[
https://issues.apache.org/jira/browse/SPARK-2408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14055408#comment-14055408
]
Sean Owen commented on SPARK-2408:
----------------------------------
This one confuses me too. It happens without the calls to checkpoint or (first)
call to count. This works fine:
str_rdd.map(a => a).count
I don't see how the Random ends up serialized with the closure of "test", as
it's not used?
Bug, or just something about Scala I also don't get?
> RDD.map(func) dependencies issue after checkpoint & count
> ---------------------------------------------------------
>
> Key: SPARK-2408
> URL: https://issues.apache.org/jira/browse/SPARK-2408
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 0.9.1, 1.0.0
> Reporter: Daniel Fry
>
> i am noticing strange behavior with a simple example use of rdd.checkpoint().
> you can paste the following code into any spark-shell (e.g. with
> MASTER=local[*])
> // build an array of 100 random lowercase strings of length 10
> val r = new scala.util.Random()
> val str_arr = (1 to 100).map(a => (1 to 10).map(b => new
> Character(((Math.abs(r.nextInt) % 26) + 97).toChar)).mkString(""))
> // make this into an rdd
> val str_rdd = sc.parallelize(str_arr)
> // checkpoint & count
> sc.setCheckpointDir("hdfs://[namenode]:54310/path/to/some/spark_checkpoint_dir")
> str_rdd.checkpoint()
> str_rdd.count
> // rdd.map some dummy function
> def test(a : String) : String = { return a }
> str_rdd.map(test).count
> this results in a surprising exception!
> org.apache.spark.SparkException: Job aborted due to stage failure: Task not
> serializable: java.io.NotSerializableException: scala.util.Random
> 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$$anonfun$abortStage$1.apply(DAGScheduler.scala:1015)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1015)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:770)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:713)
> at
> org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:697)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1176)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$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)
--
This message was sent by Atlassian JIRA
(v6.2#6252)
