Skip to content

[#19711] Fix missing step_id in Python SDK worker logs during DoFn setup #37413

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Open
Tanishq1030 wants to merge 1 commit into
base: master
Choose a base branch
from
Open

[#19711] Fix missing step_id in Python SDK worker logs during DoFn setup #37413

Tanishq1030 wants to merge 1 commit into from
+16 −6

Conversation

@Tanishq1030
Copy link

@Tanishq1030 Tanishq1030 commented Jan 25, 2026

Fixes #19711

This PR addresses the issue where step_id (instruction ID) was consistently missing or empty in worker logs generated during the DoFn.setup() lifecycle method.

Rationale

The FnApiLogRecordHandler relies on statesampler thread-local storage to populate the instruction_id in log entries. Previously, the BundleProcessor executed the setup() method for operations before the thread-local context was fully initialized for that instruction, causing logs emitted during setup to become orphaned (missing metadata).

Changes

  1. sdks/python/apache_beam/runners/worker/sdk_worker.py: Updated create_bundle_processor to pass the active instruction_id into the BundleProcessor constructor.
  2. sdks/python/apache_beam/runners/worker/bundle_processor.py:
    • Updated __init__ to accept instruction_id.
    • Added logic to manually inject the instruction_id into the statesampler context specifically while iterating through operations to call op.setup().
  3. sdks/python/apache_beam/runners/worker/log_handler.py: Updated emit() to check record.instruction_id before falling back to thread-local storage, ensuring explicitly injected IDs are respected.

Verification

I verified this fix locally using a reproduction script which forces a log during setup().

  • Before fix: Logs during setup() had instruction_id: None.
  • After fix: Logs during setup() correctly display the instruction_id (e.g., bundle_...).
  • Mention the appropriate issue in your description (e.g. fixes #19711).
  • Update CHANGES.md with noteworthy changes.
  • If this contribution is large, please file an Apache Individual Contributor License Agreement.

@gemini-code-assist
Copy link
Contributor

gemini-code-assist bot commented Jan 25, 2026

Summary of Changes

Hello @Tanishq1030, 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 resolves a critical logging issue in the Python SDK worker where step_id metadata was missing from logs emitted during the DoFn.setup() phase. By ensuring the instruction_id is correctly propagated and made available to the logging context during this early lifecycle method, the change significantly improves the debuggability and traceability of Beam pipelines, allowing developers to better understand the context of setup-related issues.

Highlights

  • Problem Resolution: Fixed an issue where the step_id (instruction ID) was consistently missing or empty in worker logs generated during the DoFn.setup() lifecycle method.
  • Root Cause Addressed: The FnApiLogRecordHandler previously failed to populate instruction_id because DoFn.setup() was executed before the statesampler thread-local context was fully initialized for the instruction.
  • BundleProcessor Enhancement: The BundleProcessor now accepts the active instruction_id during its creation and explicitly injects it into the statesampler context when calling op.setup().
  • Log Handler Priority: The FnApiLogRecordHandler's emit() method has been updated to prioritize an explicitly provided instruction_id on the log record before attempting to retrieve it from thread-local storage.
  • Verification: The fix was confirmed locally using a reproduction script, showing correct instruction_id in setup() logs post-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.

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. ↩

@github-actions
Copy link
Contributor

github-actions bot commented Jan 25, 2026

Assigning reviewers:

R: @claudevdm 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).

@github-actions
Copy link
Contributor

github-actions bot commented Feb 1, 2026

Reminder, please take a look at this pr: @claudevdm

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

Next Action: Reviewers python runners slow-review

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

Dataflow Python SDK logging: step_id is always empty string

1 participant

@Tanishq1030