Niels Becker created SPARK-16549:
------------------------------------
Summary: 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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