ERROR TaskSchedulerImpl: Exception in statusUpdate

Question

I ran Python code on Spark using Mllib. It works fine with small datasets, but I get the following error after two iterations for large datasets:

ERROR TaskSchedulerImpl: Exception in statusUpdate java.util.concurrent.RejectedExecutionException: Task org.apache.spark.scheduler.TaskResultGetter$$anon$2@15b59543 rejected from java.util.concurrent.ThreadPoolExecutor@22427929 [Terminated, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 2701] at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2050) at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:821) at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1372) at org.apache.spark.scheduler.TaskResultGetter.enqueueSuccessfulTask(TaskResultGetter.scala:49) at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$liftedTree2$1$1.apply(TaskSchedulerImpl.scala:327) at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$liftedTree2$1$1.apply(TaskSchedulerImpl.scala:324) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.TaskSchedulerImpl.liftedTree2$1(TaskSchedulerImpl.scala:324) at org.apache.spark.scheduler.TaskSchedulerImpl.statusUpdate(TaskSchedulerImpl.scala:309) at org.apache.spark.scheduler.local.LocalEndpoint$$anonfun$receive$1.applyOrElse(LocalBackend.scala:61) at org.apache.spark.rpc.akka.AkkaRpcEnv.org$apache$spark$rpc$akka$AkkaRpcEnv$$processMessage(AkkaRpcEnv.scala:178) at org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1$$anonfun$applyOrElse$4.apply$mcV$sp(AkkaRpcEnv.scala:127) at org.apache.spark.rpc.akka.AkkaRpcEnv.org$apache$spark$rpc$akka$AkkaRpcEnv$$safelyCall(AkkaRpcEnv.scala:198) at org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1.applyOrElse(AkkaRpcEnv.scala:126) at scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33) at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33) at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25) at org.apache.spark.util.ActorLogReceive$$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.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1.aroundReceive(AkkaRpcEnv.scala:93) at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) at akka.actor.ActorCell.invoke(ActorCell.scala:487) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) at akka.dispatch.Mailbox.run(Mailbox.scala:220) at akka.dispatch.ForkJoinExecutorConfigurator$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) 

Do you have any ideas?