[AURON #2236]support compile with Celeborn 0.6 and spark 4.0#2243
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ftong2020 wants to merge 3 commits intoapache:masterfrom
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[AURON #2236]support compile with Celeborn 0.6 and spark 4.0#2243ftong2020 wants to merge 3 commits intoapache:masterfrom
ftong2020 wants to merge 3 commits intoapache:masterfrom
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Pull request overview
Adds Spark 4.0 compilation support when building Auron with Celeborn 0.6 by updating Spark-version-gated APIs and aligning Spark 4’s shuffle-write execution behavior with existing Spark 3.x assumptions.
Changes:
- Extend
@sparkvergating forgetPartitionLengths()to include Spark 4.0 in Celeborn 0.6 and RSS shuffle writer implementations. - Update
NativeRDD.compute()to also defer execution forRSS_SHUFFLE_WRITERplans on Spark 4+ (to avoid early execution under the refined ShuffleWriteProcessor flow).
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| thirdparty/auron-celeborn-0.6/src/main/scala/org/apache/spark/sql/execution/auron/shuffle/celeborn/AuronCelebornShuffleWriter.scala | Adds Spark 4.0 to the version-gated getPartitionLengths() override for Celeborn 0.6 integration. |
| spark-extension/src/main/scala/org/apache/spark/sql/execution/auron/shuffle/AuronRssShuffleWriterBase.scala | Adds Spark 4.0 to the version-gated getPartitionLengths() override for RSS shuffle writer base. |
| spark-extension/src/main/scala/org/apache/spark/sql/auron/NativeRDD.scala | Extends the Spark 4+ shuffle-write deferral logic to cover RSS shuffle writer plans as well. |
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@ftong2020 Thanks for the contribution! Please update the PR title to include the issue ID: |
ftong2020
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yew1eb
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May 8, 2026
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Which issue does this PR close?
Closes #2236
Rationale for this change
Spark 4.0 has been release for over 1 year, and celeborn 0.6 provides official spark 4.0 support.
This change will add spark 4.0 support when auron is compiled with spark 4.0 and celeborn 0.6.
Spark 4.1 supported is not included in this PR since latest celeborn version does not support this configuration. Spark 4.1 also changed signature of Mapstatus.apply(), which demands heavy changes to codebase.
What changes are included in this PR?
Are there any user-facing changes?
no
How was this patch tested?
Tested in our staging env