DataHub Python Builds

These prebuilt wheel files can be used to install our Python packages as of a specific commit.

Build context

Built at 2025-12-03T06:44:43.043483+00:00.

{
  "timestamp": "2025-12-03T06:44:43.043483+00:00",
  "branch": "bart/refactor--regex-perf-opt-c9ac0667",
  "commit": {
    "hash": "c273133c7a74b8a3d127b5fe1d9b0e7d00fff68c",
    "message": "perf(ingestion): pre-compile regex patterns in hot paths\n\nExtends Rob's regex optimization pattern (#15463) to additional ingestion hot paths:\n\n1. **SqlQueriesSource**: Pre-compile temp_table_patterns using @cached_property\n   - Called for every table during query processing\n   - Eliminates repeated regex compilation overhead\n\n2. **BigQuery**: Pre-compile sharded table & wildcard patterns at module level\n   - get_table_and_shard(): Called for every BigQuery table\n   - get_table_display_name(): Called for table name normalization\n   - is_sharded_table(): Called during table classification\n\n3. **PowerBI ODBC**: Pre-compile platform detection patterns at module level\n   - normalize_platform_from_driver(): Called for every ODBC connection\n   - normalize_platform_name(): Called during platform normalization\n   - Affects 18+ database platform patterns\n\nAll changes follow the same optimization strategy as #15463:\n- Compile regex patterns once at initialization\n- Use compiled Pattern objects in hot path\n- Maintain exact behavioral equivalence\n- No config changes or breaking changes\n\nExpected impact: Performance improvement for ingestion workloads with:\n- High volume of temp table checks (SqlQueriesSource)\n- Large BigQuery datasets with sharded tables\n- PowerBI sources with many ODBC connections\n\n\ud83e\udd16 Generated with [Claude Code](https://claude.com/claude-code)\n\nCo-Authored-By: Claude "
  },
  "pr": {
    "number": 15470,
    "title": "perf(ingestion): pre-compile regex patterns in hot paths",
    "url": "https://github.com/datahub-project/datahub/pull/15470"
  }
}

Usage

Current base URL: unknown

Package	Size	Install command
acryl-datahub	2.507 MB	uv pip install 'acryl-datahub @ <base-url>/artifacts/wheels/acryl_datahub-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-actions	0.101 MB	uv pip install 'acryl-datahub-actions @ <base-url>/artifacts/wheels/acryl_datahub_actions-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-airflow-plugin	0.039 MB	uv pip install 'acryl-datahub-airflow-plugin @ <base-url>/artifacts/wheels/acryl_datahub_airflow_plugin-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-dagster-plugin	0.019 MB	uv pip install 'acryl-datahub-dagster-plugin @ <base-url>/artifacts/wheels/acryl_datahub_dagster_plugin-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-gx-plugin	0.010 MB	uv pip install 'acryl-datahub-gx-plugin @ <base-url>/artifacts/wheels/acryl_datahub_gx_plugin-0.0.0.dev1-py3-none-any.whl'
prefect-datahub	0.011 MB	uv pip install 'prefect-datahub @ <base-url>/artifacts/wheels/prefect_datahub-0.0.0.dev1-py3-none-any.whl'