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Posted to issues@spark.apache.org by "Aakash Mandlik (JIRA)" <ji...@apache.org> on 2019/04/25 09:31:00 UTC
[jira] [Commented] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when
fetching shuffle data with sort-based shuffle
[ https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16825901#comment-16825901 ]
Aakash Mandlik commented on SPARK-4105:
---------------------------------------
I am facing similar issue while persisting to S3 for Spark-2.4. Earlier the same code was working for spark-2.2.
19/04/25 14:42:36 WARN scheduler.TaskSetManager: Lost task 11.0 in stage 55.0 (TID 1345, node4, executor 1): org.xerial.snappy.SnappyIOExceptio
n: [EMPTY_INPUT] Cannot decompress empty stream
at org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:94)
at org.xerial.snappy.SnappyInputStream.<init>(SnappyInputStream.java:59)
at org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:164)
at org.apache.spark.serializer.SerializerManager.wrapForCompression(SerializerManager.scala:163)
at org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:209)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:698)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:696)
at scala.Option.map(Option.scala:146)
at org.apache.spark.storage.BlockManager.getRemoteValues(BlockManager.scala:696)
at org.apache.spark.storage.BlockManager.get(BlockManager.scala:820)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:875)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> 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, 1.2.1, 1.3.0, 1.4.1, 1.5.1, 1.6.1, 2.0.0
> Reporter: Josh Rosen
> Assignee: Davies Liu
> Priority: Critical
> Fix For: 2.2.0
>
> Attachments: JavaObjectToSerialize.java, SparkFailedToUncompressGenerator.scala
>
>
> 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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