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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/07/14 17:52:20 UTC
[jira] [Resolved] (SPARK-16549) GC Overhead Limit Reached and Core
Dump
[ https://issues.apache.org/jira/browse/SPARK-16549?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-16549.
-------------------------------
Resolution: Fixed
"Out of memory" isn't a Spark bug. From your description, it's not clear that you aren't using a lot of memory. I'm going to close this if there's not a much clearer reason to think there's a leak
> GC Overhead Limit Reached and Core Dump
> ---------------------------------------
>
> Key: SPARK-16549
> URL: https://issues.apache.org/jira/browse/SPARK-16549
> Project: Spark
> Issue Type: Bug
> Affects Versions: 1.6.1
> Environment: Mesos, Docker
> Reporter: Niels Becker
>
> I'm submitting my application via spark-submit. It is running a long living Context with many jobs and tasks.
> For a lot of tasks I get a error message:
> {quote}
> 16/07/13 19:46:12 ERROR TaskSchedulerImpl: Ignoring update with state FINISHED for TID 1387674 because its task set is gone (this is likely the result of receiving duplicate task finished status updates)
> {quote}
> After a while I got erros like:
> {quote}
> 16/07/13 19:45:43 ERROR Utils: Uncaught exception in thread task-result-getter-4
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at java.util.Arrays.copyOf(Arrays.java:3332)
> at java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:137)
> at java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:121)
> at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:421)
> at java.lang.StringBuilder.append(StringBuilder.java:136)
> at java.lang.Class.getConstructor0(Class.java:3082)
> at java.lang.Class.getConstructor(Class.java:1825)
> at com.esotericsoftware.kryo.Kryo.newSerializer(Kryo.java:322)
> at com.esotericsoftware.kryo.Kryo.getDefaultSerializer(Kryo.java:303)
> at com.esotericsoftware.kryo.Kryo.register(Kryo.java:351)
> at org.apache.spark.serializer.KryoSerializer.newKryo(KryoSerializer.scala:140)
> at org.apache.spark.serializer.KryoSerializerInstance.borrowKryo(KryoSerializer.scala:273)
> at org.apache.spark.serializer.KryoSerializerInstance.<init>(KryoSerializer.scala:258)
> at org.apache.spark.serializer.KryoSerializer.newInstance(KryoSerializer.scala:174)
> at org.apache.spark.scheduler.DirectTaskResult.value(TaskResult.scala:96)
> at org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:60)
> at org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:51)
> at org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:51)
> at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
> at org.apache.spark.scheduler.TaskResultGetter$$anon$2.run(TaskResultGetter.scala:50)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {quote}
> Finaly in the end the entire JVM crashed:
> {quote}
> #
> # A fatal error has been detected by the Java Runtime Environment:
> #
> # SIGSEGV (0xb) at pc=0x00007f576f13c7d3, pid=1152, tid=140007008368384
> #
> # JRE version: OpenJDK Runtime Environment (8.0_91-b14) (build 1.8.0_91-8u91-b14-1~bpo8+1-b14)
> # Java VM: OpenJDK 64-Bit Server VM (25.91-b14 mixed mode linux-amd64 compressed oops)
> # Problematic frame:
> # V [libjvm.so+0x6967d3]
> #
> # Core dump written. Default location: /home/notebook/nbdata/core or core.1152
> #
> # An error report file with more information is saved as:
> # /home/notebook/nbdata/hs_err_pid1152.log
> #
> # If you would like to submit a bug report, please visit:
> # http://bugreport.java.com/bugreport/crash.jsp
> #
> Aborted (core dumped)
> {quote}
> Inside my application i have a HiveContext and repeatedly run {{sqlContext.read.json(...).groupBy(...).count.collect}} which gives around 10 results from 200 million raw json records input. On my 20 node cluster this spins up ~42000 Tasks for each run.
> My coding does not store as many data that would cause a driver with 8GB memory go out of memory. So I assume something inside Spark does not cleanup finished tasks correctly.
> I can upload core dump, error log and app code if needed.
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