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https://issues.apache.org/jira/browse/SPARK-23423?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16365468#comment-16365468
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Stavros Kontopoulos commented on SPARK-23423:
---------------------------------------------
The task updates delivery is a responsibility of mesos master AFIK. Btw you
should see the status of tasks on the agent log (assuming you are not loosing
agents right?) where the mesos executor was running on. [~susanxhuynh] is it
possible that under heavy load the task updates are not delivered?
> Application declines any offers when killed+active executors rich
> spark.dynamicAllocation.maxExecutors
> ------------------------------------------------------------------------------------------------------
>
> Key: SPARK-23423
> URL: https://issues.apache.org/jira/browse/SPARK-23423
> Project: Spark
> Issue Type: Bug
> Components: Mesos, Spark Core
> Affects Versions: 2.2.1
> Reporter: Igor Berman
> Priority: Major
>
> Hi
> Mesos Version:1.1.0
> I've noticed rather strange behavior of MesosCoarseGrainedSchedulerBackend
> when running on Mesos with dynamic allocation on and limiting number of max
> executors by spark.dynamicAllocation.maxExecutors.
> Suppose we have long running driver that has cyclic pattern of resource
> consumption(with some idle times in between), due to dyn.allocation it
> receives offers and then releases them after current chunk of work processed.
> Since at
> [https://github.com/apache/spark/blob/master/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosCoarseGrainedSchedulerBackend.scala#L573]
> the backend compares numExecutors < executorLimit and
> numExecutors is defined as slaves.values.map(_.taskIDs.size).sum and slaves
> holds all slaves ever "met", i.e. both active and killed (see comment
> [https://github.com/apache/spark/blob/master/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosCoarseGrainedSchedulerBackend.scala#L122)]
>
> On the other hand, number of taskIds should be updated due to statusUpdate,
> but suppose this update is lost(actually I don't see logs of 'is now
> TASK_KILLED') so this number of executors might be wrong
>
> I've created test that "reproduces" this behavior, not sure how good it is:
> {code:java}
> //MesosCoarseGrainedSchedulerBackendSuite
> test("max executors registered stops to accept offers when dynamic allocation
> enabled") {
> setBackend(Map(
> "spark.dynamicAllocation.maxExecutors" -> "1",
> "spark.dynamicAllocation.enabled" -> "true",
> "spark.dynamicAllocation.testing" -> "true"))
> backend.doRequestTotalExecutors(1)
> val (mem, cpu) = (backend.executorMemory(sc), 4)
> val offer1 = createOffer("o1", "s1", mem, cpu)
> backend.resourceOffers(driver, List(offer1).asJava)
> verifyTaskLaunched(driver, "o1")
> backend.doKillExecutors(List("0"))
> verify(driver, times(1)).killTask(createTaskId("0"))
> val offer2 = createOffer("o2", "s2", mem, cpu)
> backend.resourceOffers(driver, List(offer2).asJava)
> verify(driver, times(1)).declineOffer(offer2.getId)
> }{code}
>
>
> Workaround: Don't set maxExecutors with dynamicAllocation on
>
> Please advice
> Igor
> marking you friends since you were last to touch this piece of code and
> probably can advice something([~vanzin], [~skonto], [~susanxhuynh])
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