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Bump apache-beam from 2.75.0 to 2.77.0 - #64

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Bumps apache-beam from 2.75.0 to 2.77.0.

Release notes

Sourced from apache-beam's releases.

Beam 2.77.0 release

We are happy to present the new 2.77.0 release of Beam. This release includes both improvements and new functionality. See the download page for this release.

For more information on changes in 2.77.0, check out the detailed release notes.

Highlights

I/Os

  • Added schema_update_options to WriteToBigQuery file loads, allowing BigQuery load jobs to add nullable fields or relax required fields when appending data (Python) (#21141).
  • BigQueryIO now supports reading BigQuery Lakehouse runtime catalog (BigLake metastore) Iceberg tables with the Storage Read API, using 4-part project.catalog.namespace.table identifiers (or a TableReference with a composite catalog.namespace dataset id). Previously such references were silently mis-parsed (Java) (#39597) .
  • SolaceIO now supports reading and writing binary and text content data payload (Java) (#39875).
  • ClickHouseIO: support writing Decimal(P, S) / Decimal32/64/128/256 columns (Java) (#39840).
  • SolaceIO now supports reading and writing user properties (message metadata) (Java) (#40099).
  • [IcebergIO] AddFiles (IcebergAddFiles in YAML) can evolve the table schema before registering files, with schema_evolution_options, required_columns, incompatible_schema_handling and unverifiable_file_handling (Java/YAML, batch only) (#40144).

New Features / Improvements

  • (Java/Python) Watch can bound its deduplication state by event time, retiring an output key once the greatest emitted timestamp has moved more than the allowed lateness past it. Java adds Watch.growthOf(...).withTimestampCursor(). Python adds allowed_lateness for the existing timestamp_cursor option (#18459).
  • (Java) Spark Structured Streaming runner: stateful ParDo with state, timers, @RequiresTimeSortedInput and tagged outputs is now supported in batch mode (#39779).
  • (Python) Added support for Vertex AI Model Monitoring V2 in RunInference (#39738).
  • [Flink Runner] Added opt-in static round-robin split assignment for small bounded sources via the new sourceStaticSplitThresholdMb pipeline option. The default of 0 keeps the existing lazy pull-based assignment (#39873).
  • Added automatic caching of bounded, single-pane side-input views for classic Java Flink DataStream execution (#39866).
  • (Python) Added Sample.Any, the Python equivalent of Java's Sample.any, which returns up to n arbitrary elements from a PCollection (#18552).

Breaking Changes

  • Portable Java SDK now encodes SchemaCoders in a portable way (#34672).
  • (Python) TensorRTEngineHandlerNumPy now requires TensorRT 10 or later. TensorRT 8.x is no longer supported, since TensorRT 10 removed the engine binding API the handler was written against (#36306).
    • Engines serialized by TensorRT 8.x must be rebuilt, as an engine can only be deserialized by the major version that built it.
    • TensorRT 10 and later require a GPU with compute capability 7.5 or higher, which excludes NVIDIA Pascal and Volta GPUs.
    • If dropping TensorRT 8.x support is a hard blocker for you, please comment on (#36306).

Bugfixes

  • (Java) Fixed the Spark runner firing processing-time timers in reverse timestamp order (#39824).
  • (Java) Fixed the Spark runner dropping the stored watermark of a streaming source with no update in a batch (#39822).
  • (Python) Fixed incorrect profiler options handling on portable runners (#39613).
  • (Java) KafkaIO dynamic reads no longer require the obsolete beam_fn_api experiment (#29998).
  • (Prism) Self-checkpointing splittable DoFns now resume after their requested delay instead of immediately, so polling SDFs no longer busy-spin (#39848).
  • (Java) MongoDbIO read splitting now preserves non-ObjectId _id types (e.g. string ids) instead of failing to parse the generated range filters (#39900).
  • (Go) Fixed GCS glob matching silently dropping objects when the glob pattern contains multi-byte characters (#39969).
  • (Python) Fixed TensorRTEngineHandlerNumPy failing with CUDA_ERROR_INVALID_VALUE on models with a single-element input or output tensor (#36306).
  • (Python) Fixed PickleCoder/_MemoizingPickleCoder.as_deterministic_coder() raising TypeError instead of returning a working deterministic coder (#28558).

... (truncated)

Changelog

Sourced from apache-beam's changelog.

[2.77.0] - 2026-10-07

I/Os

  • Added schema_update_options to WriteToBigQuery file loads, allowing BigQuery load jobs to add nullable fields or relax required fields when appending data (Python) (#21141).
  • BigQueryIO now supports reading BigQuery Lakehouse runtime catalog (BigLake metastore) Iceberg tables with the Storage Read API, using 4-part project.catalog.namespace.table identifiers (or a TableReference with a composite catalog.namespace dataset id). Previously such references were silently mis-parsed (Java) (#39597) .
  • SolaceIO now supports reading and writing binary and text content data payload (Java) (#39875).
  • ClickHouseIO: support writing Decimal(P, S) / Decimal32/64/128/256 columns (Java) (#39840).
  • SolaceIO now supports reading and writing user properties (message metadata) (Java) (#40099).
  • [IcebergIO] AddFiles (IcebergAddFiles in YAML) can evolve the table schema before registering files, with schema_evolution_options, required_columns, incompatible_schema_handling and unverifiable_file_handling (Java/YAML, batch only) (#40144).
  • [IcebergIO] Added batch and streaming CDC writes that applies INSERT/UPDATE_BEFORE/UPDATE_AFTER/DELETE changes to Iceberg V2+ tables by primary key. Invoke with IcebergIO.writeCdcRows (Java) or by setting mode: merge-on-read on the Managed ICEBERG write (Java, Python, YAML) (#39979).
  • [IcebergIO] Added an optional side-input table cache for writes to significantly reduce catalog and table requests for large pipelines. A single worker polls the table and broadcasts it to other workers in the pipeline. Enable with IcebergIO.writeRows(...).withSideInputTableCache() (Java) or by setting use_side_input_table_cache: true on the Managed ICEBERG write (Java, Python) (#39723).

New Features / Improvements

  • (Java/Python) Watch can bound its deduplication state by event time, retiring an output key once the greatest emitted timestamp has moved more than the allowed lateness past it. Java adds Watch.growthOf(...).withTimestampCursor(). Python adds allowed_lateness for the existing timestamp_cursor option (#18459).
  • (Java) Spark Structured Streaming runner: stateful ParDo with state, timers, @RequiresTimeSortedInput and tagged outputs is now supported in batch mode (#39779).
  • (Python) Added support for Vertex AI Model Monitoring V2 in RunInference (#39738).
  • [Flink Runner] Added opt-in static round-robin split assignment for small bounded sources via the new sourceStaticSplitThresholdMb pipeline option. The default of 0 keeps the existing lazy pull-based assignment (#39873).
  • Added automatic caching of bounded, single-pane side-input views for classic Java Flink DataStream execution (#39866).
  • (Python) Added Sample.Any, the Python equivalent of Java's Sample.any, which returns up to n arbitrary elements from a PCollection (#18552).

Breaking Changes

  • Portable Java SDK now encodes SchemaCoders in a portable way (#34672).
  • (Python) TensorRTEngineHandlerNumPy now requires TensorRT 10 or later. TensorRT 8.x is no longer supported, since TensorRT 10 removed the engine binding API the handler was written against (#36306).
    • Engines serialized by TensorRT 8.x must be rebuilt, as an engine can only be deserialized by the major version that built it.
    • TensorRT 10 and later require a GPU with compute capability 7.5 or higher, which excludes NVIDIA Pascal and Volta GPUs.
    • If dropping TensorRT 8.x support is a hard blocker for you, please comment on (#36306).

Bugfixes

  • (Java) Fixed the Spark runner firing processing-time timers in reverse timestamp order (#39824).
  • (Java) Fixed the Spark runner dropping the stored watermark of a streaming source with no update in a batch (#39822).
  • (Python) Fixed incorrect profiler options handling on portable runners (#39613).
  • (Python) Fixed AsyncWrapper mixing results between keys or windows whose values or custom identifiers match, and cancelling work belonging to other windows (#39996).
  • (Java) KafkaIO dynamic reads no longer require the obsolete beam_fn_api experiment (#29998).
  • (Prism) Self-checkpointing splittable DoFns now resume after their requested delay instead of immediately, so polling SDFs no longer busy-spin (#39848).
  • (Java) MongoDbIO read splitting now preserves non-ObjectId _id types (e.g. string ids) instead of failing to parse the generated range filters (#39900).
  • (Go) Fixed GCS glob matching silently dropping objects when the glob pattern contains multi-byte characters (#39969).
  • (Python) Fixed TensorRTEngineHandlerNumPy failing with CUDA_ERROR_INVALID_VALUE on models with a single-element input or output tensor (#36306).
  • (Python) Fixed PickleCoder/_MemoizingPickleCoder.as_deterministic_coder() raising TypeError instead of returning a working deterministic coder (#28558).

[2.76.0] - 2026-08-31

Highlights

  • Added a full Iceberg batch and streaming changelog source (CDC) (#38831)

... (truncated)

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Bumps [apache-beam](https://github.com/apache/beam) from 2.75.0 to 2.77.0.
- [Release notes](https://github.com/apache/beam/releases)
- [Changelog](https://github.com/apache/beam/blob/master/CHANGES.md)
- [Commits](apache/beam@v2.75.0...v2.77.0)

---
updated-dependencies:
- dependency-name: apache-beam
  dependency-version: 2.77.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Oct 13, 2026
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