[#37209] Enhance serialization error messages for better developer experience

[PDGGK]

What changes are being proposed in this pull request?

This PR addresses issue #37209 by significantly improving error messages when user code fails to serialize (pickle) for distributed execution.

Why are these changes needed?

Currently, when users pass non-serializable lambdas or closures (e.g., capturing a file handle or database connection), they get cryptic low-level errors like:

RuntimeError: Unable to pickle fn <function>: <PicklingError>

This doesn't explain:

• Why serialization is required
• What commonly causes the error
• How to fix it

This is especially frustrating for new Apache Beam users who don't understand distributed execution requirements.

Changes made:

1. Enhanced error message (ptransform.py)

The new error message includes:

• Clear explanation: "User code must be serializable (picklable) for distributed execution"
• Common causes: "This usually happens when lambdas or closures capture non-serializable objects like file handles, database connections, or thread locks"
• Concrete fixes:
  1. Using module-level functions instead of lambdas
  2. Initializing resources in setup() methods
  3. Checking what your closure captures

2. Broader exception handling

Changed from catching only RuntimeError to (RuntimeError, TypeError, Exception) because:

• cloudpickle/dill can raise TypeError or PicklingError
• Ensures the helpful message appears for all pickling failures

3. Exception chaining

Added from e to preserve the original exception context and stack trace for debugging.

4. Test coverage

Added test_callable_non_serializable_error_message() to verify:

• The error is raised correctly
• The new guidance text appears in the message

Testing

• ✅ 202 tests passed in ptransform_test.py
• ✅ New test explicitly verifies the error message content
• ✅ Manual testing with non-serializable closures confirms the improved message

Impact

• Developer Experience: Significantly reduces debugging time for serialization issues
• Stability: No change to execution logic; pure diagnostic improvement
• Compatibility: No impact on existing pipelines (still raises RuntimeError)

Example

Before:

RuntimeError: Unable to pickle fn <function>: cannot serialize <_io.TextIOWrapper>

After:

RuntimeError: Unable to pickle fn <function>: cannot serialize <_io.TextIOWrapper>. 
User code must be serializable (picklable) for distributed execution. 
This usually happens when lambdas or closures capture non-serializable objects 
like file handles, database connections, or thread locks. Try: (1) using 
module-level functions instead of lambdas, (2) initializing resources in 
setup() methods, (3) checking what your closure captures.

Fixes #37209

[gemini-code-assist]

Summary of Changes

Hello @PDGGK, 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 significantly enhances the developer experience in Apache Beam by transforming cryptic serialization error messages into clear, actionable guidance. By providing detailed explanations of why serialization is required, common pitfalls like capturing non-serializable objects, and concrete solutions, it aims to drastically reduce the debugging time for users encountering these issues, particularly those new to distributed execution paradigms.

Highlights

• Improved Serialization Error Messages: Enhanced the error messages for non-serializable user code, providing clear explanations, common causes, and actionable solutions.
• Broader Exception Handling: Expanded the exception handling for pickling failures to catch RuntimeError, TypeError, and general Exception to ensure comprehensive coverage.
• Preserved Exception Context: Implemented exception chaining (from e) to retain the original stack trace and context for better debugging.
• New Test Coverage: Added a dedicated test case to validate the new, informative serialization error message content.

🧠 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]

Checks are failing. Will not request review until checks are succeeding. If you'd like to override that behavior, comment assign set of reviewers

[github-actions]

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).