Add Picosecond timestamp support to storage API WriteTableRows

[claudevdm]

Adds the ability to write up to picosecond precision timestamps to BQ with writeTableRows. It includes round trip write/read integration tests.

Notes for reviewer:

1. Reading at picosecond precision from BQ returns an ISO string with 12 decimals. We handle converting this to the storage api proto message {seconds, picos} with a special check if the tablerow timestamp has 12 decimals and is in iso format.
2. Reading at micros/nanos precision formats timestamps as UTC strings. This can have up to 9 decimal places (nanos). This is parsed into a java.time.Instant
3. Arrow currently does not support nested types when reading from storage API because types are not recursively converted in

   beam/sdks/java/extensions/arrow/src/main/java/org/apache/beam/sdk/extensions/arrow/ArrowConversion.java

   Line 372 in cc8dc2f

   return Optional.empty();

   . The result is that the tablerow contains the native arrow type in a beam.Row, which cannot be converted to a GenericRecord etc

   beam/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryStorageArrowReader.java

   Line 75 in cc8dc2f

   return AvroUtils.toGenericRecord(row, null);

   . We could add support for nested arrow types but that is beyond the scope of this PR.

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[gemini-code-assist]

Summary of Changes

Hello @claudevdm, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances BigQueryIO by introducing full support for BigQuery TIMESTAMP columns with picosecond precision (12 fractional digits) when utilizing the BigQuery Storage Read API. The core objective is to prevent data loss for highly precise timestamp values by providing a mechanism for users to explicitly control the desired precision during data ingestion. This involves extending the BigQueryIO API with a new configuration option, updating internal data conversion and serialization logic for both Avro and Arrow formats, and ensuring that these changes are robustly tested through new unit and integration tests.

Highlights

• Picosecond Timestamp Support: BigQueryIO now supports reading and writing BigQuery TIMESTAMP columns with picosecond precision (12 fractional digits) when using the Storage Read API. This ensures that high-fidelity timestamp data can be accurately processed without loss of precision.
• New Configuration Option: A new withDirectReadPicosTimestampPrecision method has been added to BigQueryIO.TypedRead, allowing users to explicitly specify the desired timestamp precision (MICROS, NANOS, or PICOS) for direct reads.
• Internal Data Handling Enhancements: Internal utilities like BigQueryUtils and TableRowToStorageApiProto have been updated to correctly parse, represent, and serialize picosecond-precision timestamps. This includes a new TimestampPicos structure for internal representation and dynamic proto schema generation for 12-digit precision timestamps.
• Cross-Language Compatibility: The BigQueryIOReadTranslator has been updated to ensure that the new timestamp precision setting is correctly propagated and translated in cross-language transforms, maintaining consistency across different Beam SDKs.
• Comprehensive Testing: Extensive new unit and integration tests have been added to validate the correct handling of picosecond, nanosecond, and microsecond precision timestamps across both Avro and Arrow data formats, covering various read scenarios and ensuring data integrity.

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[claudevdm]

/gemini review

[claudevdm]

R: @damccorm

[claudevdm]

R: @Abacn

[claudevdm]

R: @ahmedabu98

[gemini-code-assist]

Code Review

This pull request adds support for writing picosecond-precision timestamps to BigQuery using the Storage Write API. The changes include introducing a TimestampPicos helper class and parsing logic in BigQueryUtils, updating TableRowToStorageApiProto to handle the new TimestampPicos protobuf message type during schema and data conversion, and adding comprehensive integration and unit tests to validate the new functionality. The implementation is solid and the tests are thorough. I have one suggestion to improve maintainability by deduplicating a constant.

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