[SPARK-17110] Pyspark with locality ANY throw java.io.StreamCorruptedException

• Type: Bug
• Status: Resolved
• Priority: Critical
• Resolution: Fixed
• Affects Version/s: 2.0.0
• Fix Version/s: 2.0.1, 2.1.0
• Component/s: PySpark

Environment

Cluster of 2 AWS r3.xlarge slaves launched via ec2 scripts, Spark 2.0.0, hadoop: yarn, pyspark shell

• Target Version/s: 2.0.1, 2.1.0
• Assignee: Josh Rosen
• Reporter: Tomer Kaftan
• Created: 17/Aug/16 16:59
• Updated: 07/Sep/16 02:34
• Resolved: 06/Sep/16 22:08

Description

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.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)

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:

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()

Or by running the following via spark-submit:

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()

Issue Links

• links to: [Github] Pull Request #14952 (JoshRosen)