[Draft] Run Python Flink PVR tests on DataStream API

[Abacn]

Please add a meaningful description for your change here

---

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

• Mention the appropriate issue in your description (for example: addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, comment fixes #<ISSUE NUMBER> instead.
• Update CHANGES.md with noteworthy changes.
• If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.

To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI or the workflows README to see a list of phrases to trigger workflows.

[gemini-code-assist]

Summary of Changes

Hello @Abacn, 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 focuses on enhancing the Python Flink runner test suite by enabling the execution of tests against the Flink DataStream API for batch processing. It introduces a new configuration option to control this behavior and sets up a dedicated test task to validate its functionality. Additionally, it refines the Java command-line arguments for the Flink job server to be more platform-aware and cleans up a redundant Gradle setting.

Highlights

• Flink DataStream API Testing: Introduced a new command-line argument --use_data_stream_for_batch to enable Flink DataStream API for batch pipelines in Python Flink runner tests, and added a new test task to specifically run these tests.
• Conditional Java Options: Modified the Flink job server's Java command to conditionally apply the -XX:-UseContainerSupport flag only when running on Linux, optimizing container support.
• Gradle Configuration Cleanup: Removed an unnecessary outputs.upToDateWhen { false } configuration from the SQL expansion service's shadowJar task, streamlining the build process.

🧠 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/java/extensions/sql/expansion-service/build.gradle
  • Removed outputs.upToDateWhen { false } from the shadowJar configuration.
• sdks/python/apache_beam/runners/portability/flink_runner_test.py
  • Added platform import.
  • Introduced --use_data_stream_for_batch argument to control Flink DataStream API usage for batch pipelines.
  • Modified Java subprocess command to conditionally include -XX:-UseContainerSupport only on Linux.
  • Propagated the use_data_stream_for_batch flag to Flink runner options.
• sdks/python/test-suites/portable/common.gradle
  • Modified createFlinkRunnerTestTask to accept a useDataStreamForBatch parameter.
  • Conditionally added --use_data_stream_for_batch option and modified task name based on the new parameter.
  • Added a new Flink compatibility matrix test task for LOOPBACK worker type using the DataStream API.

Ignored Files

• Ignored by pattern: .github/workflows/** (1)
  • .github/workflows/beam_PostCommit_Python_Portable_Flink.yml

Activity

• The pull request is currently in a draft state, indicating ongoing development.
• The description is a placeholder, suggesting that detailed context or specific issues addressed are yet to be provided by the author.

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. ↩