[SPARK-4105] FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle

• Type: Bug
• Status: Resolved
• Priority: Critical
• Resolution: Fixed
• Affects Version/s: 1.2.0, 1.2.1, 1.3.0, 1.4.1, 1.5.1, 1.6.1, 2.0.0
• Fix Version/s: 2.2.0
• Component/s: Shuffle, Spark Core
• Assignee: Davies Liu
• Reporter: Josh Rosen
• Created: 28/Oct/14 00:19
• Updated: 14/Dec/21 02:39
• Resolved: 09/Dec/16 23:45

Description

We have seen non-deterministic FAILED_TO_UNCOMPRESS(5) errors during shuffle read. Here's a sample stacktrace from an executor:

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)

Here's another occurrence of a similar error:

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)

The first stacktrace was reported by a Spark user. The second stacktrace occurred when running

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
}

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.

Issue Links

• is duplicated by: SPARK-12418 spark shuffle FAILED_TO_UNCOMPRESS
• relates to: SPARK-4107 Incorrect handling of Channel.read()'s return value may lead to data truncation
• relates to: SPARK-7660 Snappy-java buffer-sharing bug leads to data corruption / test failures
• relates to: SPARK-27562 Complete the verification mechanism for shuffle transmitted data
• relates to: SPARK-26089 Handle large corrupt shuffle blocks
• relates to: SPARK-3630 Identify cause of Kryo+Snappy PARSING_ERROR
• links to: [Github] Pull Request #12700 (DoingDone9)
• links to: [Github] Pull Request #15923 (davies)