Hi,
I added the following parameter:
--conf "spark.executor.extraJavaOptions=-XX:+UseG1GC
-XX:MaxGCPauseMillis=200 -XX:ParallelGCThreads=20 -XX:ConcGCThreads=5
-XX:InitiatingHeapOccupancyPercent=70 -XX:+PrintGCDetails
-XX:+PrintGCTimeStamps"
Still got Java heap space error.
Any idea to resolve? (my spark is 1.6.1)
16/07/23 23:31:50 WARN TaskSetManager: Lost task 1.0 in stage 6.0 (TID 22,
n1791): java.lang.OutOfMemoryError: Java heap space at
scala.reflect.ManifestFactory$$anon$12.newArray(Manifest.scala:138)
at
scala.reflect.ManifestFactory$$anon$12.newArray(Manifest.scala:136)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:248)
at
org.apache.spark.util.collection.CompactBuffer.toArray(CompactBuffer.scala:30)
at
org.apache.spark.mllib.tree.DecisionTree$.org$apache$spark$mllib$tree$DecisionTree$$findSplits$1(DecisionTree.scala:1009)
at
org.apache.spark.mllib.tree.DecisionTree$$anonfun$29.apply(DecisionTree.scala:1042)
at
org.apache.spark.mllib.tree.DecisionTree$$anonfun$29.apply(DecisionTree.scala:1042)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at
org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
at
org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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)
Regards
On Sat, Jul 23, 2016 at 9:49 AM, Ascot Moss <ascot.m...@gmail.com> wrote:
> Thanks. Trying with extra conf now.
>
> On Sat, Jul 23, 2016 at 6:59 AM, RK Aduri <rkad...@collectivei.com> wrote:
>
>> I can see large number of collections happening on driver and eventually,
>> driver is running out of memory. ( am not sure whether you have persisted
>> any rdd or data frame). May be you would want to avoid doing so many
>> collections or persist unwanted data in memory.
>>
>> To begin with, you may want to re-run the job with this following config:
>> --conf
>> "spark.executor.extraJavaOptions=-XX:+UseG1GC -XX:+PrintGCDetails
>> -XX:+PrintGCTimeStamps” —> and this will give you an idea of how you are
>> getting OOM.
>>
>>
>> On Jul 22, 2016, at 3:52 PM, Ascot Moss <ascot.m...@gmail.com> wrote:
>>
>> Hi
>>
>> Please help!
>>
>> When running random forest training phase in cluster mode, I got GC
>> overhead limit exceeded.
>>
>> I have used two parameters when submitting the job to cluster
>>
>> --driver-memory 64g \
>>
>> --executor-memory 8g \
>>
>> My Current settings:
>>
>> (spark-defaults.conf)
>>
>> spark.executor.memory 8g
>>
>> (spark-env.sh)
>>
>> export SPARK_WORKER_MEMORY=8g
>>
>> export HADOOP_HEAPSIZE=8000
>>
>>
>> Any idea how to resolve it?
>>
>> Regards
>>
>>
>>
>>
>>
>>
>> ### (the erro log) ###
>>
>> 16/07/23 04:34:04 WARN TaskSetManager: Lost task 2.0 in stage 6.1 (TID
>> 30, n1794): java.lang.OutOfMemoryError: GC overhead limit exceeded
>>
>> at
>> scala.reflect.ManifestFactory$$anon$12.newArray(Manifest.scala:138)
>>
>> at
>> scala.reflect.ManifestFactory$$anon$12.newArray(Manifest.scala:136)
>>
>> at
>> org.apache.spark.util.collection.CompactBuffer.growToSize(CompactBuffer.scala:144)
>>
>> at
>> org.apache.spark.util.collection.CompactBuffer.$plus$plus$eq(CompactBuffer.scala:90)
>>
>> at
>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$groupByKey$1$$anonfun$10.apply(PairRDDFunctions.scala:505)
>>
>> at
>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$groupByKey$1$$anonfun$10.apply(PairRDDFunctions.scala:505)
>>
>> at
>> org.apache.spark.util.collection.ExternalAppendOnlyMap$ExternalIterator.mergeIfKeyExists(ExternalAppendOnlyMap.scala:318)
>>
>> at
>> org.apache.spark.util.collection.ExternalAppendOnlyMap$ExternalIterator.next(ExternalAppendOnlyMap.scala:365)
>>
>> at
>> org.apache.spark.util.collection.ExternalAppendOnlyMap$ExternalIterator.next(ExternalAppendOnlyMap.scala:265)
>>
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>
>> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>
>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>
>> at
>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>>
>> at
>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>
>> at
>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>
>> at scala.collection.TraversableOnce$class.to
>> (TraversableOnce.scala:273)
>>
>> at scala.collection.AbstractIterator.to
>> <http://scala.collection.abstractiterator.to/>(Iterator.scala:1157)
>>
>> at
>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>
>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>>
>> at
>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>
>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>>
>> at
>> org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
>>
>> at
>> org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
>>
>> at
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>
>> at
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>
>> at
>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>
>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>
>> at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>
>> 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)
>>
>>
>>
>> Collective[i] dramatically improves sales and marketing performance using
>> technology, applications and a revolutionary network designed to provide
>> next generation analytics and decision-support directly to business users.
>> Our goal is to maximize human potential and minimize mistakes. In most
>> cases, the results are astounding. We cannot, however, stop emails from
>> sometimes being sent to the wrong person. If you are not the intended
>> recipient, please notify us by replying to this email's sender and deleting
>> it (and any attachments) permanently from your system. If you are, please
>> respect the confidentiality of this communication's contents.
>
>
>
