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https://issues.apache.org/jira/browse/SPARK-21656?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16117086#comment-16117086
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Thomas Graves commented on SPARK-21656:
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The executor can be idle if the scheduler doesn't put any tasks on it. The
scheduler can skip executors due to the locality settings
(spark.locality.wait.node). We have seen this many times now where it gets in
this harmonic where some executors get node locality and other don't. The
scheduler skips many of the executors that don't get locality and eventually
they idle timeout when there are 10's of thousands of tasks left.
We generally see this with very large jobs that have like 1000 executors,
150000 map tasks.
We shouldn't allow them to idle timeout if we still need them.
> spark dynamic allocation should not idle timeout executors when tasks still
> to run
> ----------------------------------------------------------------------------------
>
> Key: SPARK-21656
> URL: https://issues.apache.org/jira/browse/SPARK-21656
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.1.1
> Reporter: Jong Yoon Lee
> Fix For: 2.1.1
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> Right now spark lets go of executors when they are idle for the 60s (or
> configurable time). I have seen spark let them go when they are idle but they
> were really needed. I have seen this issue when the scheduler was waiting to
> get node locality but that takes longer then the default idle timeout. In
> these jobs the number of executors goes down really small (less than 10) but
> there are still like 80,000 tasks to run.
> We should consider not allowing executors to idle timeout if they are still
> needed according to the number of tasks to be run.
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