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https://issues.apache.org/jira/browse/SPARK-19326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15850548#comment-15850548
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Kay Ousterhout commented on SPARK-19326:
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What is the bad behavior that occurs with your example code? 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?
> 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 `spark.speculation=true`
> {code}
> val someRDD = sc.parallelize(1 to 8, 8)
> someRDD.mapPartitionsWithIndex( (index: Int, it: Iterator[Int]) => {
> if (index == 7) {
> Thread.sleep(Long.MaxValue) // fake long running task(s)
> }
> it.toList.map(x => index + ", " + x).iterator
> }).collect
> {code}
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