what's is your exector memory , please share the code also On Fri, Aug 18, 2017 at 10:06 AM, KhajaAsmath Mohammed < mdkhajaasm...@gmail.com> wrote:
> > HI, > > I am getting below error when running spark sql jobs. This error is thrown > after running 80% of tasks. any solution? > > spark.storage.memoryFraction=0.4 > spark.sql.shuffle.partitions=2000 > spark.default.parallelism=100 > #spark.eventLog.enabled=false > #spark.scheduler.revive.interval=1s > spark.driver.memory=8g > > > java.lang.OutOfMemoryError: GC overhead limit exceeded > at java.util.ArrayList.subList(ArrayList.java:955) > at java.lang.String.split(String.java:2311) > at sun.net.util.IPAddressUtil.textToNumericFormatV4( > IPAddressUtil.java:47) > at java.net.InetAddress.getAllByName(InetAddress.java:1129) > at java.net.InetAddress.getAllByName(InetAddress.java:1098) > at java.net.InetAddress.getByName(InetAddress.java:1048) > at org.apache.hadoop.net.NetUtils.normalizeHostName( > NetUtils.java:562) > at org.apache.hadoop.net.NetUtils.normalizeHostNames( > NetUtils.java:579) > at org.apache.hadoop.net.CachedDNSToSwitchMapping.resolve( > CachedDNSToSwitchMapping.java:109) > at org.apache.hadoop.yarn.util.RackResolver.coreResolve( > RackResolver.java:101) > at org.apache.hadoop.yarn.util.RackResolver.resolve( > RackResolver.java:81) > at org.apache.spark.scheduler.cluster.YarnScheduler. > getRackForHost(YarnScheduler.scala:37) > at org.apache.spark.scheduler.TaskSetManager.dequeueTask( > TaskSetManager.scala:380) > at org.apache.spark.scheduler.TaskSetManager.resourceOffer( > TaskSetManager.scala:433) > at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$ > org$apache$spark$scheduler$TaskSchedulerImpl$$ > resourceOfferSingleTaskSet$1.apply$mcVI$sp(TaskSchedulerImpl.scala:276) > at scala.collection.immutable.Range.foreach$mVc$sp(Range. > scala:160) > at org.apache.spark.scheduler.TaskSchedulerImpl.org$apache$ > spark$scheduler$TaskSchedulerImpl$$resourceOfferSingleTaskSet( > TaskSchedulerImpl.scala:271) > at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$ > resourceOffers$4$$anonfun$apply$9.apply(TaskSchedulerImpl.scala:357) > at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$ > resourceOffers$4$$anonfun$apply$9.apply(TaskSchedulerImpl.scala:355) > at scala.collection.IndexedSeqOptimized$class. > foreach(IndexedSeqOptimized.scala:33) > at scala.collection.mutable.ArrayOps$ofRef.foreach( > ArrayOps.scala:186) > at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$ > resourceOffers$4.apply(TaskSchedulerImpl.scala:355) > at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$ > resourceOffers$4.apply(TaskSchedulerImpl.scala:352) > at scala.collection.mutable.ResizableArray$class.foreach( > ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach( > ArrayBuffer.scala:48) > at org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers( > TaskSchedulerImpl.scala:352) > at org.apache.spark.scheduler.cluster. > CoarseGrainedSchedulerBackend$DriverEndpoint.org$apache$ > spark$scheduler$cluster$CoarseGrainedSchedulerBackend$ > DriverEndpoint$$makeOffers(CoarseGrainedSchedulerBackend.scala:222) > >
