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Heap space!! java.lang.OutOfMemoryError: Java heap space at java.util.Arrays.copyOf(Arrays.java:3332) #317

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ruebot opened this Issue Apr 24, 2019 · 5 comments

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@ruebot @jrwiebe
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commented Apr 24, 2019

Describe the bug

On a number FDLP and Stanford collections, we run into this space heap space error, and it kills the Spark job. Upon investigation, this does not seem to be a problem with a large ARC/WARC or large collection. This happens on small and large collections.

19/04/18 18:02:34 DEBUG BlockManager: Told master about block rdd_3_3
19/04/18 18:02:34 INFO ArchiveRecordInputFormat: Closed archive file file:/tuna1/scratch/nruest/auk_collection_testing/10689/warcs/ARCHIVEIT-10689-TEST-JOB677126-20180828211602861-00009.warc.gz
19/04/18 18:02:34 ERROR Executor: Exception in task 3.0 in stage 0.0 (TID 3)
java.lang.OutOfMemoryError: Java heap space
        at java.util.Arrays.copyOf(Arrays.java:3332)
        at java.lang.StringCoding.safeTrim(StringCoding.java:89)
        at java.lang.StringCoding.access$100(StringCoding.java:50)
        at java.lang.StringCoding$StringDecoder.decode(StringCoding.java:154)
        at java.lang.StringCoding.decode(StringCoding.java:193)
        at java.lang.StringCoding.decode(StringCoding.java:254)
        at java.lang.String.<init>(String.java:546)
        at java.lang.String.<init>(String.java:566)
        at io.archivesunleashed.ArchiveRecordImpl.<init>(ArchiveRecord.scala:117)
        at io.archivesunleashed.package$RecordLoader$$anonfun$loadArchives$2.apply(package.scala:69)
        at io.archivesunleashed.package$RecordLoader$$anonfun$loadArchives$2.apply(package.scala:69)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
        at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
        at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:139)
        at org.apache.spark.serializer.SerializerManager.dataSerializeStream(SerializerManager.scala:174)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1$$anonfun$apply$10.apply(BlockManager.scala:1203)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1$$anonfun$apply$10.apply(BlockManager.scala:1201)
        at org.apache.spark.storage.DiskStore.put(DiskStore.scala:69)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1201)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156)
        at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
        at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
        at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
        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.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
19/04/18 18:02:34 DEBUG TaskSchedulerImpl: parentName: , name: TaskSet_0.0, runningTasks: 25
19/04/18 18:02:34 INFO SparkContext: Invoking stop() from shutdown hook
19/04/18 18:02:34 WARN TaskSetManager: Lost task 3.0 in stage 0.0 (TID 3, localhost, executor driver): java.lang.OutOfMemoryError: Java heap space
        at java.util.Arrays.copyOf(Arrays.java:3332)
        at java.lang.StringCoding.safeTrim(StringCoding.java:89)
        at java.lang.StringCoding.access$100(StringCoding.java:50)
        at java.lang.StringCoding$StringDecoder.decode(StringCoding.java:154)
        at java.lang.StringCoding.decode(StringCoding.java:193)
        at java.lang.StringCoding.decode(StringCoding.java:254)
        at java.lang.String.<init>(String.java:546)
        at java.lang.String.<init>(String.java:566)
        at io.archivesunleashed.ArchiveRecordImpl.<init>(ArchiveRecord.scala:117)
        at io.archivesunleashed.package$RecordLoader$$anonfun$loadArchives$2.apply(package.scala:69)
        at io.archivesunleashed.package$RecordLoader$$anonfun$loadArchives$2.apply(package.scala:69)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
        at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
        at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:139)
        at org.apache.spark.serializer.SerializerManager.dataSerializeStream(SerializerManager.scala:174)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1$$anonfun$apply$10.apply(BlockManager.scala:1203)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1$$anonfun$apply$10.apply(BlockManager.scala:1201)
        at org.apache.spark.storage.DiskStore.put(DiskStore.scala:69)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1201)
        at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156)
        at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
        at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
        at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
        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.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)

To Reproduce
Steps to reproduce the behavior on tuna:

  1. Using /tuna1/scratch/nruest/auk_collection_testing/10689
  2. /home/ruestn/spark-2.4.1-bin-hadoop2.7/bin/spark-shell --master local[30] --driver-memory 105g --conf spark.network.timeout=10000000 --conf spark.executor.heartbeatInterval=600s --conf spark.driver.maxResultSize=4g --conf spark.serializer=org.apache.spark.serializer.KryoSerializer --conf spark.shuffle.compress=true --conf spark.rdd.compress=true -Djava.io.tmpdir=/tuna1/scratch/nruest/tmp --jars /home/ruestn/aut/target/aut-0.17.1-SNAPSHOT-fatjar.jar -i /tuna1/scratch/nruest/auk_collection_testing/10689/133/spark_jobs/10689.scala 2>&1 | tee /tuna1/scratch/nruest/auk_collection_testing/10689/133/spark_jobs/10689.scala-test.log
  3. Should pop the error around 31 in stage 1.

Expected behavior
Shouldn't hit the heap error.

Environment information

  • AUT version: 0.17.0
  • OS: Ubuntu 16.04
  • Java version: Java 8
  • Apache Spark version: 2.3.1, 2.3.2, 2.4.0, 2.4.1
  • Apache Spark w/aut: --jars and --pagackes
  • Apache Spark command used to run AUT: /home/ruestn/spark-2.4.1-bin-hadoop2.7/bin/spark-shell --master local[30] --driver-memory 105g --conf spark.network.timeout=10000000 --conf spark.executor.heartbeatInterval=600s --conf spark.driver.maxResultSize=4g --conf spark.serializer=org.apache.spark.serializer.KryoSerializer --conf spark.shuffle.compress=true --conf spark.rdd.compress=true -Djava.io.tmpdir=/tuna1/scratch/nruest/tmp --jars /home/ruestn/aut/target/aut-0.17.1-SNAPSHOT-fatjar.jar -i /tuna1/scratch/nruest/auk_collection_testing/10689/133/spark_jobs/10689.scala 2>&1 | tee /tuna1/scratch/nruest/auk_collection_testing/10689/133/spark_jobs/10689.scala-test.log

Additional context
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commented Apr 24, 2019

@jrwiebe @lintool @ianmilligan1 finally wrote it up. Let me know if I missed anything, or y'all want me to provide more info.
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commented Apr 24, 2019

@ruebot, you say:

Upon investigation, this does not seem to be a problem with a large ARC/WARC or large collection.

Can you clarify this? Are you saying that jobs run with the same parameters against other large collections – in terms of total archive size – do not result in heap space errors? Because in my testing I didn't find this to be the case. It seemed like the error was rather a function of the total size of all the WARCs being operated on simultaneously. I think I was seeing jobs bail when they didn't have about 4 times the amount of memory as [# tasks] * [average WARC size].

I found this garbage collection log analyzer useful for trying to understand how memory was being used, though I'm still puzzled that so much memory is being used. See for example how much memory is used by a job that simply chains RecordLoader.loadArchives(...).count(). Some useful background on Java garbage collection here.

To generate garbage collection logs you want to do something like this (the spark.driver.extraJavaOptions conf directive is the key):

/home/jrwiebe/spark-2.4.1-bin-hadoop2.7/bin/spark-shell --conf spark.driver.extraJavaOptions="-XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:3260-16-16g-35bigwarcs.log -XX:+PrintHeapAtGC -XX:+PrintTenuringDistribution -XX:+UnlockExperimentalVMOptions -XX:G1LogLevel=finest -XX:+UnlockDiagnosticVMOptions -XX:+G1PrintRegionLivenessInfo" --master local[16] --driver-memory 16g --conf spark.network.timeout=10000000 --conf spark.executor.heartbeatInterval=600s --conf spark.driver.maxResultSize=4g --conf spark.serializer=org.apache.spark.serializer.KryoSerializer --conf spark.shuffle.compress=true --conf spark.rdd.compress=true --packages "io.archivesunleashed:aut:0.17.0" -i 3260.scala

For the record, I'm pretty certain KryoSerializer doesn't offer any benefits for this problem; I just kept using it because I copied @ruebot's command line. I ran tests with and without some of the other tuning options as well, and was seeing similar results.
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commented Apr 24, 2019

Sorry, yeah, that's kinda vague. I meant overall, we've analyzed 170T of collections ranging from under a 1G of WARCs up to 12T with the same basic Spark settings.

I added the KryoSerializer during troubleshooting because it looked like it got further along in some cases, and reading up on it, it made sense at the time.

Thanks for putting in the GC info. I was hoping to tease it out by creating the issue 😄
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commented Apr 24, 2019

...and a single WARC as large as 80G.
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commented Apr 25, 2019

Reading the garbage collection post reminded me that Spark allocates (not sure if that is the right word), ~55-60% of "storage" memory for executors. A smarter version of me would have bookmarked that Stackoverflow or Spark mailing list thread. But, I can provide a screenshot of a the Spark UI of a currently running job.

Screenshot_2019-04-24 Spark shell - Executors

That's a job running with 30 threads, and 105G of RAM allocated: --master local[30] --driver-memory 105g
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