[jira] [Created] (SPARK-3050) Spark program running with 1.0.2 jar cannot run against a 1.0.1 cluster

["Mingyu Kim (JIRA)" <ji...@apache.org>]

Mingyu Kim created SPARK-3050:
---------------------------------

             Summary: Spark program running with 1.0.2 jar cannot run against a 1.0.1 cluster
                 Key: SPARK-3050
                 URL: https://issues.apache.org/jira/browse/SPARK-3050
             Project: Spark
          Issue Type: Bug
    Affects Versions: 1.0.2
            Reporter: Mingyu Kim
            Priority: Critical


I ran the following code with Spark 1.0.2 jar against a cluster that runs Spark 1.0.1 (i.e. localhost:7077 is running 1.0.1).

{code}
import java.util.ArrayList;
import java.util.List;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;

public class TestTest {

    public static void main(String[] args) {
        JavaSparkContext sc = new JavaSparkContext("spark://localhost:7077", "Test");
        List<Integer> list = new ArrayList<>();
        list.add(1);
        list.add(2);
        list.add(3);
        JavaRDD<Integer> rdd = sc.parallelize(list);
        System.out.println(rdd.collect());
    }

}
{code}

This throws InvalidClassException.

{code}
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:1 failed 4 times, most recent failure: Exception failure in TID 6 on host 10.100.91.90: java.io.InvalidClassException: org.apache.spark.rdd.RDD; local class incompatible: stream classdesc serialVersionUID = -6766554341038829528, local class serialVersionUID = 385418487991259089
        java.io.ObjectStreamClass.initNonProxy(ObjectStreamClass.java:604)
        java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1620)
        java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1515)
        java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1620)
        java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1515)
        java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1769)
        java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348)
        java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
        org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:63)
        org.apache.spark.scheduler.ResultTask$.deserializeInfo(ResultTask.scala:61)
        org.apache.spark.scheduler.ResultTask.readExternal(ResultTask.scala:141)
        java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1835)
        java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1794)
        java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348)
        java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
        org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:63)
        org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:85)
        org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:165)
        java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
        java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
        java.lang.Thread.run(Thread.java:722)
Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1049)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1033)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1031)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1031)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:635)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:635)
	at scala.Option.foreach(Option.scala:236)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:635)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1234)
	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
	at akka.actor.ActorCell.invoke(ActorCell.scala:456)
	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
	at akka.dispatch.Mailbox.run(Mailbox.scala:219)
	at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
	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)
{code}

I thought minor version releases and patch releases should be binary-compatible. Is that not true?



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