[SPARK-7061] Case Classes Cannot be Repartitioned/Shuffled in Spark REPL

• Type: Bug
• Status: Resolved
• Priority: Minor
• Resolution: Duplicate
• Affects Version/s: 1.2.1
• Component/s: Spark Shell

Environment

Single Node Stand Alone Spark Shell

• Assignee: Unassigned
• Reporter: Russell Spitzer
• Created: 22/Apr/15 21:41
• Updated: 22/Apr/15 23:46
• Resolved: 22/Apr/15 23:25

Description

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

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

Gives the following exception

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)

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

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)

But these functions do not

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)

All of these examples work in application code and when the shell is run in Local mode.

Issue Links

• duplicates: SPARK-6299 ClassNotFoundException in standalone mode when running groupByKey with class defined in REPL.
• is related to: SPARK-2620 case class cannot be used as key for reduce