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https://issues.apache.org/jira/browse/SPARK-19326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15851006#comment-15851006
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Tejas Patil commented on SPARK-19326:
-------------------------------------
[~kayousterhout] : I have updated the configs in the repro example in jira
description.
>> Is the problem that only one executor is requested, so no speculation can
>> occur because there's not a different node to run tasks on
No. There are multiple executors launched in the start but later as tasks
finish they are released. In the end there might be one single executor left
running a single long running task. There is no speculation happening in such
case.
> Speculated task attempts do not get launched in few scenarios
> -------------------------------------------------------------
>
> Key: SPARK-19326
> URL: https://issues.apache.org/jira/browse/SPARK-19326
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 2.0.2, 2.1.0
> Reporter: Tejas Patil
>
> Speculated copies of tasks do not get launched in some cases.
> Examples:
> - All the running executors have no CPU slots left to accommodate a
> speculated copy of the task(s). If the all running executors reside over a
> set of slow / bad hosts, they will keep the job running for long time
> - `spark.task.cpus` > 1 and the running executor has not filled up all its
> CPU slots. Since the [speculated copies of tasks should run on different
> host|https://github.com/apache/spark/blob/2e139eed3194c7b8814ff6cf007d4e8a874c1e4d/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala#L283]
> and not the host where the first copy was launched.
> In both these cases, `ExecutorAllocationManager` does not know about pending
> speculation task attempts and thinks that all the resource demands are well
> taken care of. ([relevant
> code|https://github.com/apache/spark/blob/6ee28423ad1b2e6089b82af64a31d77d3552bb38/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala#L265])
> This adds variation in the job completion times and more importantly SLA
> misses :( In prod, with a large number of jobs, I see this happening more
> often than one would think. Chasing the bad hosts or reason for slowness
> doesn't scale.
> Here is a tiny repro. Note that you need to launch this with (Mesos or YARN
> or standalone deploy mode) along with `--conf spark.speculation=true --conf
> spark.executor.cores=4 --conf spark.dynamicAllocation.maxExecutors=100`
> {code}
> val n = 100
> val someRDD = sc.parallelize(1 to n, n)
> someRDD.mapPartitionsWithIndex( (index: Int, it: Iterator[Int]) => {
> if (index == 1) {
> Thread.sleep(Long.MaxValue) // fake long running task(s)
> }
> it.toList.map(x => index + ", " + x).iterator
> }).collect
> {code}
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