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https://issues.apache.org/jira/browse/SPARK-7061?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-7061.
------------------------------
Resolution: Duplicate
Search JIRA first if you please. I think you're just hitting something already
resolved.
> 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 and when the shell is run in
> Local mode.
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