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https://issues.apache.org/jira/browse/SPARK-26755?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Imran Rashid resolved SPARK-26755.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 23677
[https://github.com/apache/spark/pull/23677]
> Optimize Spark Scheduler to dequeue speculative tasks more efficiently
> ----------------------------------------------------------------------
>
> Key: SPARK-26755
> URL: https://issues.apache.org/jira/browse/SPARK-26755
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler
> Affects Versions: 3.0.0
> Reporter: Parth Gandhi
> Assignee: Parth Gandhi
> Priority: Minor
> Fix For: 3.0.0
>
> Attachments: Screen Shot 2019-01-28 at 11.21.05 AM.png, Screen Shot
> 2019-01-28 at 11.21.25 AM.png, Screen Shot 2019-01-28 at 11.22.42 AM.png
>
>
> Currently, Spark Scheduler takes quite some time to dequeue speculative tasks
> for larger tasksets within a stage(like 100000 or more) when speculation is
> turned on. On further analysis, it was found that the "task-result-getter"
> threads remain blocked on one of the dispatcher-event-loop threads holding
> the lock on TaskSchedulerImpl object
> {code:java}
> def resourceOffers(offers: IndexedSeq[WorkerOffer]):
> Seq[Seq[TaskDescription]] = synchronized {
> {code}
> which takes quite some time to execute the method "dequeueSpeculativeTask"
> in TaskSetManager.scala, thus, slowing down the overall running time of the
> spark job. We were monitoring the time utilization of that lock for the whole
> duration of the job and it was close to 50% i.e. the code within the
> synchronized block would run for almost half the duration of the entire spark
> job. The screenshots of the thread dump have been attached below for
> reference.
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