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Posted to issues@spark.apache.org by "Patrick Wendell (JIRA)" <ji...@apache.org> on 2014/08/15 02:09:18 UTC
[jira] [Updated] (SPARK-3050) Spark program running with 1.0.2 jar
cannot run against a 1.0.1 cluster
[ https://issues.apache.org/jira/browse/SPARK-3050?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Patrick Wendell updated SPARK-3050:
-----------------------------------
Priority: Major (was: Critical)
> 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
>
> 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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