Github user mccheah commented on the pull request:
https://github.com/apache/spark/pull/8887#issuecomment-143843978
The concept looks sound to me, however I'm actually less familiar with the
logic of where tasks get allocated. Someone else should also review!
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Github user mccheah commented on a diff in the pull request:
https://github.com/apache/spark/pull/8887#discussion_r40588094
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala ---
@@ -362,8 +395,8 @@ private[spark] class TaskSchedulerImpl(
}
Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/8887#issuecomment-143832319
can you add `[YARN]` to the title?
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Github user vanzin commented on the pull request:
https://github.com/apache/spark/pull/8887#issuecomment-143751726
/cc @squito also
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Github user SparkQA commented on the pull request:
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Github user vanzin commented on the pull request:
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hmm. retest this please.
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Github user vanzin commented on the pull request:
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cc'ing a few people: @mccheah (who wrote the original preemption code),
@JoshRosen @andrewor14 (not sure who between you guys is reviewing this area
these days).
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GitHub user vanzin opened a pull request:
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[SPARK-10622] [core] Differentiate dead from "mostly dead" executors.
In YARN mode, when preemption is enabled, we may leave executors in a
zombie state while we wait to retrieve the reason for wh
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