[SPARK-32658][CORE] Fix PartitionWriterStream partition length overflow
#29474
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Test build #127626 has finished for PR 29474 at commit
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retest this please |
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cc @zhengruifeng FYI. This is a blocker |
HyukjinKwon
approved these changes
Aug 19, 2020
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Test build #127628 has finished for PR 29474 at commit
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retest this please |
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Test build #127641 has finished for PR 29474 at commit
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retest this please |
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Test build #127660 has finished for PR 29474 at commit
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thanks, merging to master/3.0! |
cloud-fan
pushed a commit
that referenced
this pull request
Aug 20, 2020
…flow ### What changes were proposed in this pull request? The `count` in `PartitionWriterStream` should be a long value, instead of int. The issue is introduced by apache/sparkabef84a . When the overflow happens, the shuffle index file would record wrong index of a reduceId, thus lead to `FetchFailedException: Stream is corrupted` error. Besides the fix, I also added some debug logs, so in the future it's easier to debug similar issues. ### Why are the changes needed? This is a regression and bug fix. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? A Spark user reported this issue when migrating their workload to 3.0. One of the jobs fail deterministically on Spark 3.0 without the patch, and the job succeed after applied the fix. Closes #29474 from jiangxb1987/fixPartitionWriteStream. Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> (cherry picked from commit f793977) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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cc @vanzin @squito @jerryshao who are the major reviewers of the original PR #25007 |
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Nice catch @jiangxb1987 ! |
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What changes were proposed in this pull request?
The
countinPartitionWriterStreamshould be a long value, instead of int. The issue is introduced by abef84a . When the overflow happens, the shuffle index file would record wrong index of a reduceId, thus lead toFetchFailedException: Stream is corruptederror.Besides the fix, I also added some debug logs, so in the future it's easier to debug similar issues.
Why are the changes needed?
This is a regression and bug fix.
Does this PR introduce any user-facing change?
No
How was this patch tested?
A Spark user reported this issue when migrating their workload to 3.0. One of the jobs fail deterministically on Spark 3.0 without the patch, and the job succeed after applied the fix.