[SPARK-48292][CORE][3.4] Revert [SPARK-39195][SQL] Spark OutputCommitCoordinator should abort stage when committed file not consistent with task status #47168
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…inator should abort stage when committed file not consistent with task status Revert apache#36564 According to discuss apache#36564 (comment) When spark commit task will commit to committedTaskPath `${outputpath}/_temporary//${appAttempId}/${taskId}` So in apache#36564 's case, since before apache#38980, each task's job id's date is not the same, when the task writes data success but fails to send back TaskSuccess RPC, the task rerun will commit to a different committedTaskPath then causing data duplicated. After apache#38980, for the same task's different attempts, the TaskId is the same now, when re-run task commit, will commit to the same committedTaskPath, and hadoop CommitProtocol will handle such case then data won't be duplicated. Note: The taskAttemptPath is not same since in the path contains the taskAttemptId. No need anymore No Existed UT No Closes apache#46696 from AngersZhuuuu/SPARK-48292. Authored-by: Angerszhuuuu <angers.zhu@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
dongjoon-hyun
changed the title
|
cc @AngersZhuuuu , @viirya , @cloud-fan , @huaxingao |
huaxingao
approved these changes
Jul 1, 2024
viirya
approved these changes
Jul 1, 2024
|
Thank you, @huaxingao and @viirya . |
dongjoon-hyun
pushed a commit
that referenced
this pull request
Jul 1, 2024
…Coordinator should abort stage when committed file not consistent with task status This is a backport of #46696 ### What changes were proposed in this pull request? Revert #36564 According to discuss #36564 (comment) When spark commit task will commit to committedTaskPath `${outputpath}/_temporary//${appAttempId}/${taskId}` So in #36564 's case, since before #38980, each task's job id's date is not the same, when the task writes data success but fails to send back TaskSuccess RPC, the task rerun will commit to a different committedTaskPath then causing data duplicated. After #38980, for the same task's different attempts, the TaskId is the same now, when re-run task commit, will commit to the same committedTaskPath, and hadoop CommitProtocol will handle such case then data won't be duplicated. Note: The taskAttemptPath is not same since in the path contains the taskAttemptId. ### Why are the changes needed? No need anymore ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existed UT ### Was this patch authored or co-authored using generative AI tooling? No Closes #47168 from dongjoon-hyun/SPARK-48292-3.4. Authored-by: Angerszhuuuu <angers.zhu@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
szehon-ho
pushed a commit
to szehon-ho/spark
that referenced
this pull request
Aug 7, 2024
…Coordinator should abort stage when committed file not consistent with task status This is a backport of apache#46696 ### What changes were proposed in this pull request? Revert apache#36564 According to discuss apache#36564 (comment) When spark commit task will commit to committedTaskPath `${outputpath}/_temporary//${appAttempId}/${taskId}` So in apache#36564 's case, since before apache#38980, each task's job id's date is not the same, when the task writes data success but fails to send back TaskSuccess RPC, the task rerun will commit to a different committedTaskPath then causing data duplicated. After apache#38980, for the same task's different attempts, the TaskId is the same now, when re-run task commit, will commit to the same committedTaskPath, and hadoop CommitProtocol will handle such case then data won't be duplicated. Note: The taskAttemptPath is not same since in the path contains the taskAttemptId. ### Why are the changes needed? No need anymore ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existed UT ### Was this patch authored or co-authored using generative AI tooling? No Closes apache#47168 from dongjoon-hyun/SPARK-48292-3.4. Authored-by: Angerszhuuuu <angers.zhu@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This is a backport of #46696
What changes were proposed in this pull request?
Revert #36564 According to discuss #36564 (comment)
When spark commit task will commit to committedTaskPath
${outputpath}/_temporary//${appAttempId}/${taskId}So in #36564 's case, since before #38980, each task's job id's date is not the same, when the task writes data success but fails to send back TaskSuccess RPC, the task rerun will commit to a different committedTaskPath then causing data duplicated.
After #38980, for the same task's different attempts, the TaskId is the same now, when re-run task commit, will commit to the same committedTaskPath, and hadoop CommitProtocol will handle such case then data won't be duplicated.
Note: The taskAttemptPath is not same since in the path contains the taskAttemptId.
Why are the changes needed?
No need anymore
Does this PR introduce any user-facing change?
No
How was this patch tested?
Existed UT
Was this patch authored or co-authored using generative AI tooling?
No