If they have a problem managing memory, wouldn't there should be a OOM? Why does AppClient throw a NPE?
*Romi Kuntsman*, *Big Data Engineer* http://www.totango.com On Mon, Nov 9, 2015 at 4:59 PM, Akhil Das <ak...@sigmoidanalytics.com> wrote: > Is that all you have in the executor logs? I suspect some of those jobs > are having a hard time managing the memory. > > Thanks > Best Regards > > On Sun, Nov 1, 2015 at 9:38 PM, Romi Kuntsman <r...@totango.com> wrote: > >> [adding dev list since it's probably a bug, but i'm not sure how to >> reproduce so I can open a bug about it] >> >> Hi, >> >> I have a standalone Spark 1.4.0 cluster with 100s of applications running >> every day. >> >> From time to time, the applications crash with the following error (see >> below) >> But at the same time (and also after that), other applications are >> running, so I can safely assume the master and workers are working. >> >> 1. why is there a NullPointerException? (i can't track the scala stack >> trace to the code, but anyway NPE is usually a obvious bug even if there's >> actually a network error...) >> 2. why can't it connect to the master? (if it's a network timeout, how to >> increase it? i see the values are hardcoded inside AppClient) >> 3. how to recover from this error? >> >> >> ERROR 01-11 15:32:54,991 SparkDeploySchedulerBackend - Application >> has been killed. Reason: All masters are unresponsive! Giving up. ERROR >> ERROR 01-11 15:32:55,087 OneForOneStrategy - ERROR >> logs/error.log >> java.lang.NullPointerException NullPointerException >> at >> org.apache.spark.deploy.client.AppClient$ClientActor$$anonfun$receiveWithLogging$1.applyOrElse(AppClient.scala:160) >> at >> scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33) >> at >> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33) >> at >> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25) >> at >> org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:59) >> at >> org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42) >> at >> scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118) >> at >> org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42) >> at akka.actor.Actor$class.aroundReceive(Actor.scala:465) >> at >> org.apache.spark.deploy.client.AppClient$ClientActor.aroundReceive(AppClient.scala:61) >> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) >> at akka.actor.ActorCell.invoke(ActorCell.scala:487) >> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) >> at akka.dispatch.Mailbox.run(Mailbox.scala:220) >> at >> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393) >> at >> scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) >> at >> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) >> at >> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) >> at >> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) >> ERROR 01-11 15:32:55,603 SparkContext - Error >> initializing SparkContext. ERROR >> java.lang.IllegalStateException: Cannot call methods on a stopped >> SparkContext >> at org.apache.spark.SparkContext.org >> $apache$spark$SparkContext$$assertNotStopped(SparkContext.scala:103) >> at >> org.apache.spark.SparkContext.getSchedulingMode(SparkContext.scala:1501) >> at >> org.apache.spark.SparkContext.postEnvironmentUpdate(SparkContext.scala:2005) >> at org.apache.spark.SparkContext.<init>(SparkContext.scala:543) >> at >> org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:61) >> >> >> Thanks! >> >> *Romi Kuntsman*, *Big Data Engineer* >> http://www.totango.com >> > >
