My JDK is Java 1.8 u40 On Sun, Jul 24, 2016 at 3:45 AM, Ted Yu <yuzhih...@gmail.com> wrote:
> Since you specified +PrintGCDetails, you should be able to get some more > detail from the GC log. > > Also, which JDK version are you using ? > > Please use Java 8 where G1GC is more reliable. > > On Sat, Jul 23, 2016 at 10:38 AM, Ascot Moss <ascot.m...@gmail.com> wrote: > >> 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. >>> >>> >>> >> >
