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https://issues.apache.org/jira/browse/SPARK-23365?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16363130#comment-16363130
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Apache Spark commented on SPARK-23365:
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User 'squito' has created a pull request for this issue:
https://github.com/apache/spark/pull/20604
> DynamicAllocation with failure in straggler task can lead to a hung spark job
> -----------------------------------------------------------------------------
>
> Key: SPARK-23365
> URL: https://issues.apache.org/jira/browse/SPARK-23365
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 2.1.2, 2.2.1, 2.3.0
> Reporter: Imran Rashid
> Priority: Major
>
> Dynamic Allocation can lead to a spark app getting stuck with 0 executors
> requested when the executors in the last tasks of a taskset fail (eg. with an
> OOM).
> This happens when {{ExecutorAllocationManager}} s internal target number of
> executors gets out of sync with {{CoarseGrainedSchedulerBackend}} s target
> number. {{EAM}} updates the {{CGSB}} in two ways: (1) it tracks how many
> tasks are active or pending in submitted stages, and computes how many
> executors would be needed for them. And as tasks finish, it will actively
> decrease that count, informing the {{CGSB}} along the way. (2) When it
> decides executors are inactive for long enough, then it requests that
> {{CGSB}} kill the executors -- this also tells the {{CGSB}} to update its
> target number of executors:
> https://github.com/apache/spark/blob/4df84c3f818aa536515729b442601e08c253ed35/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala#L622
> So when there is just one task left, you could have the following sequence of
> events:
> (1) the {{EAM}} sets the desired number of executors to 1, and updates the
> {{CGSB}} too
> (2) while that final task is still running, the other executors cross the
> idle timeout, and the {{EAM}} requests the {{CGSB}} kill them
> (3) now the {{EAM}} has a target of 1 executor, and the {{CGSB}} has a target
> of 0 executors
> If the final task completed normally now, everything would be OK; the next
> taskset would get submitted, the {{EAM}} would increase the target number of
> executors and it would update the {{CGSB}}.
> But if the executor for that final task failed (eg. an OOM), then the {{EAM}}
> thinks it [doesn't need to update
> anything|https://github.com/apache/spark/blob/4df84c3f818aa536515729b442601e08c253ed35/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala#L384-L386],
> because its target is already 1, which is all it needs for that final task;
> and the {{CGSB}} doesn't update anything either since its target is 0.
> I think you can determine if this is the cause of a stuck app by looking for
> {noformat}
> yarn.YarnAllocator: Driver requested a total number of 0 executor(s).
> {noformat}
> in the logs of the ApplicationMaster (at least on yarn).
> You can reproduce this with this test app, run with {{--conf
> "spark.dynamicAllocation.minExecutors=1" --conf
> "spark.dynamicAllocation.maxExecutors=5" --conf
> "spark.dynamicAllocation.executorIdleTimeout=5s"}}
> {code}
> import org.apache.spark.SparkEnv
> sc.setLogLevel("INFO")
> sc.parallelize(1 to 10000, 10000).count()
> val execs = sc.parallelize(1 to 1000, 1000).map { _ =>
> SparkEnv.get.executorId}.collect().toSet
> val badExec = execs.head
> println("will kill exec " + badExec)
> new Thread() {
> override def run(): Unit = {
> Thread.sleep(10000)
> println("about to kill exec " + badExec)
> sc.killExecutor(badExec)
> }
> }.start()
> sc.parallelize(1 to 5, 5).mapPartitions { itr =>
> val exec = SparkEnv.get.executorId
> if (exec == badExec) {
> Thread.sleep(20000) // long enough that all the other tasks finish, and
> the executors cross the idle timeout
> // meanwhile, something else should kill this executor
> itr
> } else {
> itr
> }
> }.collect()
> {code}
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