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From "Marcelo Vanzin (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-23365) DynamicAllocation with failure in straggler task can lead to a hung spark job
Date Tue, 27 Feb 2018 19:14:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-23365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Marcelo Vanzin resolved SPARK-23365.
------------------------------------
       Resolution: Fixed
    Fix Version/s: 2.3.1
                   2.4.0

Issue resolved by pull request 20604
[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
>            Assignee: Imran Rashid
>            Priority: Major
>             Fix For: 2.4.0, 2.3.1
>
>
> 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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