[Python] Ad BigQuery Change History source.

[claudevdm]

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[gemini-code-assist]

Summary of Changes

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This pull request introduces a new PTransform for Apache Beam's Python SDK, enabling streaming Change Data Capture (CDC) from Google BigQuery. It allows users to continuously ingest changes from BigQuery tables using either the APPENDS() or CHANGES() SQL functions. The design focuses on robust watermark handling, efficient data retrieval via the BigQuery Storage Read API, and automatic cleanup of temporary resources, providing a reliable and scalable solution for real-time data synchronization with BigQuery.

Highlights

• New BigQuery CDC PTransform: Introduced a new ReadBigQueryChangeHistory PTransform in the Python SDK, enabling streaming Change Data Capture (CDC) from Google BigQuery.
• Support for APPENDS/CHANGES functions: The PTransform is designed to continuously poll BigQuery's APPENDS() or CHANGES() SQL functions to capture data modifications.
• Modular Architecture: The implementation is structured into distinct stages: a polling Splittable DoFn (SDF) for generating query ranges, an _ExecuteQueryFn for running BQ queries and writing results to temporary tables, an SDF for reading from these temporary tables via the Storage Read API with dynamic splitting, and a stateful _CleanupTempTablesFn for managing and deleting temporary tables.
• Advanced Watermark Management: Custom watermark estimators (_PollWatermarkEstimator, _CDCWatermarkEstimatorProvider) are implemented to accurately track watermarks based on poll cursors and event timestamps, ensuring correct processing order.
• Automated Temporary Resource Management: The system automatically creates temporary datasets with a 24-hour expiration policy for robustness and ensures cleanup of temporary tables after data has been processed.
• Comprehensive Testing: New integration tests (bigquery_change_history_it_test.py) validate the PTransform against a real BigQuery environment, and unit tests (bigquery_change_history_test.py) cover query building logic, range computation, and input validation.

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Changelog

• sdks/python/apache_beam/io/gcp/bigquery_change_history.py
  • Added ReadBigQueryChangeHistory PTransform for BigQuery CDC.
  • Implemented polling SDF (_PollChangeHistoryFn) for query range generation.
  • Introduced _ExecuteQueryFn to run BQ queries and manage temporary tables.
  • Developed _ReadStorageStreamsSDF for reading data from temporary tables using the BigQuery Storage Read API.
  • Included _CleanupTempTablesFn for stateful cleanup of temporary BigQuery tables.
  • Defined helper functions like build_changes_query and compute_ranges for SQL generation and time range management.
  • Added custom watermark estimators (_PollWatermarkEstimator, _CDCWatermarkEstimatorProvider) for accurate watermark tracking.
• sdks/python/apache_beam/io/gcp/bigquery_change_history_it_test.py
  • Added integration tests for ReadBigQueryChangeHistory and its internal components.
  • Included tests for _CleanupTempTablesFn, _ReadStorageStreamsSDF, _PollChangeHistoryFn, and _ExecuteQueryFn.
  • Implemented an end-to-end test for the public API.
• sdks/python/apache_beam/io/gcp/bigquery_change_history_test.py
  • Added unit tests for build_changes_query, compute_ranges, _table_key, and ReadBigQueryChangeHistory validation.

Activity

• The pull request introduces a new feature and associated tests, with no explicit comments or reviews mentioned in the provided context.

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[claudevdm]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a new BigQuery CDC (Change Data Capture) source, ReadBigQueryChangeHistory. This is a significant and well-implemented feature, including the core PTransform, comprehensive unit tests, and integration tests. The architecture is robust, correctly leveraging advanced Beam concepts like Splittable DoFns, stateful DoFns for cleanup, and custom watermark estimators. My review includes suggestions to improve error handling robustness, adhere to Python style guidelines, and clean up some unused code in the integration tests.

[claudevdm]

R: @damccorm

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