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From "Kay Ousterhout (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19326) Speculated task attempts do not get launched in few scenarios
Date Fri, 03 Feb 2017 20:34:51 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15852085#comment-15852085
] 

Kay Ousterhout commented on SPARK-19326:
----------------------------------------

I see that makes sense; thanks for the additional explanation.  [~andrewor14] did you think
about this issue when implementing dynamic allocation originally? I noticed there'a a [comment
saying that speculation is not considered for simplicity](https://github.com/apache/spark/blob/6ee28423ad1b2e6089b82af64a31d77d3552bb38/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala#L579),
but it does seem like this functionality can prevent speculation from occurring.

> 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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