[ https://issues.apache.org/jira/browse/SPARK-6954?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-6954: ----------------------------------- Assignee: Apache Spark > Dynamic allocation: numExecutorsPending in ExecutorAllocationManager should > never become negative > ------------------------------------------------------------------------------------------------- > > Key: SPARK-6954 > URL: https://issues.apache.org/jira/browse/SPARK-6954 > Project: Spark > Issue Type: Bug > Components: YARN > Affects Versions: 1.3.0 > Reporter: Cheolsoo Park > Assignee: Apache Spark > Priority: Minor > Labels: yarn > > I have a simple test case for dynamic allocation on YARN that fails with the > following stack trace- > {code} > 15/04/16 00:52:14 ERROR Utils: Uncaught exception in thread > spark-dynamic-executor-allocation-0 > java.lang.IllegalArgumentException: Attempted to request a negative number of > executor(s) -21 from the cluster manager. Please specify a positive number! > at > org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.requestTotalExecutors(CoarseGrainedSchedulerBackend.scala:338) > at > org.apache.spark.SparkContext.requestTotalExecutors(SparkContext.scala:1137) > at > org.apache.spark.ExecutorAllocationManager.addExecutors(ExecutorAllocationManager.scala:294) > at > org.apache.spark.ExecutorAllocationManager.addOrCancelExecutorRequests(ExecutorAllocationManager.scala:263) > at > org.apache.spark.ExecutorAllocationManager.org$apache$spark$ExecutorAllocationManager$$schedule(ExecutorAllocationManager.scala:230) > at > org.apache.spark.ExecutorAllocationManager$$anon$1$$anonfun$run$1.apply$mcV$sp(ExecutorAllocationManager.scala:189) > at > org.apache.spark.ExecutorAllocationManager$$anon$1$$anonfun$run$1.apply(ExecutorAllocationManager.scala:189) > at > org.apache.spark.ExecutorAllocationManager$$anon$1$$anonfun$run$1.apply(ExecutorAllocationManager.scala:189) > at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1618) > at > org.apache.spark.ExecutorAllocationManager$$anon$1.run(ExecutorAllocationManager.scala:189) > at > java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471) > at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304) > at > java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178) > at > java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > {code} > My test is as follows- > # Start spark-shell with a single executor. > # Run a {{select count(\*)}} query. The number of executors rises as input > size is non-trivial. > # After the job finishes, the number of executors falls as most of them > become idle. > # Rerun the same query again, and it fails with the above error. > In fact, this error only happens when I configure {{executorIdleTimeout}} > very small. For eg, I can reproduce it with the following configs- > {code} > spark.dynamicAllocation.executorIdleTimeout 5 > spark.dynamicAllocation.schedulerBacklogTimeout 5 > {code} > Although I can simply increase {{executorIdleTimeout}} to something like 60 > secs to avoid the error, I think this is still a bug to be fixed. > The root cause seems that {{numExecutorsPending}} accidentally becomes > negative if executors are killed too aggressively (i.e. > {{executorIdleTimeout}} is too small) because under that circumstance, the > new target # of executors can be smaller than the current # of executors. > When that happens, {{ExecutorAllocationManager}} ends up trying to add a > negative number of executors, which throws an exception. -- 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