[#37994] Fix NullPointerException in Spark Runner with multiple outputs and serialization

[stankiewicz]

When using Spark StorageLevels that require serialization (e.g., MEMORY_AND_DISK_SER). the Spark Runner would fail with a NullPointerException if a ParDo had unconsumed outputs. This occurred because unconsumed outputs are pruned from the coder map, leaving them without a coder during the RDD encoding phase.

This fix:

• Updates TranslationUtils.getTupleTagEncodeFunction to return null for tags without a coder.
• Adds non-null filtering before RDD persistence in TransformTranslator, StatefulStreamingParDoEvaluator, and StreamingTransformTranslator to gracefully skip these unconsumed outputs.
• Added a regression test in TransformTranslatorTest to verify the fix with multiple outputs and serialization enabled.
  Fixes [Bug]: Apache Beam Managed Iceberg IO Does not work with Spark Serialization #37994

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Summary of Changes

Hello, 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 NullPointerException in the Spark Runner that occurs when using storage levels requiring serialization, specifically when a ParDo operation has unconsumed outputs. The fix ensures that these unconsumed outputs are correctly identified and skipped during the encoding phase, preventing the runner from attempting to access missing coders.

Highlights

• NullPointerException Fix: Updated TranslationUtils to return null for tags without a coder, preventing crashes when unconsumed outputs are encountered.
• RDD Persistence Optimization: Added non-null filtering in TransformTranslator, StatefulStreamingParDoEvaluator, and StreamingTransformTranslator to gracefully skip unconsumed outputs during RDD persistence.
• Regression Testing: Added a new test case in TransformTranslatorTest to verify correct behavior with multiple outputs and serialization enabled.

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[stankiewicz]

/gemini review

[gemini-code-assist]

Code Review

This pull request addresses potential NullPointerExceptions in the Spark runner by correctly handling unconsumed side outputs during serialization. It modifies the encoding logic to return null when a coder is missing for an unconsumed output and introduces null filters across several translation classes to skip these entries. A new test case validates this behavior under forced serialization. Review feedback suggests refining a code comment for technical accuracy regarding unconsumed outputs and ensuring consistent single-line formatting for comments.

[github-actions]

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[github-actions]

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R: @jrmccluskey added as fallback since no labels match configuration

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[stankiewicz]

R: @twosom maybe

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[twosom]

Hi, @stankiewicz
thank you for your contribution! The changes look great to me.

However, I don't have the permissions to formally review or approve this PR. I'll tag someone who can help you further.

R: @Abacn

P.S. It might be better to commit by modifying the files under .github/trigger_files. You can refer to my PR for an example of how to do this.

[twosom]

Hi, @stankiewicz
thank you for your contribution! The changes look great to me.

However, I don't have the permissions to formally review or approve this PR. I'll tag someone who can help you further.

R: @Abacn

P.S. It might be better to commit by modifying the files under .github/trigger_files. You can refer to my PR for an example of how to do this.

[twosom]

Run Spark ValidatesRunner

[Abacn]

The trigger file has renamed to '.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark.json'