[jira] [Created] (SPARK-7061) Case Classes Cannot be Repartitioned/Shuffled in Spark REPL

["Russell Alexander Spitzer (JIRA)" <ji...@apache.org>]

Russell Alexander Spitzer created SPARK-7061:
------------------------------------------------

             Summary: Case Classes Cannot be Repartitioned/Shuffled in Spark REPL
                 Key: SPARK-7061
                 URL: https://issues.apache.org/jira/browse/SPARK-7061
             Project: Spark
          Issue Type: Bug
          Components: Spark Shell
    Affects Versions: 1.2.1
         Environment: Single Node Stand Alone Spark Shell
            Reporter: Russell Alexander Spitzer
            Priority: Minor


Running the following code in the  spark shell against a stand alone master.

{code}
case class CustomerID( id:Int)
sc.parallelize(1 to 1000).map(CustomerID(_)).repartition(1).take(1)
{code}

Gives the following exception

{code}
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 5, 10.0.2.15): java.lang.ClassNotFoundException: $iwC$$iwC$CustomerID
	at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
	at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
	at java.security.AccessController.doPrivileged(Native Method)
	at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
	at java.lang.Class.forName0(Native Method)
	at java.lang.Class.forName(Class.java:274)
	at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:59)
	at java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1612)
	at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1517)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1771)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
	at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
	at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
	at org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133)
	at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
	at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
	at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
	at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
	at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
	at scala.collection.AbstractIterator.to(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
	at org.apache.spark.rdd.RDD$$anonfun$27.apply(RDD.scala:1098)
	at org.apache.spark.rdd.RDD$$anonfun$27.apply(RDD.scala:1098)
	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1353)
	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1353)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
	at org.apache.spark.scheduler.Task.run(Task.scala:56)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:745)
{code}

I believe this is related to the shuffle code since the following other examples also give this exception.

{code}
val idsOfInterest = sc.parallelize(1 to 1000).map(CustomerID(_)).groupBy(_.id).take(1)
val idsOfInterest = sc.parallelize(1 to 1000).map( x => (CustomerID(_),x)).groupByKey().take(1)
val idsOfInterest = sc.parallelize(1 to 1000).map( x => (CustomerID(_),x)).reduceByKey((x,y) => x+y).take(1)
{code}

But these functions do not
{code}
sc.parallelize(1 to 1000).map(CustomerID(_)).reduce( (x,y) => CustomerID(x.id+y.id) )
sc.parallelize(1 to 1000).map(CustomerID(_)).map( x=> CustomerID(x.id+5) ).take(1)
{code}


All of these examples work in application code.



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