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From "Alex Baretta (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-5235) java.io.NotSerializableException: org.apache.spark.sql.SQLConf
Date Wed, 14 Jan 2015 17:12:35 GMT

    [ https://issues.apache.org/jira/browse/SPARK-5235?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14277240#comment-14277240
] 

Alex Baretta commented on SPARK-5235:
-------------------------------------

[~sowen] I agree with you that contexts have no business getting serialized and shipped around
the cluster. That being said, this issue is a regression, as this simply used to work. Since
this is a regression, it is very much appropriate to fix the issue by restoring the previous
behavior, and then take time to think of a better design.

> java.io.NotSerializableException: org.apache.spark.sql.SQLConf
> --------------------------------------------------------------
>
>                 Key: SPARK-5235
>                 URL: https://issues.apache.org/jira/browse/SPARK-5235
>             Project: Spark
>          Issue Type: Bug
>            Reporter: Alex Baretta
>
> The SQLConf field in SQLContext is neither Serializable nor transient. Here's the stack
trace I get when running SQL queries against a Parquet file.
> Exception in thread "Thread-43" org.apache.spark.SparkException: Job aborted due to stage
failure: Task not serializable: java.io.NotSerializableException: org.apache.spark.sql.SQLConf
>         at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1195)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1184)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1183)
>         at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1183)
>         at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:843)
>         at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:779)
>         at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:763)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1364)
>         at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundReceive(DAGScheduler.scala:1356)
>         at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
>         at akka.actor.ActorCell.invoke(ActorCell.scala:487)
>         at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
>         at akka.dispatch.Mailbox.run(Mailbox.scala:220)
>         at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
>         at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
>         at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
>         at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
>         at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)



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