Yes, thank you for suggestion. The error I found below was in the worker logs.
AssociationError [akka.tcp://sparkwor...@cloudera01.local.company.com:7078] -> [akka.tcp://sparkexecu...@cloudera01.local.company.com:33329]: Error [Association failed with [akka.tcp://sparkexecu...@cloudera01.local.company.com:33329]] [ akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkexecu...@cloudera01.local.company.com:33329] Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: cloudera01.local.company.com/10.40.19.67:33329 ] I looked into suggestions for this type of error and before I found out the real reason for the error I upgraded my CDH to 5.2 so I could try setting the driver and executor ports rather than have Spark choose them at random. My boss later turned off iptables and I no longer get that error. I do get a different one however. I have gone back into my project and changed my hadoop version to 2.5.0-cdh5.2.0 so that should not be a problem. from the master logs 2014-11-17 18:09:49,707 ERROR akka.remote.EndpointWriter: AssociationError [akka.tcp://sparkmas...@cloudera01.local.local.com:7077] -> [akka.tcp://spark@localhost:38181]: Error [Association failed with [akka.tcp://spark@localhost:38181]] [ akka.remote.EndpointAssociationException: Association failed with [akka.tcp://spark@localhost:38181] Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: localhost/127.0.0.1:38181 ] 2014-11-17 18:19:08,271 INFO akka.actor.LocalActorRef: Message [akka.remote.transport.AssociationHandle$Disassociated] from Actor[akka://sparkMaster/deadLetters] to Actor[akka://sparkMaster/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2FsparkMaster%4010.40.19.67%3A37795-29#-1248895472] was not delivered. [30] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'. 2014-11-17 18:19:28,251 ERROR Remoting: org.apache.spark.deploy.ApplicationDescription; local class incompatible: stream classdesc serialVersionUID = 583745679236071411, local class serialVersionUID = 7674242335164700840 java.io.InvalidClassException: org.apache.spark.deploy.ApplicationDescription; local class incompatible: stream classdesc serialVersionUID = 583745679236071411, local class serialVersionUID = 7674242335164700840 at java.io.ObjectStreamClass.initNonProxy(ObjectStreamClass.java:617) at java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1622) at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1517) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1771) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) at akka.serialization.JavaSerializer$$anonfun$1.apply(Serializer.scala:136) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57) at akka.serialization.JavaSerializer.fromBinary(Serializer.scala:136) at akka.serialization.Serialization$$anonfun$deserialize$1.apply(Serialization.scala:104) at scala.util.Try$.apply(Try.scala:161) at akka.serialization.Serialization.deserialize(Serialization.scala:98) at akka.remote.serialization.MessageContainerSerializer.fromBinary(MessageContainerSerializer.scala:58) at akka.serialization.Serialization$$anonfun$deserialize$1.apply(Serialization.scala:104) at scala.util.Try$.apply(Try.scala:161) at akka.serialization.Serialization.deserialize(Serialization.scala:98) at akka.remote.MessageSerializer$.deserialize(MessageSerializer.scala:23) at akka.remote.DefaultMessageDispatcher.payload$lzycompute$1(Endpoint.scala:55) at akka.remote.DefaultMessageDispatcher.payload$1(Endpoint.scala:55) at akka.remote.DefaultMessageDispatcher.dispatch(Endpoint.scala:73) at akka.remote.EndpointReader$$anonfun$receive$2.applyOrElse(Endpoint.scala:764) at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) at akka.actor.ActorCell.invoke(ActorCell.scala:456) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) at akka.dispatch.Mailbox.run(Mailbox.scala:219) at akka.dispatch.ForkJoinExecutorConfigurator$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) and from regular output 14/11/17 18:09:49 ERROR SparkDeploySchedulerBackend: Application has been killed. Reason: All masters are unresponsive! Giving up. 14/11/17 18:09:49 ERROR TaskSchedulerImpl: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up. Seems pretty apparent the masters are unresponsive but why is the question? It is the inverse of the first problem I was having where the executors were unresponsive. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Using-data-in-RDD-to-specify-HDFS-directory-to-write-to-tp18789p19114.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org