It did. It got failed and respawned 4 times. In this case, the too many open files is a sign that you need increase the system-wide limit of open files. Try adding ulimit -n 16000 to your conf/spark-env.sh.
TD On Thu, Aug 28, 2014 at 5:29 PM, Tim Smith <secs...@gmail.com> wrote: > Appeared after running for a while. I re-ran the job and this time, it > crashed with: > 14/08/29 00:18:50 WARN ReceiverTracker: Error reported by receiver for > stream 0: Error in block pushing thread - java.net.SocketException: Too > many open files > > Shouldn't the failed receiver get re-spawned on a different worker? > > > > On Thu, Aug 28, 2014 at 4:12 PM, Tathagata Das < > tathagata.das1...@gmail.com> wrote: > >> Do you see this error right in the beginning or after running for >> sometime? >> >> The root cause seems to be that somehow your Spark executors got killed, >> which killed receivers and caused further errors. Please try to take a look >> at the executor logs of the lost executor to find what is the root cause >> that caused the executor to fail. >> >> TD >> >> >> On Thu, Aug 28, 2014 at 3:54 PM, Tim Smith <secs...@gmail.com> wrote: >> >>> Hi, >>> >>> Have a Spark-1.0.0 (CDH5) streaming job reading from kafka that died >>> with: >>> >>> 14/08/28 22:28:15 INFO DAGScheduler: Failed to run runJob at >>> ReceiverTracker.scala:275 >>> Exception in thread "Thread-59" 14/08/28 22:28:15 INFO >>> YarnClientClusterScheduler: Cancelling stage 2 >>> 14/08/28 22:28:15 INFO DAGScheduler: Executor lost: 5 (epoch 4) >>> 14/08/28 22:28:15 INFO BlockManagerMasterActor: Trying to remove >>> executor 5 from BlockManagerMaster. >>> 14/08/28 22:28:15 INFO BlockManagerMaster: Removed 5 successfully in >>> removeExecutor >>> org.apache.spark.SparkException: Job aborted due to stage failure: Task >>> 2.0:0 failed 4 times, most recent failure: TID 6481 on host >>> node-dn1-1.ops.sfdc.net failed for unknown reason >>> Driver stacktrace: >>> at org.apache.spark.scheduler.DAGScheduler.org >>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1033) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1017) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1015) >>> at >>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>> at >>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>> at >>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1015) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:633) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:633) >>> at scala.Option.foreach(Option.scala:236) >>> at >>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:633) >>> at >>> org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1207) >>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) >>> at akka.actor.ActorCell.invoke(ActorCell.scala:456) >>> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) >>> at akka.dispatch.Mailbox.run(Mailbox.scala:219) >>> at >>> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) >>> 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) >>> >>> >>> Any insights into this error? >>> >>> Thanks, >>> >>> Tim >>> >>> >> >
