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Github user SparkQA commented on the issue:
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Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/17543
That JIRA is great. I'll close this PR for now and link my JIRA in there.
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Github user kayousterhout commented on the issue:
https://github.com/apache/spark/pull/17543
In theory (as you may know), the way this is supposed to work is that,
since each reduce task reads the map outputs in random order, we delay
re-scheduling the earlier stage, to try to
Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/17543
Let me try to draw a graph to better explain this.
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Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/17543
Yes, your explanation is on point. If I have 4+ executors that died, then
all retries of Stage B will also eventually fail. If we didn't ignore these
failures, we could have re-computed the outputs
Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/17543
cc @kayousterhout @markhamstra for feedback.
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