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Posted to issues@spark.apache.org by "Josh Rosen (JIRA)" <ji...@apache.org> on 2014/10/28 01:20:33 UTC
[jira] [Created] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when
fetching shuffle data with sort-based shuffle
Josh Rosen created SPARK-4105:
---------------------------------
Summary: FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID 33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.<init>(SnappyInputStream.java:58)
at org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:181)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{code}
Here's another occurrence of a similar error:
{code}
java.io.IOException: failed to read chunk
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:348)
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:159)
org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
java.io.ObjectInputStream$PeekInputStream.read(ObjectInputStream.java:2310)
java.io.ObjectInputStream$BlockDataInputStream.read(ObjectInputStream.java:2712)
java.io.ObjectInputStream$BlockDataInputStream.readFully(ObjectInputStream.java:2742)
java.io.ObjectInputStream.readArray(ObjectInputStream.java:1687)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1344)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133)
org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:58)
org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:46)
org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:92)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
org.apache.spark.scheduler.Task.run(Task.scala:56)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:182)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:745)
{code}
The first stacktrace was reported by a Spark user. The second stacktrace occurred when running
{code}
import java.util.Random
val numKeyValPairs=1000
val numberOfMappers=200
val keySize=10000
for (i <- 0 to 19) {
val pairs1 = sc.parallelize(0 to numberOfMappers, numberOfMappers).flatMap(p=>{
val randGen = new Random
val arr1 = new Array[(Int, Array[Byte])](numKeyValPairs)
for (i <- 0 until numKeyValPairs){
val byteArr = new Array[Byte](keySize)
randGen.nextBytes(byteArr)
arr1(i) = (randGen.nextInt(Int.MaxValue),byteArr)
}
arr1
})
pairs1.groupByKey(numberOfMappers).count
}
{code}
This job frequently runs without any problems, but when it fails it seem that every post-shuffle task fails with either PARSING_ERROR(2), FAILED_TO_UNCOMPRESS(5), or some other decompression error. I've seen reports of similar problems when using LZF compression, so I think that this is caused by some sort of general stream corruption issue.
This issue has been observed even when no spilling occurs, so I don't believe that this is due to a bug in spilling code.
I was unable to reproduce this when running this code in a fresh Spark EC2 cluster and we've been having a hard time finding a deterministic reproduction.
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