fix(python): Register all declared DoOutputsTuple outputs in composite transforms

[hjtran]

Summary

• Fix composite transform output registration to use all declared tags instead of lazily-populated _pcolls dict
• Prevents missing outputs in pipeline proto when DoOutputsTuple subclasses haven't accessed all outputs at registration time
• Add regression test for DoOutputsTuple subclass with __or__ override

Description

PR #36220 changed composite output registration to iterate over result._pcolls.items(). However, _pcolls is lazily populated — PCollections are only added when accessed via __getitem__. This means unaccessed outputs are silently dropped from the composite's registered outputs.

This breaks when a DoOutputsTuple subclass (e.g. one that overrides __or__ to pipe to the main output) is returned from a composite's expand(). At registration time, only outputs that happened to be accessed are in _pcolls, so the main output may be missing from the pipeline proto. This causes disconnected edges in pipeline visualization.

Fix: Iterate over all declared tags (_main_tag + _tags) and access each via result[tag] to trigger lazy creation, ensuring all outputs are registered.

Test plan

• New test test_do_outputs_tuple_subclass_registers_all_outputs verifies consumed PCollection appears in composite's proto outputs
• Existing test_multiple_outputs_composite_ptransform updated to expect all 3 outputs (main + 2 tagged)
• Full pipeline_test.py suite passes (63 passed, 2 skipped)

🤖 Generated with Claude Code

[gemini-code-assist]

Summary of Changes

Hello @hjtran, 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 addresses a critical issue in Apache Beam's Python SDK where composite transforms returning DoOutputsTuple subclasses could lead to incomplete pipeline definitions. Previously, the output registration process relied on a lazily populated internal dictionary, causing unaccessed outputs to be omitted from the pipeline proto. The fix ensures that all declared outputs are explicitly accessed and registered, guaranteeing that the pipeline's structure is accurately represented and preventing logical disconnections in the pipeline graph.

Highlights

• Composite Transform Output Registration Fix: The mechanism for registering outputs of composite transforms that return DoOutputsTuple subclasses has been fixed to ensure all declared tags are registered, rather than relying on the lazily-populated _pcolls dictionary.
• Prevention of Missing Outputs: This change prevents outputs from being silently dropped from the pipeline proto, especially when DoOutputsTuple subclasses haven't accessed all their outputs at the time of registration, thus avoiding disconnected edges in pipeline visualizations.
• New Regression Test: A new regression test, test_do_outputs_tuple_subclass_registers_all_outputs, has been added to specifically verify that DoOutputsTuple subclasses, particularly those with __or__ overrides, correctly register all their declared outputs.
• Existing Test Update: The existing test_multiple_outputs_composite_ptransform has been updated to correctly assert for three outputs (main + two tagged) instead of two, reflecting the expected behavior after the fix.

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Changelog

• sdks/python/apache_beam/pipeline.py
  • Modified the apply method within the Pipeline class to correctly register all outputs of pvalue.DoOutputsTuple instances returned by composite transforms.
  • Changed the iteration logic from result._pcolls.items() to explicitly iterate over [result._main_tag] + list(result._tags) and access each output via result[tag] to trigger lazy creation and ensure registration.
• sdks/python/apache_beam/pipeline_test.py
  • Updated test_multiple_outputs_composite_ptransform to assert that the 'Split Sales' transform has 3 outputs (main + 2 tagged), correcting a previous assertion of 2 outputs.
  • Added a new test case, test_do_outputs_tuple_subclass_registers_all_outputs, which defines a custom DoOutputsTuple subclass (PipeToMain) and a composite transform (MyComposite) to verify that all declared outputs are correctly registered in the pipeline proto, even if not explicitly accessed before registration.

Activity

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