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From "Tomer Kaftan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-17110) Pyspark with locality ANY throw java.io.StreamCorruptedException
Date Sat, 27 Aug 2016 06:07:20 GMT

     [ https://issues.apache.org/jira/browse/SPARK-17110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Tomer Kaftan updated SPARK-17110:
---------------------------------
    Description: 
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}

  was:
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 nodes 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}


> 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, Spark
2.0.0, hadoop: yarn, 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:
> {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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