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From "Michael Armbrust (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-2632) Importing a method of class in Spark REPL causes the REPL to pulls in unnecessary stuff.
Date Fri, 01 Aug 2014 05:58:39 GMT

     [ https://issues.apache.org/jira/browse/SPARK-2632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Michael Armbrust resolved SPARK-2632.
-------------------------------------

       Resolution: Fixed
    Fix Version/s: 1.1.0
         Assignee: Prashant Sharma

> Importing a method of class in Spark REPL causes the REPL to pulls in unnecessary stuff.
> ----------------------------------------------------------------------------------------
>
>                 Key: SPARK-2632
>                 URL: https://issues.apache.org/jira/browse/SPARK-2632
>             Project: Spark
>          Issue Type: Bug
>    Affects Versions: 1.0.0, 1.0.1
>            Reporter: Yin Huai
>            Assignee: Prashant Sharma
>             Fix For: 1.1.0
>
>
>  Master is affected by this bug. To reproduce the exception, you can start a local cluster
(sbin/start-all.sh) then open a spark shell.
> {code}
> class X() { println("What!"); def y = 3 }
> val x = new X
> import x.y
> case class Person(name: String, age: Int)
> sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0),
p(1).trim.toInt)).collect
> {code}
> Then you will find the exception. I am attaching the stack trace below...
> {code}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage
0.0 (TID 0) had a not serializable result: $iwC$$iwC$$iwC$$iwC$X
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1045)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1029)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1027)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> 	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1027)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:632)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:632)
> 	at scala.Option.foreach(Option.scala:236)
> 	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:632)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1230)
> 	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> 	at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> 	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> 	at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> 	at akka.dispatch.ForkJoinExecutorConfigurator$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)
> {code}



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