How can i increase the number of tasks from 174 to 500 without running repartition.
The input size is 512.0 MB (hadoop) / 4159106. Can this be reduced to 64 MB so as to increase the number of tasks. Similar to split size that increases the number of mappers in Hadoop M/R. On Thu, Jun 25, 2015 at 12:06 AM, Akhil Das <ak...@sigmoidanalytics.com> wrote: > Look in the tuning section > <https://spark.apache.org/docs/latest/tuning.html>, also you need to > figure out whats taking time and where's your bottleneck etc. If everything > is tuned properly, then you will need to throw more cores :) > > Thanks > Best Regards > > On Thu, Jun 25, 2015 at 12:19 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> > wrote: > >> Its taking an hour and on Hadoop it takes 1h 30m, is there a way to make >> it run faster ? >> >> On Wed, Jun 24, 2015 at 11:39 AM, Akhil Das <ak...@sigmoidanalytics.com> >> wrote: >> >>> Cool. :) >>> On 24 Jun 2015 23:44, "ÐΞ€ρ@Ҝ (๏̯͡๏)" <deepuj...@gmail.com> wrote: >>> >>>> Its running now. >>>> >>>> On Wed, Jun 24, 2015 at 10:45 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> >>>> wrote: >>>> >>>>> Now running with >>>>> >>>>> *--num-executors 9973 --driver-memory 14g --driver-java-options >>>>> "-XX:MaxPermSize=512M -Xmx4096M -Xms4096M" --executor-memory 14g >>>>> --executor-cores 1* >>>>> >>>>> >>>>> On Wed, Jun 24, 2015 at 10:34 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> >>>>> wrote: >>>>> >>>>>> There are multiple of these >>>>>> >>>>>> 1) >>>>>> 15/06/24 09:53:37 ERROR executor.Executor: Exception in task 443.0 in >>>>>> stage 3.0 (TID 1767) >>>>>> java.lang.OutOfMemoryError: GC overhead limit exceeded >>>>>> at >>>>>> sun.reflect.GeneratedSerializationConstructorAccessor1327.newInstance(Unknown >>>>>> Source) >>>>>> at java.lang.reflect.Constructor.newInstance(Constructor.java:526) >>>>>> at >>>>>> org.objenesis.instantiator.sun.SunReflectionFactoryInstantiator.newInstance(SunReflectionFactoryInstantiator.java:56) >>>>>> at com.esotericsoftware.kryo.Kryo.newInstance(Kryo.java:1065) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.create(FieldSerializer.java:228) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:217) >>>>>> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:134) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.MapSerializer.read(MapSerializer.java:17) >>>>>> at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:648) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:605) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221) >>>>>> at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:648) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:605) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221) >>>>>> at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:648) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:605) >>>>>> at >>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221) >>>>>> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729) >>>>>> at com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:42) >>>>>> at com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:33) >>>>>> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729) >>>>>> at >>>>>> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:138) >>>>>> at >>>>>> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133) >>>>>> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71) >>>>>> at >>>>>> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) >>>>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) >>>>>> at >>>>>> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) >>>>>> at >>>>>> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) >>>>>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>>>>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>>>>> at >>>>>> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:125) >>>>>> 15/06/24 09:53:37 ERROR actor.ActorSystemImpl: exception on LARS? >>>>>> timer thread >>>>>> >>>>>> 2) >>>>>> 15/06/24 09:53:37 ERROR actor.ActorSystemImpl: exception on LARS? >>>>>> timer thread >>>>>> java.lang.OutOfMemoryError: GC overhead limit exceeded >>>>>> at akka.dispatch.AbstractNodeQueue.<init>(AbstractNodeQueue.java:22) >>>>>> at >>>>>> akka.actor.LightArrayRevolverScheduler$TaskQueue.<init>(Scheduler.scala:443) >>>>>> at >>>>>> akka.actor.LightArrayRevolverScheduler$$anon$8.nextTick(Scheduler.scala:409) >>>>>> at >>>>>> akka.actor.LightArrayRevolverScheduler$$anon$8.run(Scheduler.scala:375) >>>>>> at java.lang.Thread.run(Thread.java:745) >>>>>> 3) >>>>>> # java.lang.OutOfMemoryError: GC overhead limit exceeded >>>>>> # -XX:OnOutOfMemoryError="kill %p" >>>>>> # Executing /bin/sh -c "kill 20674"... >>>>>> [ERROR] [06/24/2015 09:53:37.590] [Executor task launch worker-5] >>>>>> [akka.tcp://sparkdri...@phxdpehdc9dn2137.stratus.phx.ebay.com:47708/] >>>>>> swallowing exception during message send >>>>>> (akka.remote.RemoteTransportExceptionNoStackTrace) >>>>>> >>>>>> >>>>>> On Wed, Jun 24, 2015 at 10:31 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> I see this >>>>>>> >>>>>>> java.lang.OutOfMemoryError: GC overhead limit exceeded >>>>>>> at java.util.Arrays.copyOfRange(Arrays.java:2694) >>>>>>> at java.lang.String.<init>(String.java:203) >>>>>>> at java.lang.StringBuilder.toString(StringBuilder.java:405) >>>>>>> at java.io.UnixFileSystem.resolve(UnixFileSystem.java:108) >>>>>>> at java.io.File.<init>(File.java:367) >>>>>>> at >>>>>>> org.apache.spark.storage.DiskBlockManager.getFile(DiskBlockManager.scala:81) >>>>>>> at >>>>>>> org.apache.spark.storage.DiskBlockManager.getFile(DiskBlockManager.scala:84) >>>>>>> at >>>>>>> org.apache.spark.shuffle.IndexShuffleBlockManager.getIndexFile(IndexShuffleBlockManager.scala:60) >>>>>>> at >>>>>>> org.apache.spark.shuffle.IndexShuffleBlockManager.getBlockData(IndexShuffleBlockManager.scala:107) >>>>>>> at >>>>>>> org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:304) >>>>>>> at >>>>>>> org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:57) >>>>>>> at >>>>>>> org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:57) >>>>>>> at >>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >>>>>>> at >>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >>>>>>> at >>>>>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >>>>>>> at >>>>>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) >>>>>>> at >>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244) >>>>>>> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108) >>>>>>> at >>>>>>> org.apache.spark.network.netty.NettyBlockRpcServer.receive(NettyBlockRpcServer.scala:57) >>>>>>> at >>>>>>> org.apache.spark.network.server.TransportRequestHandler.processRpcRequest(TransportRequestHandler.java:124) >>>>>>> at >>>>>>> org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:97) >>>>>>> at >>>>>>> org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:91) >>>>>>> at >>>>>>> org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:44) >>>>>>> at >>>>>>> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105) >>>>>>> at >>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) >>>>>>> at >>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319) >>>>>>> at >>>>>>> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103) >>>>>>> at >>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) >>>>>>> at >>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319) >>>>>>> at >>>>>>> io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:163) >>>>>>> at >>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) >>>>>>> >>>>>>> On Wed, Jun 24, 2015 at 7:16 AM, Akhil Das < >>>>>>> ak...@sigmoidanalytics.com> wrote: >>>>>>> >>>>>>>> Can you look a bit more in the error logs? It could be getting >>>>>>>> killed because of OOM etc. One thing you can try is to set the >>>>>>>> spark.shuffle.blockTransferService to nio from netty. >>>>>>>> >>>>>>>> Thanks >>>>>>>> Best Regards >>>>>>>> >>>>>>>> On Wed, Jun 24, 2015 at 5:46 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com >>>>>>>> > wrote: >>>>>>>> >>>>>>>>> I have a Spark job that has 7 stages. The first 3 stage complete >>>>>>>>> and the fourth stage beings (joins two RDDs). This stage has multiple >>>>>>>>> task >>>>>>>>> failures all the below exception. >>>>>>>>> >>>>>>>>> Multiple tasks (100s) of them get the same exception with >>>>>>>>> different hosts. How can all the host suddenly stop responding when >>>>>>>>> few >>>>>>>>> moments ago 3 stages ran successfully. If I re-run the three stages >>>>>>>>> will >>>>>>>>> again run successfully. I cannot think of it being a cluster issue. >>>>>>>>> >>>>>>>>> >>>>>>>>> Any suggestions ? >>>>>>>>> >>>>>>>>> >>>>>>>>> Spark Version : 1.3.1 >>>>>>>>> >>>>>>>>> Exception: >>>>>>>>> >>>>>>>>> org.apache.spark.shuffle.FetchFailedException: Failed to connect to >>>>>>>>> HOST >>>>>>>>> at >>>>>>>>> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.org$apache$spark$shuffle$hash$BlockStoreShuffleFetcher$$unpackBlock$1(BlockStoreShuffleFetcher.scala:67) >>>>>>>>> at >>>>>>>>> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83) >>>>>>>>> at >>>>>>>>> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83) >>>>>>>>> at >>>>>>>>> scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) >>>>>>>>> at >>>>>>>>> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) >>>>>>>>> at >>>>>>>>> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) >>>>>>>>> at >>>>>>>>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>>>>>>>> at >>>>>>>>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>>>>>>>> at >>>>>>>>> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:125) >>>>>>>>> at org.apache.sp >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Deepak >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Deepak >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Deepak >>>>>> >>>>>> >>>>> >>>>> >>>>> -- >>>>> Deepak >>>>> >>>>> >>>> >>>> >>>> -- >>>> Deepak >>>> >>>> >> >> >> -- >> Deepak >> >> > -- Deepak
