Have you seen the following ? http://stackoverflow.com/questions/27553547/xloggc-not-creating-log-file-if-path-doesnt-exist-for-the-first-time
On Sat, Jul 23, 2016 at 5:18 PM, Ascot Moss <ascot.m...@gmail.com> wrote: > I tried to add -Xloggc:./jvm_gc.log > > --conf "spark.executor.extraJavaOptions=-XX:+UseG1GC -XX:+PrintGCDetails > -XX:+PrintGCTimeStamps -Xloggc:./jvm_gc.log -XX:+PrintGCDateStamps" > > however, I could not find ./jvm_gc.log > > How to resolve the OOM and gc log issue? > > Regards > > On Sun, Jul 24, 2016 at 6:37 AM, Ascot Moss <ascot.m...@gmail.com> wrote: > >> 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. 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