[
https://issues.apache.org/jira/browse/SPARK-16549?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Niels Becker updated SPARK-16549:
---------------------------------
Description:
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.
{code}
val write = new java.io.PrintWriter(new java.io.FileOutputStream(outFile, true))
write.println("load, exec, spark_load, spark_exec")
for(i <- 1 to count) {
val startLoadTime = System.nanoTime()
val df = sqlContext.read.json(...)
val startExecTime = System.nanoTime()
df.groupBy(...).count.collect
val endTime = System.nanoTime()
val str = s"${timediff(startLoadTime, startExecTime)},
${timediff(startExecTime,endTime)}"
println(f"[$i%03d/${count}%03d] $str%s")
write.println(str)
write.flush()
}
write.close()
I can upload core dump, error log and app code if needed.
was:
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.
{code}
val write = new java.io.PrintWriter(new java.io.FileOutputStream(outFile, true))
write.println("load, exec, spark_load, spark_exec")
println("Starting Context")
sqlContext = setupContext(dfName + "-" + testName)
val timer = new TimeListener()
sqlContext.sparkContext.addSparkListener(timer)
println("Running Test "+testName+" with DF "+dfName+"
"+(count*scaleFactor).toInt+" times")
for(i <- 1 to (count*scaleFactor).toInt) {
val startLoadTime = System.nanoTime()
val df = dfLoader(testJoin)
val startExecTime = System.nanoTime()
testFn(df)
val endTime = System.nanoTime()
val lastJobTimes = timer.getLastTimings
val str = s"${timediff(startLoadTime, startExecTime)},
${timediff(startExecTime,endTime)}, ${lastJobTimes._1/1000.0},
${lastJobTimes._2/1000.0}"
println(f"[$i%03d/${(count*scaleFactor).toInt}%03d] $str%s")
write.println(str)
write.flush()
}
write.close()
I can upload core dump, error log and app code if needed.
> 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.
> {code}
> val write = new java.io.PrintWriter(new java.io.FileOutputStream(outFile,
> true))
> write.println("load, exec, spark_load, spark_exec")
>
> for(i <- 1 to count) {
> val startLoadTime = System.nanoTime()
> val df = sqlContext.read.json(...)
> val startExecTime = System.nanoTime()
> df.groupBy(...).count.collect
> val endTime = System.nanoTime()
> val str = s"${timediff(startLoadTime, startExecTime)},
> ${timediff(startExecTime,endTime)}"
> println(f"[$i%03d/${count}%03d] $str%s")
> write.println(str)
> write.flush()
> }
> write.close()
> I can upload core dump, error log and app code if needed.
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