[jira] [Created] (SPARK-17110) Pyspark with locality ANY throw java.io.StreamCorruptedException

["Tomer Kaftan (JIRA)" <ji...@apache.org>]

Tomer Kaftan created SPARK-17110:
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             Summary: 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 nodes launched via ec2 scripts, pyspark shell
            Reporter: Tomer Kaftan
            Priority: Critical


In Pyspark 2.0.0, any task that accesses cached data non-locally throws a StreamCorruptedException like the stacktrace below:

```
WARN TaskSetManager: Lost task 7.0 in stage 2.0 (TID 26, 172.31.26.184): java.io.StreamC
orruptedException: 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)
```

The simplest way I have found to reproduce this is by running the following code in the pyspark shell, on a cluster of 2 nodes set to use only one worker core each:

```python
x = sc.parallelize([1, 1, 1, 1, 1, 1000, 1, 1, 1], numSlices=9).cache()

import time
def waitMap(x):
    time.sleep(x)
    return x

x.map(waitMap).count()
```



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