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From Akhil Das <ak...@sigmoidanalytics.com>
Subject Re: Some spark apps fail with "All masters are unresponsive", while others pass normally
Date Mon, 09 Nov 2015 14:59:23 GMT
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 <romi@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
>

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