Tejas Patil created SPARK-19326: ----------------------------------- Summary: 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: 2.1.0, 2.0.2 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 == 8) { Thread.sleep(Long.MaxValue) // fake long running task(s) } it.toList.map(x => index + ", " + x).iterator }).collect {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org