Looks like a Spark error as far as I can tell. This error is very generic and indicates that the job was not accepted for execution so Spark may be configured wrong. This looks like a question for the Spark people
My Spark sanity check: 1) In the Spark UI at http:///Hadoop.Master:8080 does everything look correct? 2) Have you tested your spark *cluster* with one of their examples? Have you run *any non-Mahout* code on the cluster to check that it is configured properly? 3) Are you using exactly the same Spark and Hadoop locally as on the cluster? 4) Did you launch both local and cluster jobs from the same cluster machine? The only difference being the master URL (local[2] vs. spark://Hadoop.Master:7077)? 14/09/22 04:12:47 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory 14/09/22 04:12:49 INFO client.AppClient$ClientActor: Connecting to master spark://Hadoop.Master:7077... On Sep 24, 2014, at 8:18 PM, pol <[email protected]> wrote: Hi, Pat Dataset is the same, and the data is very few for test. This is a bug? On Sep 25, 2014, at 02:57, Pat Ferrel <[email protected]> wrote: > Are you using different data sets on the local and cluster? > > Try increasing spark memory with -sem, I use -sem 6g for the epinions data > set. > > The ID dictionaries are kept in-memory on each cluster machine so a large > number of user or item IDs will need more memory. > > > On Sep 24, 2014, at 9:31 AM, pol <[email protected]> wrote: > > Hi, All > > I’m sure it’s ok that launching Spark standalone to a cluster, but it > can’t work used for spark-itemsimilarity. > > Launching on 'local' it’s ok: > mahout spark-itemsimilarity -i /user/root/test/input/data.txt -o > /user/root/test/output -os -ma local[2] -f1 purchase -f2 view -ic 2 -fc 1 > -sem 1g > > but launching on a standalone cluster will be an error: > mahout spark-itemsimilarity -i /user/root/test/input/data.txt -o > /user/root/test/output -os -ma spark://Hadoop.Master:7077 -f1 purchase -f2 > view -ic 2 -fc 1 -sem 1g > ------------ > 14/09/22 04:12:47 WARN scheduler.TaskSchedulerImpl: Initial job has not > accepted any resources; check your cluster UI to ensure that workers are > registered and have sufficient memory > 14/09/22 04:12:49 INFO client.AppClient$ClientActor: Connecting to master > spark://Hadoop.Master:7077... > 14/09/22 04:13:02 WARN scheduler.TaskSchedulerImpl: Initial job has not > accepted any resources; check your cluster UI to ensure that workers are > registered and have sufficient memory > 14/09/22 04:13:09 INFO client.AppClient$ClientActor: Connecting to master > spark://Hadoop.Master:7077... > 14/09/22 04:13:17 WARN scheduler.TaskSchedulerImpl: Initial job has not > accepted any resources; check your cluster UI to ensure that workers are > registered and have sufficient memory > 14/09/22 04:13:29 ERROR cluster.SparkDeploySchedulerBackend: Application has > been killed. Reason: All masters are unresponsive! Giving up. > 14/09/22 04:13:29 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 1.0, > whose tasks have all completed, from pool > 14/09/22 04:13:29 INFO scheduler.TaskSchedulerImpl: Cancelling stage 1 > 14/09/22 04:13:29 INFO scheduler.DAGScheduler: Failed to run collect at > TextDelimitedReaderWriter.scala:74 > Exception in thread "main" org.apache.spark.SparkException: Job aborted due > to stage failure: All masters are unresponsive! Giving up. > at > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1044) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1028) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1026) > at > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1026) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:634) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:634) > at scala.Option.foreach(Option.scala:236) > at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:634) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1229) > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) > at akka.actor.ActorCell.invoke(ActorCell.scala:456) > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) > at akka.dispatch.Mailbox.run(Mailbox.scala:219) > at > akka.dispatch.ForkJoinExecutorConfigurator$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) > ------------ > > Thanks. > >
