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https://issues.apache.org/jira/browse/SPARK-17110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Josh Rosen resolved SPARK-17110.
--------------------------------
Resolution: Fixed
Fix Version/s: 2.1.0
2.0.1
Issue resolved by pull request 14952
[https://github.com/apache/spark/pull/14952]
> Pyspark with locality ANY throw java.io.StreamCorruptedException
> ----------------------------------------------------------------
>
> Key: SPARK-17110
> URL: https://issues.apache.org/jira/browse/SPARK-17110
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.0.0
> Environment: Cluster of 2 AWS r3.xlarge slaves launched via ec2
> scripts, Spark 2.0.0, hadoop: yarn, pyspark shell
> Reporter: Tomer Kaftan
> Assignee: Josh Rosen
> Priority: Critical
> Fix For: 2.0.1, 2.1.0
>
>
> In Pyspark 2.0.0, any task that accesses cached data non-locally throws a
> StreamCorruptedException like the stacktrace below:
> {noformat}
> WARN TaskSetManager: Lost task 7.0 in stage 2.0 (TID 26, 172.31.26.184):
> java.io.StreamCorruptedException: invalid stream header: 12010A80
> at
> java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:807)
> at java.io.ObjectInputStream.<init>(ObjectInputStream.java:302)
> at
> org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63)
> at
> org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63)
> at
> org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122)
> at
> org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:146)
> at
> org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:524)
> at
> org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:522)
> at scala.Option.map(Option.scala:146)
> at
> org.apache.spark.storage.BlockManager.getRemoteValues(BlockManager.scala:522)
> at org.apache.spark.storage.BlockManager.get(BlockManager.scala:609)
> at
> org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:661)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> 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)
> {noformat}
> The simplest way I have found to reproduce this is by running the following
> code in the pyspark shell, on a cluster of 2 slaves set to use only one
> worker core each:
> {code}
> x = sc.parallelize([1, 1, 1, 1, 1, 1000, 1, 1, 1], numSlices=9).cache()
> x.count()
> import time
> def waitMap(x):
> time.sleep(x)
> return x
> x.map(waitMap).count()
> {code}
> Or by running the following via spark-submit:
> {code}
> from pyspark import SparkContext
> sc = SparkContext()
> x = sc.parallelize([1, 1, 1, 1, 1, 1000, 1, 1, 1], numSlices=9).cache()
> x.count()
> import time
> def waitMap(x):
> time.sleep(x)
> return x
> x.map(waitMap).count()
> {code}
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