Igor Berman commented on SPARK-23423:

[~skonto] so I've run application today with relevant logs at debug level. 
Seems like with dynamic allocation on with executors starting and shutting down 
the chances that every slave will get 2 failures staring some executor are much 
higher that in regular case(without dynamic allocation)  seems like SPARK-19755 
is the core issue here - after half day of long running driver in client mode 
almost 1/3 of slaves out of all mesos slaves could be marked as blacklisted.

the reasons for executor failures might be different and transient(e.g. port 

I think I'll close this Jira as duplicate for SPARK-19755, WDYT?

> 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
>              Labels: Mesos, dynamic_allocation
>         Attachments: no-dyn-allocation-failed-no-statusUpdate.txt
> 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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