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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