fix(python): Register all output pcollections of a transform rather than only ones that happened to be accessed in DoOutputsTuple

[hjtran]

Summary

• Fix composite transform output registration to iterate over all declared tags instead of the lazily-populated _pcolls dict
• Ensures pipeline proto graph is complete and correct for composites returning DoOutputsTuple

Description

PR #36220 changed composite output registration to iterate over result._pcolls.items(). However, _pcolls is lazily populated — entries are only added when accessed via __getitem__. Any output that hasn't been accessed at registration time is silently missing from the composite's outputs in the pipeline proto, producing an incorrect graph with disconnected edges.

Fix: Iterate over all declared tags (_main_tag + _tags) and access each via result[tag], ensuring all outputs are materialized and registered regardless of prior access patterns.

🤖 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.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

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

• No human activity (comments, reviews, etc.) has been recorded for this pull request yet.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[hjtran]

The single CI failure is unrelated to this change — it's a flaky BigQuery cloud test (test_read_export_exception_1) that got a DEADLINE_EXCEEDED gRPC timeout instead of the expected accessDenied error.

[github-actions]

Assigning reviewers:

R: @jrmccluskey for label python.

Note: If you would like to opt out of this review, comment assign to next reviewer.

Available commands:

• stop reviewer notifications - opt out of the automated review tooling
• remind me after tests pass - tag the comment author after tests pass
• waiting on author - shift the attention set back to the author (any comment or push by the author will return the attention set to the reviewers)

The PR bot will only process comments in the main thread (not review comments).