": 70,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 176,
      "write_warm_ms": 192
    },
    {
      "id": "df-writes/iceberg/87_empty_table_no_data_files",
      "num": 87,
      "name": "empty_table_no_data_files",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/87_empty_table_no_data_files.sql",
      "read_script": "generator/spark-reads-iceberg/verify_87_empty_table_no_data_files.py",
      "description": "- Empty table with protocol and metadata but zero data files - Schema-only table definition (25 columns) - Deletion vectors enabled - Data warehouse fact table schema",
      "status": "pass",
      "duration_ms": 90,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:26.042389+00:00",
      "read_cold_ms": 26,
      "read_warm_ms": 19,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 14,
      "write_warm_ms": 6,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:integer",
        "type:string",
        "type:timestamp",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/880_merge_cdc_nmbys_typed",
      "num": 880,
      "name": "merge_cdc_nmbys_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/880_merge_cdc_nmbys_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_880_merge_cdc_nmbys_typed.py",
      "description": "CDC + NOT MATCHED BY SOURCE with typed UPDATE. CDF must capture the NM-BY-SOURCE changes as update_preimage/update_postimage pairs with correct DECIMAL and BOOLEAN types.",
      "status": "pass",
      "duration_ms": 199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:26.241797+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 206,
      "write_warm_ms": 333
    },
    {
      "id": "df-writes/iceberg/881_merge_colmap_decimal",
      "num": 881,
      "name": "merge_colmap_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/881_merge_colmap_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_881_merge_colmap_decimal.py",
      "description": "Column mapping (name mode) + DECIMAL through MERGE. Tests that DECIMAL(12,4) values are correctly written and read when column mapping rewrites physical column names in Parquet.",
      "status": "pass",
      "duration_ms": 182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:26.424331+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 93,
      "write_warm_ms": 70
    },
    {
      "id": "df-writes/iceberg/882_merge_colmap_timestamp",
      "num": 882,
      "name": "merge_colmap_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/882_merge_colmap_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_882_merge_colmap_timestamp.py",
      "description": "Column mapping (name mode) + TIMESTAMP through MERGE. Tests that TIMESTAMP values survive column mapping's physical name rewriting during MERGE.",
      "status": "pass",
      "duration_ms": 182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:26.607307+00:00",
      "read_cold_ms": 53,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 105,
      "write_warm_ms": 85
    },
    {
      "id": "df-writes/iceberg/883_merge_colmap_struct",
      "num": 883,
      "name": "merge_colmap_struct",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/883_merge_colmap_struct.sql",
      "read_script": "generator/spark-reads-iceberg/verify_883_merge_colmap_struct.py",
      "description": "Column mapping (name mode) + STRUCT through MERGE. Tests that nested STRUCT fields are correctly mapped through column mapping's physical name rewriting.",
      "status": "pass",
      "duration_ms": 287,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:26.894802+00:00",
      "read_cold_ms": 63,
      "read_warm_ms": 65,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:nested-types",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 96,
      "write_warm_ms": 56
    },
    {
      "id": "df-writes/iceberg/884_merge_three_decimal_ops",
      "num": 884,
      "name": "merge_three_decimal_ops",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/884_merge_three_decimal_ops.sql",
      "read_script": "generator/spark-reads-iceberg/verify_884_merge_three_decimal_ops.py",
      "description": "Three sequential MERGEs each modifying DECIMAL differently. Tests DECIMAL stability across 3 consecutive rewrite cycles: +10, *2, -5.",
      "status": "pass",
      "duration_ms": 324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:27.219058+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 163,
      "write_warm_ms": 234
    },
    {
      "id": "df-writes/iceberg/885_merge_interleaved_update",
      "num": 885,
      "name": "merge_interleaved_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/885_merge_interleaved_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_885_merge_interleaved_update.py",
      "description": "UPDATE then MERGE then UPDATE then MERGE. Tests 4-operation typed interleave where standalone UPDATEs and MERGEs alternate, each modifying typed columns.",
      "status": "pass",
      "duration_ms": 458,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:27.677722+00:00",
      "read_cold_ms": 103,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 186,
      "write_warm_ms": 138
    },
    {
      "id": "df-writes/iceberg/886_merge_delete_reinsert_typed",
      "num": 886,
      "name": "merge_delete_reinsert_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/886_merge_delete_reinsert_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_886_merge_delete_reinsert_typed.py",
      "description": "MERGE DELETE all matched rows, then separate INSERT of typed data. Tests that typed INSERT works correctly after a MERGE-DELETE has cleared the table.",
      "status": "pass",
      "duration_ms": 224,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:27.902530+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 199,
      "write_warm_ms": 176
    },
    {
      "id": "df-writes/iceberg/887_merge_wide_typed",
      "num": 887,
      "name": "merge_wide_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/887_merge_wide_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_887_merge_wide_typed.py",
      "description": "MERGE on 15-column table with diverse types. Tests wide typed schema through MERGE where all 14 non-key columns are updated or inserted.",
      "status": "pass",
      "duration_ms": 364,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:28.266951+00:00",
      "read_cold_ms": 138,
      "read_warm_ms": 122,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:wide-schema",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 86,
      "write_warm_ms": 113
    },
    {
      "id": "df-writes/iceberg/888_merge_decimal_rounding",
      "num": 888,
      "name": "merge_decimal_rounding",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/888_merge_decimal_rounding.sql",
      "read_script": "generator/spark-reads-iceberg/verify_888_merge_decimal_rounding.py",
      "description": "MERGE where DECIMAL computation requires rounding. Tests that rounding behavior is correct when computing subtotal = ROUND(price * qty, 2).",
      "status": "pass",
      "duration_ms": 239,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:28.506767+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 66,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 44,
      "write_warm_ms": 60
    },
    {
      "id": "df-writes/iceberg/889_merge_timestamp_ordering",
      "num": 889,
      "name": "merge_timestamp_ordering",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/889_merge_timestamp_ordering.sql",
      "read_script": "generator/spark-reads-iceberg/verify_889_merge_timestamp_ordering.py",
      "description": "MERGE with timestamps that maintain ordering in target but are reversed in source. After MERGE, timestamps are non-monotonic. Tests that the engine does not assume or enforce timestamp ordering.",
      "status": "pass",
      "duration_ms": 202,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:28.709553+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 58,
      "write_warm_ms": 48
    },
    {
      "id": "df-writes/iceberg/88_single_record_minimal_table",
      "num": 88,
      "name": "single_record_minimal_table",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/88_single_record_minimal_table.sql",
      "read_script": "generator/spark-reads-iceberg/verify_88_single_record_minimal_table.py",
      "description": "- Table with exactly one record (minimal data scenario) - Statistics where min = max for all columns - Single Parquet file with one row group - Multiple UPDATE operations incrementing config_version - Decimal, boolean, timestamp, date types - Deletion vectors enabled",
      "status": "pass",
      "duration_ms": 320,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:29.029830+00:00",
      "read_cold_ms": 127,
      "read_warm_ms": 95,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 115,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "storage:rowgroup-stats",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/890_merge_bool_int_partition",
      "num": 890,
      "name": "merge_bool_int_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/890_merge_bool_int_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_890_merge_bool_int_partition.py",
      "description": "MERGE on partitioned table with BOOLEAN and INT typed updates. Tests that partition-aware MERGE correctly updates BOOLEAN and INT columns across multiple partitions, including inserting new rows into existing partitions.",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:29.251213+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 215,
      "write_warm_ms": 163
    },
    {
      "id": "df-writes/iceberg/891_merge_decimal_cdc_nmbys",
      "num": 891,
      "name": "merge_decimal_cdc_nmbys",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/891_merge_decimal_cdc_nmbys.sql",
      "read_script": "generator/spark-reads-iceberg/verify_891_merge_decimal_cdc_nmbys.py",
      "description": "DECIMAL + CDC + NOT MATCHED BY SOURCE. Three-way type-aware test combining DECIMAL precision with CDF capture and orphan detection.",
      "status": "pass",
      "duration_ms": 211,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:29.462523+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 290,
      "write_warm_ms": 359
    },
    {
      "id": "df-writes/iceberg/892_merge_timestamp_evolve_cdc",
      "num": 892,
      "name": "merge_timestamp_evolve_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/892_merge_timestamp_evolve_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_892_merge_timestamp_evolve_cdc.py",
      "description": "TIMESTAMP + schema evolution + CDC + MERGE. Three-way combination: evolve schema to add TIMESTAMP, then MERGE populates it, all with CDF enabled.",
      "status": "pass",
      "duration_ms": 225,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:29.688494+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 230,
      "write_warm_ms": 300
    },
    {
      "id": "df-writes/iceberg/893_merge_struct_cdc",
      "num": 893,
      "name": "merge_struct_cdc",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/893_merge_struct_cdc.sql",
      "read_script": "generator/spark-reads-iceberg/verify_893_merge_struct_cdc.py",
      "description": "STRUCT + CDC + MERGE. Tests that STRUCT values are correctly captured in CDF update_preimage and update_postimage records.",
      "status": "pass",
      "duration_ms": 177,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:29.865627+00:00",
      "read_cold_ms": 56,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:struct",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 250,
      "write_warm_ms": 128
    },
    {
      "id": "df-writes/iceberg/894_merge_all_clauses_all_types",
      "num": 894,
      "name": "merge_all_clauses_all_types",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/894_merge_all_clauses_all_types.sql",
      "read_script": "generator/spark-reads-iceberg/verify_894_merge_all_clauses_all_types.py",
      "description": "MERGE with DELETE+UPDATE+INSERT+NM-BY-SOURCE all touching typed columns. 4 clauses x multiple types in a single MERGE statement.",
      "status": "pass",
      "duration_ms": 199,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:30.065519+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 87,
      "write_warm_ms": 63
    },
    {
      "id": "df-writes/iceberg/895_merge_partition_evolve_typed",
      "num": 895,
      "name": "merge_partition_evolve_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/895_merge_partition_evolve_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_895_merge_partition_evolve_typed.py",
      "description": "Partition + schema evolution + typed MERGE. Three-way combination where a partitioned table gets a new DECIMAL column via evolution, then MERGE populates it across all partitions.",
      "status": "pass",
      "duration_ms": 268,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:30.334428+00:00",
      "read_cold_ms": 89,
      "read_warm_ms": 79,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 223,
      "write_warm_ms": 140
    },
    {
      "id": "df-writes/iceberg/896_merge_colmap_cdc_typed",
      "num": 896,
      "name": "merge_colmap_cdc_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/896_merge_colmap_cdc_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_896_merge_colmap_cdc_typed.py",
      "description": "Column mapping + CDC + typed MERGE. Three-way combination where column mapping rewrites physical names and CDF must capture typed columns using the logical (not physical) column names.",
      "status": "pass",
      "duration_ms": 266,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:30.601458+00:00",
      "read_cold_ms": 104,
      "read_warm_ms": 64,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 326,
      "write_warm_ms": 266
    },
    {
      "id": "df-writes/iceberg/897_merge_constraint_evolve_typed",
      "num": 897,
      "name": "merge_constraint_evolve_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/897_merge_constraint_evolve_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_897_merge_constraint_evolve_typed.py",
      "description": "Constraint + schema evolution + typed MERGE. Three-way combination where a CHECK constraint exists, then a new DECIMAL column is added via evolution, and MERGE must satisfy the constraint while populating the new column.",
      "status": "pass",
      "duration_ms": 464,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:31.066104+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 57,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 88,
      "write_warm_ms": 130
    },
    {
      "id": "df-writes/iceberg/898_merge_typed_idempotent",
      "num": 898,
      "name": "merge_typed_idempotent",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/898_merge_typed_idempotent.sql",
      "read_script": "generator/spark-reads-iceberg/verify_898_merge_typed_idempotent.py",
      "description": "Two identical MERGEs in sequence (idempotent test). Same source applied twice. Final state should be the same as after the first MERGE. Tests that MERGE is idempotent when source data does not change.",
      "status": "pass",
      "duration_ms": 410,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:31.477357+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 159,
      "write_warm_ms": 116
    },
    {
      "id": "df-writes/iceberg/899_merge_large_typed",
      "num": 899,
      "name": "merge_large_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/899_merge_large_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_899_merge_large_typed.py",
      "description": "scale combined with typed columns to catch performance-sensitive type bugs that only manifest at higher row counts (e.g., buffer overflow in DECIMAL encoding, or TIMESTAMP batch alignment issues).",
      "status": "pass",
      "duration_ms": 279,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:31.757427+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 52,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:merge",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 78,
      "write_warm_ms": 156
    },
    {
      "id": "df-writes/iceberg/89_statistics_special_numeric_values",
      "num": 89,
      "name": "statistics_special_numeric_values",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/89_statistics_special_numeric_values.sql",
      "read_script": "generator/spark-reads-iceberg/verify_89_statistics_special_numeric_values.py",
      "description": "Demonstrates statistics containing special floating-point values. Tests how Delta handles IEEE 754 special values in statistics: - NaN (Not a Number) - Positive Infinity (+Inf) - Negative Infinity (-Inf) - Negative zero (-0.0) - Subnormal/denormalized numbers",
      "status": "pass",
      "duration_ms": 67,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:31.825307+00:00",
      "read_cold_ms": 20,
      "read_warm_ms": 12,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 100,
      "write_warm_ms": 59,
      "tags": [
        "type:boolean",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/900_merge_ultimate_complex",
      "num": 900,
      "name": "merge_ultimate_complex",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/900_merge_ultimate_complex.sql",
      "read_script": "generator/spark-reads-iceberg/verify_900_merge_ultimate_complex.py",
      "description": "partition + constraint + schema evolution + OPTIMIZE. This is the most complex single-MERGE test in the suite, combining every feature that could interact with typed columns.",
      "status": "pass",
      "duration_ms": 522,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:32.347470+00:00",
      "read_cold_ms": 79,
      "read_warm_ms": 82,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:cdc-write",
        "dml:insert",
        "dml:merge",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "delta:optimize",
        "delta:partitioning",
        "iceberg:uniform",
        "schema:add-column",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 606,
      "write_warm_ms": 687
    },
    {
      "id": "df-writes/iceberg/901_update_int_arithmetic",
      "num": 901,
      "name": "update_int_arithmetic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/901_update_int_arithmetic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_901_update_int_arithmetic.py",
      "description": "UPDATE with INT arithmetic expressions (add, subtract,",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:32.580177+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 53,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 55,
      "write_warm_ms": 51
    },
    {
      "id": "df-writes/iceberg/902_update_double_arithmetic",
      "num": 902,
      "name": "update_double_arithmetic",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/902_update_double_arithmetic.sql",
      "read_script": "generator/spark-reads-iceberg/verify_902_update_double_arithmetic.py",
      "description": "UPDATE with DOUBLE arithmetic expressions (multiply,",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:32.816192+00:00",
      "read_cold_ms": 101,
      "read_warm_ms": 76,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 69,
      "write_warm_ms": 45
    },
    {
      "id": "df-writes/iceberg/903_update_decimal_add",
      "num": 903,
      "name": "update_decimal_add",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/903_update_decimal_add.sql",
      "read_script": "generator/spark-reads-iceberg/verify_903_update_decimal_add.py",
      "description": "UPDATE DECIMAL(10,2) with addition. Tests that DECIMAL",
      "status": "pass",
      "duration_ms": 200,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:33.016371+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 53,
      "write_warm_ms": 38
    },
    {
      "id": "df-writes/iceberg/904_update_decimal_multiply",
      "num": 904,
      "name": "update_decimal_multiply",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/904_update_decimal_multiply.sql",
      "read_script": "generator/spark-reads-iceberg/verify_904_update_decimal_multiply.py",
      "description": "UPDATE DECIMAL with multiplication. Tests that DECIMAL",
      "status": "pass",
      "duration_ms": 182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:33.198489+00:00",
      "read_cold_ms": 58,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 108,
      "write_warm_ms": 71
    },
    {
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      "num": 905,
      "name": "update_decimal_four_scales",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/905_update_decimal_four_scales.sql",
      "read_script": "generator/spark-reads-iceberg/verify_905_update_decimal_four_scales.py",
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      "status": "pass",
      "duration_ms": 215,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:33.413872+00:00",
      "read_cold_ms": 65,
      "read_warm_ms": 63,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 45,
      "write_warm_ms": 39
    },
    {
      "id": "df-writes/iceberg/906_update_timestamp_shift",
      "num": 906,
      "name": "update_timestamp_shift",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/906_update_timestamp_shift.sql",
      "read_script": "generator/spark-reads-iceberg/verify_906_update_timestamp_shift.py",
      "description": "UPDATE TIMESTAMP by shifting microsecond offsets. Tests",
      "status": "pass",
      "duration_ms": 182,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:33.596747+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 120,
      "write_warm_ms": 38
    },
    {
      "id": "df-writes/iceberg/907_update_timestamp_to_fixed",
      "num": 907,
      "name": "update_timestamp_to_fixed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/907_update_timestamp_to_fixed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_907_update_timestamp_to_fixed.py",
      "description": "UPDATE TIMESTAMP to a single fixed value. Tests that",
      "status": "pass",
      "duration_ms": 265,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:33.862220+00:00",
      "read_cold_ms": 61,
      "read_warm_ms": 105,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 58,
      "write_warm_ms": 43
    },
    {
      "id": "df-writes/iceberg/908_update_date_shift",
      "num": 908,
      "name": "update_date_shift",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/908_update_date_shift.sql",
      "read_script": "generator/spark-reads-iceberg/verify_908_update_date_shift.py",
      "description": "UPDATE DATE column (Date32) by shifting values forward",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:34.102777+00:00",
      "read_cold_ms": 84,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:date",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 169,
      "write_warm_ms": 84
    },
    {
      "id": "df-writes/iceberg/909_update_boolean_flip",
      "num": 909,
      "name": "update_boolean_flip",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/909_update_boolean_flip.sql",
      "read_script": "generator/spark-reads-iceberg/verify_909_update_boolean_flip.py",
      "description": "UPDATE that flips BOOLEAN columns using CASE expressions.",
      "status": "pass",
      "duration_ms": 234,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:34.338007+00:00",
      "read_cold_ms": 64,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 84,
      "write_warm_ms": 75
    },
    {
      "id": "df-writes/iceberg/90_null_only_column_statistics",
      "num": 90,
      "name": "null_only_column_statistics",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/90_null_only_column_statistics.sql",
      "read_script": "generator/spark-reads-iceberg/verify_90_null_only_column_statistics.py",
      "description": "- Statistics for columns containing only NULL values - Tests how Delta handles statistics when: - All values in a column are NULL - nullCount equals numRecords - minValues and maxValues are absent or null",
      "status": "pass",
      "duration_ms": 3044,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:37.382250+00:00",
      "read_cold_ms": 96,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 208,
      "write_warm_ms": 247,
      "tags": [
        "type:binary",
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "scale:large-dataset",
        "storage:statistics",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/910_update_boolean_conditional",
      "num": 910,
      "name": "update_boolean_conditional",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/910_update_boolean_conditional.sql",
      "read_script": "generator/spark-reads-iceberg/verify_910_update_boolean_conditional.py",
      "description": "UPDATE SET BOOLEAN based on numeric predicate. Tests",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:37.615318+00:00",
      "read_cold_ms": 101,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 66,
      "write_warm_ms": 42
    },
    {
      "id": "df-writes/iceberg/911_update_string_concat",
      "num": 911,
      "name": "update_string_concat",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/911_update_string_concat.sql",
      "read_script": "generator/spark-reads-iceberg/verify_911_update_string_concat.py",
      "description": "UPDATE STRING columns with CONCAT expressions. Tests",
      "status": "pass",
      "duration_ms": 198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:37.814421+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 126,
      "write_warm_ms": 48
    },
    {
      "id": "df-writes/iceberg/912_update_string_cast",
      "num": 912,
      "name": "update_string_cast",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/912_update_string_cast.sql",
      "read_script": "generator/spark-reads-iceberg/verify_912_update_string_cast.py",
      "description": "UPDATE STRING columns from numeric values via CAST.",
      "status": "pass",
      "duration_ms": 248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:38.063235+00:00",
      "read_cold_ms": 72,
      "read_warm_ms": 94,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 55,
      "write_warm_ms": 133
    },
    {
      "id": "df-writes/iceberg/913_update_to_null_per_type",
      "num": 913,
      "name": "update_to_null_per_type",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/913_update_to_null_per_type.sql",
      "read_script": "generator/spark-reads-iceberg/verify_913_update_to_null_per_type.py",
      "description": "UPDATE each data type column to NULL individually.",
      "status": "pass",
      "duration_ms": 232,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:38.296289+00:00",
      "read_cold_ms": 77,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:null-handling",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 291,
      "write_warm_ms": 379
    },
    {
      "id": "df-writes/iceberg/914_update_from_null_to_value",
      "num": 914,
      "name": "update_from_null_to_value",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/914_update_from_null_to_value.sql",
      "read_script": "generator/spark-reads-iceberg/verify_914_update_from_null_to_value.py",
      "description": "UPDATE that replaces NULL with typed values. Tests the",
      "status": "pass",
      "duration_ms": 300,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:38.596550+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 116,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 99,
      "write_warm_ms": 155
    },
    {
      "id": "df-writes/iceberg/915_update_struct_preserve",
      "num": 915,
      "name": "update_struct_preserve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/915_update_struct_preserve.sql",
      "read_script": "generator/spark-reads-iceberg/verify_915_update_struct_preserve.py",
      "description": "UPDATE scalar columns on a table that contains a STRUCT",
      "status": "pass",
      "duration_ms": 419,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:39.015676+00:00",
      "read_cold_ms": 113,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 57,
      "write_warm_ms": 44
    },
    {
      "id": "df-writes/iceberg/916_update_decimal_negative",
      "num": 916,
      "name": "update_decimal_negative",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/916_update_decimal_negative.sql",
      "read_script": "generator/spark-reads-iceberg/verify_916_update_decimal_negative.py",
      "description": "UPDATE DECIMAL to negative values and zero. Tests sign",
      "status": "pass",
      "duration_ms": 209,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:39.224954+00:00",
      "read_cold_ms": 81,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 150,
      "write_warm_ms": 124
    },
    {
      "id": "df-writes/iceberg/917_update_decimal_boundary",
      "num": 917,
      "name": "update_decimal_boundary",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/917_update_decimal_boundary.sql",
      "read_script": "generator/spark-reads-iceberg/verify_917_update_decimal_boundary.py",
      "description": "UPDATE DECIMAL at precision boundaries. DECIMAL(5,2)",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:39.445094+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 191,
      "write_warm_ms": 95
    },
    {
      "id": "df-writes/iceberg/918_update_int_boundary",
      "num": 918,
      "name": "update_int_boundary",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/918_update_int_boundary.sql",
      "read_script": "generator/spark-reads-iceberg/verify_918_update_int_boundary.py",
      "description": "UPDATE INT near max/min boundaries. Tests that the",
      "status": "pass",
      "duration_ms": 198,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:39.643977+00:00",
      "read_cold_ms": 55,
      "read_warm_ms": 67,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 180,
      "write_warm_ms": 96
    },
    {
      "id": "df-writes/iceberg/919_update_double_extremes",
      "num": 919,
      "name": "update_double_extremes",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/919_update_double_extremes.sql",
      "read_script": "generator/spark-reads-iceberg/verify_919_update_double_extremes.py",
      "description": "UPDATE DOUBLE with very small and very large values.",
      "status": "pass",
      "duration_ms": 258,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:39.902434+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 85,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boundary",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 48,
      "write_warm_ms": 88
    },
    {
      "id": "df-writes/iceberg/91_utf8_special_characters_paths",
      "num": 91,
      "name": "utf8_special_characters_paths",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/91_utf8_special_characters_paths.sql",
      "read_script": "generator/spark-reads-iceberg/verify_91_utf8_special_characters_paths.py",
      "description": "Validates a global hospitality reviews table with UTF-8 and special characters. 46 hotels across multiple scripts (Japanese, Chinese, Arabic, Cyrillic, Greek, Hebrew, Thai, Hindi, European diacritics, emojis, special chars). Partitioned by country_code. Total reviews ~290...",
      "status": "pass",
      "duration_ms": 850,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:40.753136+00:00",
      "read_cold_ms": 111,
      "read_warm_ms": 39,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 4380,
      "write_warm_ms": 5696,
      "tags": [
        "type:boolean",
        "type:date",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/920_update_case_multi_branch",
      "num": 920,
      "name": "update_case_multi_branch",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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        "format:iceberg"
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      "write_warm_ms": 236
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      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "write_warm_ms": 138
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      "extras": {},
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      "concern_note": null,
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      "write_warm_ms": 209
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/944_update_decimal_constraint.sql",
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      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 48
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      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_945_update_int_constraint.py",
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      "duration_ms": 350,
      "error": null,
      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 112
    },
    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/946_update_decimal_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_946_update_decimal_colmap.py",
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      "notes": null,
      "extras": {},
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      "concern_note": null,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/947_update_timestamp_colmap.sql",
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      "extras": {},
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 129
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    {
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/948_update_evolve_then_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_948_update_evolve_then_update.py",
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      "error": null,
      "notes": null,
      "extras": {},
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      "concern_note": null,
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      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/949_update_evolve_timestamp.sql",
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      "duration_ms": 313,
      "error": null,
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      "extras": {},
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      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 51
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      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_94_set_transaction_idempotent_writes.py",
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      "error": null,
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      "extras": {},
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      "manual_tags": [],
      "concern_note": null
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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      "read_script": "generator/spark-reads-iceberg/verify_950_update_evolve_boolean.py",
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      "duration_ms": 295,
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      "notes": null,
      "extras": {},
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        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 96
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/951_update_then_delete_typed.sql",
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        "format:iceberg"
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      "manual_tags": [],
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      "write_warm_ms": 159
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
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        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 109,
      "write_warm_ms": 98
    },
    {
      "id": "df-writes/iceberg/953_update_timestamp_partition",
      "num": 953,
      "name": "update_timestamp_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/953_update_timestamp_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_953_update_timestamp_partition.py",
      "description": "UPDATE TIMESTAMP column on a partitioned table.",
      "status": "pass",
      "duration_ms": 193,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:56.862714+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 108,
      "write_warm_ms": 66
    },
    {
      "id": "df-writes/iceberg/954_update_boolean_partition",
      "num": 954,
      "name": "update_boolean_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/954_update_boolean_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_954_update_boolean_partition.py",
      "description": "UPDATE BOOLEAN column on a partitioned table.",
      "status": "pass",
      "duration_ms": 240,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:57.103095+00:00",
      "read_cold_ms": 71,
      "read_warm_ms": 77,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 62,
      "write_warm_ms": 198
    },
    {
      "id": "df-writes/iceberg/955_update_all_types_partition",
      "num": 955,
      "name": "update_all_types_partition",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/955_update_all_types_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_955_update_all_types_partition.py",
      "description": "UPDATE different typed columns per partition on a",
      "status": "pass",
      "duration_ms": 381,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:57.484991+00:00",
      "read_cold_ms": 117,
      "read_warm_ms": 168,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:partitioning",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 280,
      "write_warm_ms": 251
    },
    {
      "id": "df-writes/iceberg/956_update_decimal_cdc_exact",
      "num": 956,
      "name": "update_decimal_cdc_exact",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/956_update_decimal_cdc_exact.sql",
      "read_script": "generator/spark-reads-iceberg/verify_956_update_decimal_cdc_exact.py",
      "description": "UPDATE DECIMAL with CDC enabled, exact CDF row counts.",
      "status": "pass",
      "duration_ms": 195,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:57.680526+00:00",
      "read_cold_ms": 60,
      "read_warm_ms": 60,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 117,
      "write_warm_ms": 156
    },
    {
      "id": "df-writes/iceberg/957_update_mixed_cdc_exact",
      "num": 957,
      "name": "update_mixed_cdc_exact",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/957_update_mixed_cdc_exact.sql",
      "read_script": "generator/spark-reads-iceberg/verify_957_update_mixed_cdc_exact.py",
      "description": "UPDATE multiple typed columns with CDC enabled, exact",
      "status": "pass",
      "duration_ms": 196,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:57.877380+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:cdc-write",
        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 113,
      "write_warm_ms": 81
    },
    {
      "id": "df-writes/iceberg/958_update_chain_decimal",
      "num": 958,
      "name": "update_chain_decimal",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/958_update_chain_decimal.sql",
      "read_script": "generator/spark-reads-iceberg/verify_958_update_chain_decimal.py",
      "description": "3 sequential UPDATEs on a DECIMAL(10,2) column with",
      "status": "pass",
      "duration_ms": 246,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:58.124353+00:00",
      "read_cold_ms": 85,
      "read_warm_ms": 69,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 264,
      "write_warm_ms": 159
    },
    {
      "id": "df-writes/iceberg/959_update_chain_timestamp",
      "num": 959,
      "name": "update_chain_timestamp",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/959_update_chain_timestamp.sql",
      "read_script": "generator/spark-reads-iceberg/verify_959_update_chain_timestamp.py",
      "description": "3 sequential UPDATEs on a TIMESTAMP column with different",
      "status": "pass",
      "duration_ms": 241,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:58.366525+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 84,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 214,
      "write_warm_ms": 179
    },
    {
      "id": "df-writes/iceberg/95_protocol_version_edge_cases",
      "num": 95,
      "name": "protocol_version_edge_cases",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/95_protocol_version_edge_cases.sql",
      "read_script": "generator/spark-reads-iceberg/verify_95_protocol_version_edge_cases.py",
      "description": "Demonstrates protocol version edge cases and compatibility. Tests how Delta handles various protocol version scenarios: - Minimum required reader/writer versions - Feature flags vs version numbers - Backward compatibility requirements - Unknown feature handling",
      "status": "pass",
      "duration_ms": 1131,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:59.498134+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 103,
      "skip_feature": null,
      "skip_cause": null,
      "write_cold_ms": 333,
      "write_warm_ms": 523,
      "tags": [
        "type:boolean",
        "type:date",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "delta:optimize",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
      "id": "df-writes/iceberg/960_update_chain_boolean",
      "num": 960,
      "name": "update_chain_boolean",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/960_update_chain_boolean.sql",
      "read_script": "generator/spark-reads-iceberg/verify_960_update_chain_boolean.py",
      "description": "3 sequential UPDATEs flipping a BOOLEAN column with",
      "status": "pass",
      "duration_ms": 249,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:29:59.747993+00:00",
      "read_cold_ms": 82,
      "read_warm_ms": 83,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 206,
      "write_warm_ms": 237
    },
    {
      "id": "df-writes/iceberg/961_update_struct_colmap",
      "num": 961,
      "name": "update_struct_colmap",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/961_update_struct_colmap.sql",
      "read_script": "generator/spark-reads-iceberg/verify_961_update_struct_colmap.py",
      "description": "UPDATE scalar columns on a table with STRUCT column and",
      "status": "pass",
      "duration_ms": 507,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:00.255601+00:00",
      "read_cold_ms": 78,
      "read_warm_ms": 61,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:floating",
        "type:integer",
        "type:string",
        "type:struct",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 106,
      "write_warm_ms": 73
    },
    {
      "id": "df-writes/iceberg/962_update_decimal_not_null",
      "num": 962,
      "name": "update_decimal_not_null",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/962_update_decimal_not_null.sql",
      "read_script": "generator/spark-reads-iceberg/verify_962_update_decimal_not_null.py",
      "description": "UPDATE DECIMAL column on a table with NOT NULL columns.",
      "status": "pass",
      "duration_ms": 187,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:00.443671+00:00",
      "read_cold_ms": 67,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 153,
      "write_warm_ms": 74
    },
    {
      "id": "df-writes/iceberg/963_update_overwrite_then_update",
      "num": 963,
      "name": "update_overwrite_then_update",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/963_update_overwrite_then_update.sql",
      "read_script": "generator/spark-reads-iceberg/verify_963_update_overwrite_then_update.py",
      "description": "INSERT OVERWRITE then UPDATE typed columns. Tests that",
      "status": "pass",
      "duration_ms": 183,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:00.627419+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 54,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:overwrite",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 183,
      "write_warm_ms": 109
    },
    {
      "id": "df-writes/iceberg/964_update_where_compound_typed",
      "num": 964,
      "name": "update_where_compound_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/964_update_where_compound_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_964_update_where_compound_typed.py",
      "description": "UPDATE with compound WHERE clause mixing typed predicates",
      "status": "pass",
      "duration_ms": 220,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:00.848479+00:00",
      "read_cold_ms": 86,
      "read_warm_ms": 59,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 178,
      "write_warm_ms": 87
    },
    {
      "id": "df-writes/iceberg/965_update_decimal_then_update_int",
      "num": 965,
      "name": "update_decimal_then_update_int",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/965_update_decimal_then_update_int.sql",
      "read_script": "generator/spark-reads-iceberg/verify_965_update_decimal_then_update_int.py",
      "description": "UPDATE DECIMAL column then UPDATE INT column on the same",
      "status": "pass",
      "duration_ms": 324,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:01.173480+00:00",
      "read_cold_ms": 59,
      "read_warm_ms": 56,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 115,
      "write_warm_ms": 153
    },
    {
      "id": "df-writes/iceberg/966_update_all_types_where_id_range",
      "num": 966,
      "name": "update_all_types_where_id_range",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/966_update_all_types_where_id_range.sql",
      "read_script": "generator/spark-reads-iceberg/verify_966_update_all_types_where_id_range.py",
      "description": "UPDATE all 6 typed columns (STRING, INT, DOUBLE, BOOLEAN,",
      "status": "pass",
      "duration_ms": 192,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:01.365887+00:00",
      "read_cold_ms": 57,
      "read_warm_ms": 49,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 105,
      "write_warm_ms": 151
    },
    {
      "id": "df-writes/iceberg/967_update_string_operations",
      "num": 967,
      "name": "update_string_operations",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/967_update_string_operations.sql",
      "read_script": "generator/spark-reads-iceberg/verify_967_update_string_operations.py",
      "description": "Various string operations in UPDATE SET clause: CONCAT,",
      "status": "pass",
      "duration_ms": 248,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:01.614144+00:00",
      "read_cold_ms": 73,
      "read_warm_ms": 74,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
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        "type:string",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 155,
      "write_warm_ms": 76
    },
    {
      "id": "df-writes/iceberg/968_update_decimal_from_int_cast",
      "num": 968,
      "name": "update_decimal_from_int_cast",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/968_update_decimal_from_int_cast.sql",
      "read_script": "generator/spark-reads-iceberg/verify_968_update_decimal_from_int_cast.py",
      "description": "UPDATE DECIMAL(10,2) column from INT column via CAST.",
      "status": "pass",
      "duration_ms": 235,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:01.849522+00:00",
      "read_cold_ms": 83,
      "read_warm_ms": 68,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
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        "type:floating",
        "type:integer",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 95,
      "write_warm_ms": 72
    },
    {
      "id": "df-writes/iceberg/969_update_int_from_decimal_cast",
      "num": 969,
      "name": "update_int_from_decimal_cast",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/969_update_int_from_decimal_cast.sql",
      "read_script": "generator/spark-reads-iceberg/verify_969_update_int_from_decimal_cast.py",
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      "description": "**CURRENTLY DISABLED** - Table 95 is disabled due to Arrow 57.x Parquet reader limitation.",
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      "error": "df-sql cannot generate raw binary data (Gap #7: CHR() emits UTF-8 only). Coverage of binary_data_type_handling is provided by the spark-writes -> df-reads-spark direction at slot 96; the dbx-17.3 generator-rust + generator-spark pair at slot 96 also covers it.",
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      "extras": {},
      "verified_at": null,
      "finished_at": "2026-05-03T22:44:09.940857+00:00",
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        "type:boolean",
        "type:integer",
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        "format:iceberg"
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      "concern_note": null,
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    },
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/972_update_typed_partition_cdc.sql",
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      "concern_note": null,
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      "write_warm_ms": 426
    },
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      "num": 973,
      "name": "update_typed_constraint_evolve",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/973_update_typed_constraint_evolve.sql",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:03.230006+00:00",
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      "read_warm_ms": 89,
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        "delta:column-mapping",
        "delta:constraints",
        "delta:deletion-vectors",
        "iceberg:uniform",
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        "direction:df-writes",
        "format:iceberg"
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      "concern_note": null,
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    },
    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/974_update_typed_optimize_partition.sql",
      "read_script": "generator/spark-reads-iceberg/verify_974_update_typed_optimize_partition.py",
      "description": "Typed UPDATE + OPTIMIZE + partition. Three-way.",
      "status": "pass",
      "duration_ms": 244,
      "error": null,
      "notes": null,
      "extras": {},
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        "delta:column-mapping",
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        "direction:df-writes",
        "format:iceberg"
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      "concern_note": null,
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    },
    {
      "id": "df-writes/iceberg/975_update_colmap_evolve_typed",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/975_update_colmap_evolve_typed.sql",
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      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:03.766042+00:00",
      "read_cold_ms": 152,
      "read_warm_ms": 67,
      "skip_feature": null,
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        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
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      "write_warm_ms": 87
    },
    {
      "id": "df-writes/iceberg/976_update_cdc_constraint_typed",
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/976_update_cdc_constraint_typed.sql",
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      "error": null,
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        "dml:insert",
        "dml:update",
        "delta:change-data-feed",
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/977_update_large_decimal.sql",
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      "error": null,
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      "extras": {},
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        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
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      "write_warm_ms": 282
    },
    {
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      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/978_update_large_mixed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_978_update_large_mixed.py",
      "description": "Large scale UPDATE (2000 rows) with 4 typed columns.",
      "status": "pass",
      "duration_ms": 244,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:04.549318+00:00",
      "read_cold_ms": 60,
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      "skip_feature": null,
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        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
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      "write_warm_ms": 179
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    {
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      "num": 979,
      "name": "update_large_sequential",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/979_update_large_sequential.sql",
      "read_script": "generator/spark-reads-iceberg/verify_979_update_large_sequential.py",
      "description": "Large scale (1000 rows) with 10 sequential UPDATEs.",
      "status": "pass",
      "duration_ms": 392,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:04.942398+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 55,
      "skip_feature": null,
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        "dml:insert",
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        "direction:df-writes",
        "format:iceberg"
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      "manual_tags": [],
      "concern_note": null,
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    },
    {
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      "num": 97,
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql/97_decimal_precision_edge_cases.sql",
      "read_script": "generator/spark-reads-df/verify_97_decimal_precision_edge_cases.py",
      "description": "Demonstrates binary data type handling in Delta Lake. Tests how Delta handles binary columns: - Raw binary data storage - Statistics for binary columns (typically not collected) - NULL handling for binary - Various binary content types",
      "status": "pending",
      "duration_ms": null,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
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        "scale:large-dataset",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null
    },
    {
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      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/980_update_single_row_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_980_update_single_row_typed.py",
      "description": "UPDATE single row with all types. Tests minimum-scale",
      "status": "pass",
      "duration_ms": 221,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:06.459859+00:00",
      "read_cold_ms": 68,
      "read_warm_ms": 58,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 90,
      "write_warm_ms": 204
    },
    {
      "id": "df-writes/iceberg/981_update_empty_result_typed",
      "num": 981,
      "name": "update_empty_result_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/981_update_empty_result_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_981_update_empty_result_typed.py",
      "description": "UPDATE WHERE false on typed table. No rows match the",
      "status": "pass",
      "duration_ms": 63,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:06.523292+00:00",
      "read_cold_ms": 19,
      "read_warm_ms": 11,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 43,
      "write_warm_ms": 40
    },
    {
      "id": "df-writes/iceberg/982_update_full_table_typed",
      "num": 982,
      "name": "update_full_table_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/982_update_full_table_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_982_update_full_table_typed.py",
      "description": "UPDATE all rows (no WHERE clause) with typed columns.",
      "status": "pass",
      "duration_ms": 192,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:06.715871+00:00",
      "read_cold_ms": 66,
      "read_warm_ms": 62,
      "skip_feature": null,
      "skip_cause": null,
      "tags": [
        "type:boolean",
        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 77,
      "write_warm_ms": 72
    },
    {
      "id": "df-writes/iceberg/983_update_typed_delete_typed",
      "num": 983,
      "name": "update_typed_delete_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/983_update_typed_delete_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_983_update_typed_delete_typed.py",
      "description": "UPDATE typed then DELETE on typed predicate. Tests",
      "status": "pass",
      "duration_ms": 237,
      "error": null,
      "notes": null,
      "extras": {},
      "verified_at": "2026-05-20T12:30:06.953809+00:00",
      "read_cold_ms": 77,
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        "type:decimal",
        "type:floating",
        "type:integer",
        "type:string",
        "type:timestamp",
        "dml:delete",
        "dml:insert",
        "dml:update",
        "delta:column-mapping",
        "delta:deletion-vectors",
        "iceberg:uniform",
        "direction:df-writes",
        "format:iceberg"
      ],
      "manual_tags": [],
      "concern_note": null,
      "write_cold_ms": 110,
      "write_warm_ms": 94
    },
    {
      "id": "df-writes/iceberg/984_update_delete_update_typed",
      "num": 984,
      "name": "update_delete_update_typed",
      "type": "df-writes",
      "format": "iceberg",
      "writer": "delta-forge",
      "reader": "spark",
      "write_script": "generator/df-sql-iceberg/984_update_delete_update_typed.sql",
      "read_script": "generator/spark-reads-iceberg/verify_984_update_delete_update_typed.py",
      "description": "UPDATE then DELETE then UPDATE. Three-step DML chain",
      "status": "pass",
      "duration_ms": 287,
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          },
          {
            "slug": "type:binary",
            "label": "BINARY",
            "hint": "Binary / byte-array columns."
          },
          {
            "slug": "type:boolean",
            "label": "BOOLEAN",
            "hint": "Boolean columns and predicates."
          },
          {
            "slug": "type:date",
            "label": "DATE",
            "hint": "DATE (no time component)."
          },
          {
            "slug": "type:timestamp",
            "label": "TIMESTAMP",
            "hint": "Timezone-aware timestamp."
          },
          {
            "slug": "type:timestamp-ntz",
            "label": "TIMESTAMP_NTZ",
            "hint": "Timestamp without time zone (Databricks 13+)."
          },
          {
            "slug": "type:struct",
            "label": "STRUCT",
            "hint": "Nested struct columns."
          },
          {
            "slug": "type:array",
            "label": "ARRAY",
            "hint": "Array / list columns."
          },
          {
            "slug": "type:map",
            "label": "MAP",
            "hint": "Map / key-value columns."
          },
          {
            "slug": "type:unicode",
            "label": "Unicode column names",
            "hint": "Non-ASCII identifiers and values."
          },
          {
            "slug": "type:null-handling",
            "label": "NULL handling",
            "hint": "All-null rows, sparse nulls, NULL per type."
          },
          {
            "slug": "type:boundary",
            "label": "Boundary values",
            "hint": "INT/BIGINT min/max, decimal precision edges."
          }
        ]
      },
      {
        "key": "dml",
        "label": "DML",
        "blurb": "Mutation operations and the patterns engineers actually run.",
        "tags": [
          {
            "slug": "dml:insert",
            "label": "INSERT",
            "hint": "Single and multi-batch INSERT."
          },
          {
            "slug": "dml:update",
            "label": "UPDATE",
            "hint": "UPDATE with predicates and computed SET clauses."
          },
          {
            "slug": "dml:delete",
            "label": "DELETE",
            "hint": "DELETE with predicates including compound and modular forms."
          },
          {
            "slug": "dml:merge",
            "label": "MERGE",
            "hint": "MERGE / UPSERT (matched + not-matched)."
          },
          {
            "slug": "dml:overwrite",
            "label": "INSERT OVERWRITE",
            "hint": "INSERT OVERWRITE and overwrite-schema patterns."
          },
          {
            "slug": "dml:truncate",
            "label": "TRUNCATE",
            "hint": "TRUNCATE / wipe-then-reload."
          },
          {
            "slug": "dml:cdc-write",
            "label": "CDC-emitting DML",
            "hint": "DML that writes CDF entries (UPDATE / DELETE on CDF-enabled tables)."
          }
        ]
      },
      {
        "key": "delta",
        "label": "Delta Lake Features",
        "blurb": "Delta protocol features a Databricks workload relies on.",
        "tags": [
          {
            "slug": "delta:deletion-vectors",
            "label": "Deletion vectors",
            "hint": "delta.enableDeletionVectors -- bitmap-based deletes."
          },
          {
            "slug": "delta:change-data-feed",
            "label": "Change Data Feed (CDF)",
            "hint": "delta.enableChangeDataFeed -- _change_type / commit-version stream."
          },
          {
            "slug": "delta:column-mapping",
            "label": "Column mapping",
            "hint": "delta.columnMapping.mode -- rename/drop without rewriting data."
          },
          {
            "slug": "delta:generated-columns",
            "label": "Generated columns",
            "hint": "GENERATED ALWAYS AS column definitions."
          },
          {
            "slug": "delta:identity-columns",
            "label": "Identity columns",
            "hint": "GENERATED ... AS IDENTITY columns."
          },
          {
            "slug": "delta:constraints",
            "label": "CHECK / NOT NULL",
            "hint": "CHECK constraints and NOT NULL enforcement."
          },
          {
            "slug": "delta:default-values",
            "label": "DEFAULT values",
            "hint": "Column-level DEFAULT clauses."
          },
          {
            "slug": "delta:partitioning",
            "label": "Partitioning",
            "hint": "PARTITIONED BY / partition pruning."
          },
          {
            "slug": "delta:liquid-clustering",
            "label": "Liquid clustering",
            "hint": "CLUSTER BY / liquid clustering."
          },
          {
            "slug": "delta:z-order",
            "label": "Z-ORDER",
            "hint": "OPTIMIZE ZORDER BY data layout."
          },
          {
            "slug": "delta:optimize",
            "label": "OPTIMIZE",
            "hint": "File compaction via OPTIMIZE."
          },
          {
            "slug": "delta:vacuum",
            "label": "VACUUM",
            "hint": "Physical file cleanup."
          },
          {
            "slug": "delta:time-travel",
            "label": "Time travel",
            "hint": "VERSION AS OF / TIMESTAMP AS OF / restoreable history."
          },
          {
            "slug": "delta:row-tracking",
            "label": "Row tracking",
            "hint": "delta.enableRowTracking -- stable row IDs."
          },
          {
            "slug": "delta:checkpoint-v1",
            "label": "Checkpoint v1",
            "hint": "Classic single-file Parquet checkpoint."
          },
          {
            "slug": "delta:checkpoint-v2",
            "label": "Checkpoint v2",
            "hint": "v2 spec format with explicit metadata."
          },
          {
            "slug": "delta:checkpoint-multipart",
            "label": "Multipart checkpoint",
            "hint": "Multipart checkpoint split across multiple files."
          },
          {
            "slug": "delta:checkpoint-sidecar",
            "label": "Sidecar checkpoint",
            "hint": "Checkpoint with sidecar files."
          },
          {
            "slug": "delta:log-compaction",
            "label": "Log compaction",
            "hint": "Compacted delta-log file ranges."
          }
        ]
      },
      {
        "key": "iceberg",
        "label": "Iceberg / UniForm",
        "blurb": "Cross-format interoperability for Iceberg consumers.",
        "tags": [
          {
            "slug": "iceberg:uniform",
            "label": "UniForm enabled",
            "hint": "delta.universalFormat.enabledFormats = iceberg."
          },
          {
            "slug": "iceberg:snapshots",
            "label": "Iceberg snapshots",
            "hint": "Metadata.json + snapshot file layout."
          },
          {
            "slug": "iceberg:partition-spec",
            "label": "Partition spec",
            "hint": "Iceberg partition spec evolution."
          },
          {
            "slug": "iceberg:format-version",
            "label": "Format v2",
            "hint": "Iceberg format-version=2 (delete files, positional/equality)."
          }
        ]
      },
      {
        "key": "schema",
        "label": "Schema Evolution",
        "blurb": "Can the schema change without rewriting history.",
        "tags": [
          {
            "slug": "schema:add-column",
            "label": "ADD COLUMN",
            "hint": "Append new columns to an existing table."
          },
          {
            "slug": "schema:drop-column",
            "label": "DROP COLUMN",
            "hint": "Drop columns under column mapping."
          },
          {
            "slug": "schema:rename-column",
            "label": "RENAME COLUMN",
            "hint": "Rename columns under column mapping."
          },
          {
            "slug": "schema:type-widening",
            "label": "Type widening",
            "hint": "INT -> BIGINT and other widening conversions."
          },
          {
            "slug": "schema:reorder",
            "label": "Reorder columns",
            "hint": "Reorder column position without rewriting data."
          },
          {
            "slug": "schema:field-id-reuse",
            "label": "Field-id reuse",
            "hint": "Reuse of field IDs after drop / rename."
          }
        ]
      },
      {
        "key": "scale",
        "label": "Scale & Performance",
        "blurb": "Wide schemas, large datasets, and read-time efficiency.",
        "tags": [
          {
            "slug": "scale:large-dataset",
            "label": "Large datasets",
            "hint": "Tables with >=10k rows or millions in a single file."
          },
          {
            "slug": "scale:wide-schema",
            "label": "Wide schema",
            "hint": ">=20 columns / mixed-type wide rows."
          },
          {
            "slug": "scale:many-batches",
            "label": "Many batches",
            "hint": "Tables built via many small append batches."
          },
          {
            "slug": "scale:nested-types",
            "label": "Nested types",
            "hint": "Struct / array / map with non-trivial nesting."
          }
        ]
      },
      {
        "key": "storage",
        "label": "Storage & Files",
        "blurb": "Parquet-level behaviour and physical statistics.",
        "tags": [
          {
            "slug": "storage:parquet-encoding",
            "label": "Parquet encodings",
            "hint": "Plain, dictionary, RLE, delta-binary-packed."
          },
          {
            "slug": "storage:parquet-compression",
            "label": "Parquet compression",
            "hint": "Snappy / Zstd / Gzip / Uncompressed."
          },
          {
            "slug": "storage:rowgroup-stats",
            "label": "Row-group statistics",
            "hint": "Parquet row-group min/max vs Delta stats."
          },
          {
            "slug": "storage:statistics",
            "label": "Delta statistics",
            "hint": "Column-level stats and string truncation."
          }
        ]
      },
      {
        "key": "robustness",
        "label": "Robustness",
        "blurb": "How the system behaves when something is wrong or contested.",
        "tags": [
          {
            "slug": "robust:concurrent-writes",
            "label": "Concurrent writes",
            "hint": "Two writers, conflict detection."
          },
          {
            "slug": "robust:log-corruption",
            "label": "Log corruption",
            "hint": "Recovery from corrupted log entries."
          },
          {
            "slug": "robust:malformed-input",
            "label": "Malformed input",
            "hint": "Malformed JSON / partial files."
          },
          {
            "slug": "robust:cross-version",
            "label": "Cross-version",
            "hint": "Cross-version protocol compatibility."
          },
          {
            "slug": "robust:checkpoint-missing",
            "label": "Missing checkpoint parts",
            "hint": "Multipart checkpoint with missing parts."
          }
        ]
      },
      {
        "key": "context",
        "label": "Context",
        "blurb": "Format and round-trip direction filters.",
        "tags": [
          {
            "slug": "format:delta",
            "label": "Delta Lake",
            "hint": "Delta-format tables."
          },
          {
            "slug": "format:iceberg",
            "label": "Apache Iceberg",
            "hint": "Iceberg-format tables (incl. UniForm-bridged)."
          },
          {
            "slug": "direction:df-writes",
            "label": "DeltaForge writes",
            "hint": "DeltaForge writes, Spark reads."
          },
          {
            "slug": "direction:spark-writes",
            "label": "Spark writes",
            "hint": "Spark writes, DeltaForge reads."
          },
          {
            "slug": "direction:chaos",
            "label": "Coexistence",
            "hint": "Both engines touch the same table."
          }
        ]
      }
    ]
  },
  "tag_counts": {
    "delta:change-data-feed": 1070,
    "delta:checkpoint-multipart": 12,
    "delta:checkpoint-sidecar": 8,
    "delta:checkpoint-v1": 4,
    "delta:checkpoint-v2": 2,
    "delta:column-mapping": 4166,
    "delta:constraints": 680,
    "delta:default-values": 518,
    "delta:deletion-vectors": 7254,
    "delta:generated-columns": 178,
    "delta:identity-columns": 346,
    "delta:liquid-clustering": 56,
    "delta:log-compaction": 4,
    "delta:optimize": 900,
    "delta:partitioning": 1130,
    "delta:row-tracking": 190,
    "delta:time-travel": 526,
    "delta:vacuum": 350,
    "delta:z-order": 322,
    "direction:df-writes": 7522,
    "dml:cdc-write": 840,
    "dml:delete": 2030,
    "dml:insert": 7492,
    "dml:merge": 1284,
    "dml:overwrite": 340,
    "dml:truncate": 84,
    "dml:update": 1916,
    "format:delta": 3761,
    "format:iceberg": 3761,
    "iceberg:format-version": 2,
    "iceberg:partition-spec": 4,
    "iceberg:snapshots": 4,
    "iceberg:uniform": 3789,
    "robust:checkpoint-missing": 2,
    "robust:concurrent-writes": 26,
    "robust:cross-version": 2,
    "robust:log-corruption": 2,
    "robust:malformed-input": 2,
    "scale:large-dataset": 370,
    "scale:many-batches": 20,
    "scale:nested-types": 90,
    "scale:wide-schema": 242,
    "schema:add-column": 606,
    "schema:drop-column": 78,
    "schema:field-id-reuse": 2,
    "schema:rename-column": 82,
    "schema:type-widening": 138,
    "storage:parquet-compression": 4,
    "storage:parquet-encoding": 10,
    "storage:rowgroup-stats": 6,
    "storage:statistics": 30,
    "type:array": 86,
    "type:binary": 60,
    "type:boolean": 612,
    "type:boundary": 114,
    "type:date": 264,
    "type:decimal": 1104,
    "type:floating": 1756,
    "type:integer": 7492,
    "type:map": 76,
    "type:null-handling": 34,
    "type:string": 5812,
    "type:struct": 224,
    "type:timestamp": 816,
    "type:timestamp-ntz": 36,
    "type:unicode": 72
  }
}