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Russell Alexander Spitzer commented on SPARK-7061: -------------------------------------------------- Thanks! I didn't see it come up in a general text search, only the previous one I linked. But that is the issue (6299) > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org