beam
https://github.com/apache/beam
Java
Apache Beam is a unified programming model for Batch and Streaming data processing.
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- Issues
- The pip in /sdks/python/container/py314 for nltk - Update #1704404192 job is flaky
- [Bug]: [Python] VertexAIFeatureStoreEnrichmentHandler raises UnboundLocalError on NotFound when exception_level=ExceptionLevel.QUIET
- [Bug]: [Python] BigTableEnrichmentHandler coerces None lookup keys to literal b'None' and formats blank key name on KeyError
- [Bug]: [Python] FeastFeatureStoreEnrichmentHandler crashes get_cache_key with entity_row_fn, ignores exception_level, and leaks temp YAML files
- [Bug]: [Python] Batched Enrichment / RequestResponseIO resets per-element event-time timestamps to END_OF_GLOBAL_WINDOW
- [Bug]: [Python/YAML] AssignTimestamps raises TypeError on ISO-8601 timestamp strings and datetime objects
- [Bug]: [Python/YAML] Combine infers typing.Any output type for builtin CombineFns (count, mean, group, concat), breaking WriteToParquet
- [Bug]: [Python/ML] RunInference.with_exception_handling() documents Exception tuple while emitting str, and shared.Shared fails on dict models
- [Feature Request]: [Python/YAML] Support trigger, allowed_lateness, and accumulation_mode in Beam YAML WindowInto
- [Feature Request]: [Python/YAML] Add declarative RunInference provider in Beam YAML with HuggingFace, Vertex AI, and error_handling DLQ
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- Java not yet supported