Did you find anything regarding the OOM in the executor logs? Thanks Best Regards
On Mon, Nov 9, 2015 at 8:44 PM, Romi Kuntsman <r...@totango.com> wrote: > 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 >>> >> >> >
