you can also activate detail GC prints to get more infos

2016-02-11 7:43 GMT+01:00 Shiva Ramagopal <tr.s...@gmail.com>:

> How are you submitting/running the job - via spark-submit or as a plain
> old Java program?
>
> If you are using spark-submit, you can control the memory setting via the
> configuration parameter spark.executor.memory in spark-defaults.conf.
>
> If you are running it as a Java program, use -Xmx to set the maximum heap
> size.
>
> On Thu, Feb 11, 2016 at 5:46 AM, Nirav Patel <npa...@xactlycorp.com>
> wrote:
>
>> In Yarn we have following settings enabled so that job can use virtual
>> memory to have a capacity beyond physical memory off course.
>>
>> <property>
>>         <name>yarn.nodemanager.vmem-check-enabled</name>
>>         <value>false</value>
>> </property>
>>
>> <property>
>>         <name>yarn.nodemanager.pmem-check-enabled</name>
>>         <value>false</value>
>> </property>
>>
>> vmem to pmem ration is 2:1. However spark doesn't seem to be able to
>> utilize this vmem limits
>> we are getting following heap space error which seemed to be contained
>> within spark executor.
>>
>> 16/02/09 23:08:06 ERROR executor.CoarseGrainedExecutorBackend: RECEIVED
>> SIGNAL 15: SIGTERM
>> 16/02/09 23:08:06 ERROR executor.Executor: Exception in task 4.0 in stage
>> 7.6 (TID 22363)
>> java.lang.OutOfMemoryError: Java heap space
>> at java.util.IdentityHashMap.resize(IdentityHashMap.java:469)
>> at java.util.IdentityHashMap.put(IdentityHashMap.java:445)
>> at
>> org.apache.spark.util.SizeEstimator$SearchState.enqueue(SizeEstimator.scala:159)
>> at
>> org.apache.spark.util.SizeEstimator$$anonfun$visitSingleObject$1.apply(SizeEstimator.scala:203)
>> at
>> org.apache.spark.util.SizeEstimator$$anonfun$visitSingleObject$1.apply(SizeEstimator.scala:202)
>> at scala.collection.immutable.List.foreach(List.scala:318)
>> at
>> org.apache.spark.util.SizeEstimator$.visitSingleObject(SizeEstimator.scala:202)
>> at
>> org.apache.spark.util.SizeEstimator$.org$apache$spark$util$SizeEstimator$$estimate(SizeEstimator.scala:186)
>> at org.apache.spark.util.SizeEstimator$.estimate(SizeEstimator.scala:54)
>> at
>> org.apache.spark.util.collection.SizeTracker$class.takeSample(SizeTracker.scala:78)
>> at
>> org.apache.spark.util.collection.SizeTracker$class.afterUpdate(SizeTracker.scala:70)
>> at
>> org.apache.spark.util.collection.SizeTrackingVector.$plus$eq(SizeTrackingVector.scala:31)
>> at
>> org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:278)
>> at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
>> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262)
>> at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>> at org.apache.spark.scheduler.Task.run(Task.scala:88)
>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>> 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:744)
>>
>>
>>
>> Yarn resource manager doesn't give any indication that whether container
>> ran out of phycial or virtual memory limits.
>>
>> Also how to profile this container memory usage? We know our data is
>> skewed so some of the executor will have large data (~2M RDD objects) to
>> process. I used following as executorJavaOpts but it doesn't seem to work.
>> -XX:-HeapDumpOnOutOfMemoryError -XX:OnOutOfMemoryError='kill -3 %p'
>> -XX:HeapDumpPath=/opt/cores/spark
>>
>>
>>
>>
>>
>>
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>>
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>
>
>


-- 
*Olivier Girardot* | AssociƩ
o.girar...@lateral-thoughts.com
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