[Python] Improve error messaging for PTransform serialization failures

[Kalpana-chavhan]

Description

This PR improves the Developer Experience (DX) when a user-defined function (UDF) or transform fails to serialize during pipeline construction. Currently, users are presented with a generic PicklingError or AttributeError from the internal pickler, which can be opaque to developers new to distributed processing.

The change wraps these failures in a descriptive RuntimeError that identifies the specific failing function and provides a clear, actionable troubleshooting guide directly in the console.

Proposed Changes

• Modified sdks/python/apache_beam/transforms/ptransform.py to intercept serialization errors during the initialization of transforms (e.g., beam.Map, beam.FlatMap).

• Added a specialized test case in sdks/python/apache_beam/transforms/ptransform_test.py to ensure the improved message format is raised and contains the correct troubleshooting steps.

Comparison of Error Messages

Before (Generic Traceback)

The previous error provided a low-level traceback that left developers guessing why their code failed to initialize.

RuntimeError: Unable to pickle fn <function <lambda> at 0x7f...>: 
can't pickle _io.TextIOWrapper objects

After (With Troubleshooting Guide)

The new error message identifies the SDK context and provides actionable steps to resolve the issue without requiring the user to search the documentation.

[Apache Beam SDK] Serialization Failure: The function '<function <lambda> at 0x7f...>' 
could not be serialized.
----------------------------------------------------------------------
Apache Beam ships your code to remote workers. This requires your 
functions and their captured variables to be 'picklable'.
Common Solutions:
 1. Use a named function defined at the module level instead of a lambda.
 2. Ensure all variables captured in the closure are serializable.
 3. If you're using a complex object (like a DB client or ML model),
    initialize it inside a DoFn.setup() method rather than the constructor.

Reference: https://beam.apache.org/documentation/programming-guide/#serialization
----------------------------------------------------------------------

Testing Accomplished

• Unit Test Added: Added test_ptransform_serialization_error_message to ptransform_test.py.

• Verification: Confirmed that the test correctly identifies the custom error string when a non-serializable object (like a file handle) is captured in a lambda.

• Regression Check: Ran the full suite for ptransform_test.py to ensure no impact on valid transform initializations.

Impact

• Developer Experience: Significantly reduces debugging time for common "gotchas" in the Python SDK.
• Onboarding: Helps new Beam users understand the requirement for picklable functions early in their development cycle.
• Performance: No impact on pipeline execution speed; this check only occurs during the pipeline construction/graph-building phase.

closes #37209

[gemini-code-assist]

Summary of Changes

Hello @Kalpana-chavhan, 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 improves the developer experience in Apache Beam by providing more informative and actionable error messages for PTransform serialization failures. Instead of cryptic pickling errors, users will now receive a RuntimeError that clearly identifies the problematic function and offers practical solutions, thereby streamlining the debugging process and aiding new users in understanding Beam's serialization requirements.

Highlights

• Improved Error Messaging: Enhanced the error messages for PTransform serialization failures, replacing generic PicklingError or AttributeError with a more descriptive RuntimeError.
• Actionable Troubleshooting: The new error message now includes the specific failing function and a clear, actionable troubleshooting guide directly in the console, providing common solutions and a reference link.
• New Test Case: A dedicated test has been added to ptransform_test.py to verify that the improved error message is correctly raised and contains the expected troubleshooting steps when serialization fails.

🧠 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