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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)