Ogni volta che provo a eseguire un'applicazione Spark su un cluster Cloudera CDH 5.4.4, modalità client filato, ottengo la seguente eccezione (ripetuta molte volte nella traccia dello stack). Il processo continua comunque (è un avvertimento), ma non è possibile trovare qualcosa nei log. Come posso risolverlo?Eccezione in esecuzione /etc/hadoop/conf.cloudera.yarn/topology.py
15/09/01 08:53:58 WARN net.ScriptBasedMapping: Exception running /etc/hadoop/conf.cloudera.yarn/topology.py 10.0.0.5
java.io.IOException: Cannot run program "/etc/hadoop/conf.cloudera.yarn/topology.py" (in directory "/home/azureuser/scripts/streaming"): error=13, Permission denied
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1047)
at org.apache.hadoop.util.Shell.runCommand(Shell.java:485)
at org.apache.hadoop.util.Shell.run(Shell.java:455)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:715)
at org.apache.hadoop.net.ScriptBasedMapping$RawScriptBasedMapping.runResolveCommand(ScriptBasedMapping.java:251)
at org.apache.hadoop.net.ScriptBasedMapping$RawScriptBasedMapping.resolve(ScriptBasedMapping.java:188)
at org.apache.hadoop.net.CachedDNSToSwitchMapping.resolve(CachedDNSToSwitchMapping.java:119)
at org.apache.hadoop.yarn.util.RackResolver.coreResolve(RackResolver.java:101)
at org.apache.hadoop.yarn.util.RackResolver.resolve(RackResolver.java:81)
at org.apache.spark.scheduler.cluster.YarnScheduler.getRackForHost(YarnScheduler.scala:38)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$1.apply(TaskSchedulerImpl.scala:271)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$1.apply(TaskSchedulerImpl.scala:263)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:263)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.makeOffers(CoarseGrainedSchedulerBackend.scala:167)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor$$anonfun$receiveWithLogging$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:131)
at scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33)
at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33)
at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25)
at org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:53)
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$1.applyOrElse(ActorLogReceive.scala:42)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$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)
Caused by: java.io.IOException: error=13, Permission denied
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:186)
at java.lang.ProcessImpl.start(ProcessImpl.java:130)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1028)
... 32 more
vorrei che il messaggio è stato più informativo e non dirigere ad un collegamento chiuso. – Dror
Ho scaricato i file di configurazione del client da Cloudera Manager e i nomi delle cartelle sono diversi filato-conf vs conf.cloudera.yarn. Inoltre, non eseguire autorizzazioni su topology.py. Ho controllato i nodi del cluster e hanno i nomi e le autorizzazioni previsti. –