Shuffle key is depending on your implementation, I'm not sure if you are familiar with MapReduce, the mapper output is a key-value pair, where the key is the shuffle key for shuffling, Spark is also the same.
2015-05-04 17:31 GMT+08:00 ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>: > Hello Shao, > Can you talk more about shuffle key or point me to APIs that allow me to > change shuffle key. I will try with different keys and see the performance. > > What is the shuffle key by default ? > > On Mon, May 4, 2015 at 2:37 PM, Saisai Shao <sai.sai.s...@gmail.com> > wrote: > >> IMHO If your data or your algorithm is prone to data skew, I think you >> have to fix this from application level, Spark itself cannot overcome this >> problem (if one key has large amount of values), you may change your >> algorithm to choose another shuffle key, somethings like this to avoid >> shuffle on skewed keys. >> >> 2015-05-04 16:41 GMT+08:00 ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>: >> >>> Hello Dean & Others, >>> Thanks for the response. >>> >>> I tried with 100,200, 400, 600 and 1200 repartitions with 100,200,400 >>> and 800 executors. Each time all the tasks of join complete in less than a >>> minute except one and that one tasks runs forever. I have a huge cluster at >>> my disposal. >>> >>> The data for each of 1199 tasks is around 40MB/30k records and for 1 >>> never ending task is 1.5G/98million records. I see that there is data skew >>> among tasks. I had observed this a week earlier and i have no clue on how >>> to fix it and when someone suggested that repartition might make things >>> more parallel, but the problem is still persistent. >>> >>> Please suggest on how to get the task to complete. >>> All i want to do is join two datasets. (dataset1 is in sequence file and >>> dataset2 is in avro format). >>> >>> >>> >>> Ex: >>> Tasks IndexIDAttemptStatusLocality LevelExecutor ID / HostLaunch Time >>> DurationGC TimeShuffle Read Size / RecordsShuffle Spill (Memory)Shuffle >>> Spill (Disk)Errors 0 3771 0 RUNNING PROCESS_LOCAL 114 / host1 2015/05/04 >>> 01:27:44 7.3 min 19 s 1591.2 MB / 98931767 0.0 B 0.0 B 1 3772 0 >>> SUCCESS PROCESS_LOCAL 226 / host2 2015/05/04 01:27:44 28 s 2 s 39.2 >>> MB / 29754 0.0 B 0.0 B 2 3773 0 SUCCESS PROCESS_LOCAL 283 / host3 >>> 2015/05/04 >>> 01:27:44 26 s 2 s 39.0 MB / 29646 0.0 B 0.0 B 5 3776 0 SUCCESS >>> PROCESS_LOCAL 320 / host4 2015/05/04 01:27:44 31 s 3 s 38.8 MB / >>> 29512 0.0 B 0.0 B 4 3775 0 SUCCESS PROCESS_LOCAL 203 / host5 2015/05/04 >>> 01:27:44 41 s 3 s 38.4 MB / 29169 0.0 B 0.0 B 3 3774 0 SUCCESS >>> PROCESS_LOCAL 84 / host6 2015/05/04 01:27:44 24 s 2 s 38.5 MB / 29258 >>> 0.0 B 0.0 B 8 3779 0 SUCCESS PROCESS_LOCAL 309 / host7 2015/05/04 >>> 01:27:44 31 s 4 s 39.5 MB / 30008 0.0 B 0.0 B >>> >>> There are 1200 tasks in total. >>> >>> >>> On Sun, May 3, 2015 at 9:53 PM, Dean Wampler <deanwamp...@gmail.com> >>> wrote: >>> >>>> I don't know the full context of what you're doing, but serialization >>>> errors usually mean you're attempting to serialize something that can't be >>>> serialized, like the SparkContext. Kryo won't help there. >>>> >>>> The arguments to spark-submit you posted previously look good: >>>> >>>> 2) --num-executors 96 --driver-memory 12g --driver-java-options >>>> "-XX:MaxPermSize=10G" --executor-memory 12g --executor-cores 4 >>>> >>>> I suspect you aren't getting the parallelism you need. For >>>> partitioning, if your data is in HDFS and your block size is 128MB, then >>>> you'll get ~195 partitions anyway. If it takes 7 hours to do a join over >>>> 25GB of data, you have some other serious bottleneck. You should examine >>>> the web console and the logs to determine where all the time is going. >>>> Questions you might pursue: >>>> >>>> - How long does each task take to complete? >>>> - How many of those 195 partitions/tasks are processed at the same >>>> time? That is, how many "slots" are available? Maybe you need more >>>> nodes >>>> if the number of slots is too low. Based on your command arguments, you >>>> should be able to process 1/2 of them at a time, unless the cluster is >>>> busy. >>>> - Is the cluster swamped with other work? >>>> - How much data does each task process? Is the data roughly the >>>> same from one task to the next? If not, then you might have serious key >>>> skew? >>>> >>>> You may also need to research the details of how joins are implemented >>>> and some of the common tricks for organizing data to minimize having to >>>> shuffle all N by M records. >>>> >>>> >>>> >>>> Dean Wampler, Ph.D. >>>> Author: Programming Scala, 2nd Edition >>>> <http://shop.oreilly.com/product/0636920033073.do> (O'Reilly) >>>> Typesafe <http://typesafe.com> >>>> @deanwampler <http://twitter.com/deanwampler> >>>> http://polyglotprogramming.com >>>> >>>> On Sun, May 3, 2015 at 11:02 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> >>>> wrote: >>>> >>>>> Hello Deam, >>>>> If I don;t use Kryo serializer i got Serialization error and hence am >>>>> using it. >>>>> If I don';t use partitionBy/reparition then the simply join never >>>>> completed even after 7 hours and infact as next step i need to run it >>>>> against 250G as that is my full dataset size. Someone here suggested to me >>>>> to use repartition. >>>>> >>>>> Assuming reparition is mandatory , how do i decide whats the right >>>>> number ? When i am using 400 i do not get NullPointerException that i >>>>> talked about, which is strange. I never saw that exception against small >>>>> random dataset but see it with 25G and again with 400 partitions , i do >>>>> not >>>>> see it. >>>>> >>>>> >>>>> On Sun, May 3, 2015 at 9:15 PM, Dean Wampler <deanwamp...@gmail.com> >>>>> wrote: >>>>> >>>>>> IMHO, you are trying waaay to hard to optimize work on what is really >>>>>> a small data set. 25G, even 250G, is not that much data, especially if >>>>>> you've spent a month trying to get something to work that should be >>>>>> simple. >>>>>> All these errors are from optimization attempts. >>>>>> >>>>>> Kryo is great, but if it's not working reliably for some reason, then >>>>>> don't use it. Rather than force 200 partitions, let Spark try to figure >>>>>> out >>>>>> a good-enough number. (If you really need to force a partition count, use >>>>>> the repartition method instead, unless you're overriding the >>>>>> partitioner.) >>>>>> >>>>>> So. I recommend that you eliminate all the optimizations: Kryo, >>>>>> partitionBy, etc. Just use the simplest code you can. Make it work first. >>>>>> Then, if it really isn't fast enough, look for actual evidence of >>>>>> bottlenecks and optimize those. >>>>>> >>>>>> >>>>>> >>>>>> Dean Wampler, Ph.D. >>>>>> Author: Programming Scala, 2nd Edition >>>>>> <http://shop.oreilly.com/product/0636920033073.do> (O'Reilly) >>>>>> Typesafe <http://typesafe.com> >>>>>> @deanwampler <http://twitter.com/deanwampler> >>>>>> http://polyglotprogramming.com >>>>>> >>>>>> On Sun, May 3, 2015 at 10:22 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Hello Dean & Others, >>>>>>> Thanks for your suggestions. >>>>>>> I have two data sets and all i want to do is a simple equi join. I >>>>>>> have 10G limit and as my dataset_1 exceeded that it was throwing OOM >>>>>>> error. >>>>>>> Hence i switched back to use .join() API instead of map-side broadcast >>>>>>> join. >>>>>>> I am repartitioning the data with 100,200 and i see a >>>>>>> NullPointerException now. >>>>>>> >>>>>>> When i run against 25G of each input and with .partitionBy(new >>>>>>> org.apache.spark.HashPartitioner(200)) , I see NullPointerExveption >>>>>>> >>>>>>> >>>>>>> this trace does not include a line from my code and hence i do not >>>>>>> what is causing error ? >>>>>>> I do have registered kryo serializer. >>>>>>> >>>>>>> val conf = new SparkConf() >>>>>>> .setAppName(detail) >>>>>>> * .set("spark.serializer", >>>>>>> "org.apache.spark.serializer.KryoSerializer")* >>>>>>> .set("spark.kryoserializer.buffer.mb", >>>>>>> arguments.get("buffersize").get) >>>>>>> .set("spark.kryoserializer.buffer.max.mb", >>>>>>> arguments.get("maxbuffersize").get) >>>>>>> .set("spark.driver.maxResultSize", >>>>>>> arguments.get("maxResultSize").get) >>>>>>> .set("spark.yarn.maxAppAttempts", "0") >>>>>>> * >>>>>>> .registerKryoClasses(Array(classOf[com.ebay.ep.poc.spark.reporting.process.model.dw.SpsLeve* >>>>>>> lMetricSum])) >>>>>>> val sc = new SparkContext(conf) >>>>>>> >>>>>>> I see the exception when this task runs >>>>>>> >>>>>>> val viEvents = details.map { vi => (vi.get(14).asInstanceOf[Long], >>>>>>> vi) } >>>>>>> >>>>>>> Its a simple mapping of input records to (itemId, record) >>>>>>> >>>>>>> I found this >>>>>>> >>>>>>> http://stackoverflow.com/questions/23962796/kryo-readobject-cause-nullpointerexception-with-arraylist >>>>>>> and >>>>>>> >>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Kryo-NPE-with-Array-td19797.html >>>>>>> >>>>>>> Looks like Kryo (2.21v) changed something to stop using default >>>>>>> constructors. >>>>>>> >>>>>>> (Kryo.DefaultInstantiatorStrategy) >>>>>>> kryo.getInstantiatorStrategy()).setFallbackInstantiatorStrategy(new >>>>>>> StdInstantiatorStrategy()); >>>>>>> >>>>>>> >>>>>>> Please suggest >>>>>>> >>>>>>> >>>>>>> Trace: >>>>>>> 15/05/01 03:02:15 ERROR executor.Executor: Exception in task 110.1 >>>>>>> in stage 2.0 (TID 774) >>>>>>> com.esotericsoftware.kryo.KryoException: >>>>>>> java.lang.NullPointerException >>>>>>> Serialization trace: >>>>>>> values (org.apache.avro.generic.GenericData$Record) >>>>>>> datum (org.apache.avro.mapred.AvroKey) >>>>>>> at >>>>>>> com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:626) >>>>>>> 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:41) >>>>>>> at >>>>>>> com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:33) >>>>>>> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo. >>>>>>> java:729)Regards, >>>>>>> >>>>>>> >>>>>>> Any suggestions. >>>>>>> I am not able to get this thing to work over a month now, its kind >>>>>>> of getting frustrating. >>>>>>> >>>>>>> On Sun, May 3, 2015 at 8:03 PM, Dean Wampler <deanwamp...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>>> How big is the data you're returning to the driver with >>>>>>>> collectAsMap? You are probably running out of memory trying to copy too >>>>>>>> much data back to it. >>>>>>>> >>>>>>>> If you're trying to force a map-side join, Spark can do that for >>>>>>>> you in some cases within the regular DataFrame/RDD context. See >>>>>>>> http://spark.apache.org/docs/latest/sql-programming-guide.html#performance-tuning >>>>>>>> and this talk by Michael Armbrust for example, >>>>>>>> http://spark-summit.org/wp-content/uploads/2014/07/Performing-Advanced-Analytics-on-Relational-Data-with-Spark-SQL-Michael-Armbrust.pdf. >>>>>>>> >>>>>>>> >>>>>>>> dean >>>>>>>> >>>>>>>> Dean Wampler, Ph.D. >>>>>>>> Author: Programming Scala, 2nd Edition >>>>>>>> <http://shop.oreilly.com/product/0636920033073.do> (O'Reilly) >>>>>>>> Typesafe <http://typesafe.com> >>>>>>>> @deanwampler <http://twitter.com/deanwampler> >>>>>>>> http://polyglotprogramming.com >>>>>>>> >>>>>>>> On Thu, Apr 30, 2015 at 12:40 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) < >>>>>>>> deepuj...@gmail.com> wrote: >>>>>>>> >>>>>>>>> Full Exception >>>>>>>>> *15/04/30 09:59:49 INFO scheduler.DAGScheduler: Stage 1 >>>>>>>>> (collectAsMap at VISummaryDataProvider.scala:37) failed in 884.087 s* >>>>>>>>> *15/04/30 09:59:49 INFO scheduler.DAGScheduler: Job 0 failed: >>>>>>>>> collectAsMap at VISummaryDataProvider.scala:37, took 1093.418249 s* >>>>>>>>> 15/04/30 09:59:49 ERROR yarn.ApplicationMaster: User class threw >>>>>>>>> exception: Job aborted due to stage failure: Exception while getting >>>>>>>>> task >>>>>>>>> result: org.apache.spark.SparkException: Error sending message >>>>>>>>> [message = >>>>>>>>> GetLocations(taskresult_112)] >>>>>>>>> org.apache.spark.SparkException: Job aborted due to stage failure: >>>>>>>>> Exception while getting task result: org.apache.spark.SparkException: >>>>>>>>> Error >>>>>>>>> sending message [message = GetLocations(taskresult_112)] >>>>>>>>> at org.apache.spark.scheduler.DAGScheduler.org >>>>>>>>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1204) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1193) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1192) >>>>>>>>> at >>>>>>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>>>>>>> at >>>>>>>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1192) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693) >>>>>>>>> at scala.Option.foreach(Option.scala:236) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:693) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1393) >>>>>>>>> at >>>>>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1354) >>>>>>>>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) >>>>>>>>> 15/04/30 09:59:49 INFO yarn.ApplicationMaster: Final app status: >>>>>>>>> FAILED, exitCode: 15, (reason: User class threw exception: Job >>>>>>>>> aborted due >>>>>>>>> to stage failure: Exception while getting task result: >>>>>>>>> org.apache.spark.SparkException: Error sending message [message = >>>>>>>>> GetLocations(taskresult_112)]) >>>>>>>>> >>>>>>>>> >>>>>>>>> *Code at line 37* >>>>>>>>> >>>>>>>>> val lstgItemMap = listings.map { lstg => >>>>>>>>> (lstg.getItemId().toLong, lstg) }.collectAsMap >>>>>>>>> >>>>>>>>> Listing data set size is 26G (10 files) and my driver memory is >>>>>>>>> 12G (I cant go beyond it). The reason i do collectAsMap is to >>>>>>>>> brodcast it >>>>>>>>> and do a map-side join instead of regular join. >>>>>>>>> >>>>>>>>> >>>>>>>>> Please suggest ? >>>>>>>>> >>>>>>>>> >>>>>>>>> On Thu, Apr 30, 2015 at 10:52 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) < >>>>>>>>> deepuj...@gmail.com> wrote: >>>>>>>>> >>>>>>>>>> My Spark Job is failing and i see >>>>>>>>>> >>>>>>>>>> ============================== >>>>>>>>>> >>>>>>>>>> 15/04/30 09:59:49 ERROR yarn.ApplicationMaster: User class threw >>>>>>>>>> exception: Job aborted due to stage failure: Exception while getting >>>>>>>>>> task >>>>>>>>>> result: org.apache.spark.SparkException: Error sending message >>>>>>>>>> [message = >>>>>>>>>> GetLocations(taskresult_112)] >>>>>>>>>> >>>>>>>>>> org.apache.spark.SparkException: Job aborted due to stage >>>>>>>>>> failure: Exception while getting task result: >>>>>>>>>> org.apache.spark.SparkException: Error sending message [message = >>>>>>>>>> GetLocations(taskresult_112)] >>>>>>>>>> >>>>>>>>>> at org.apache.spark.scheduler.DAGScheduler.org >>>>>>>>>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1204) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1193) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1192) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1192) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693) >>>>>>>>>> >>>>>>>>>> at scala.Option.foreach(Option.scala:236) >>>>>>>>>> >>>>>>>>>> at >>>>>>>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:693) >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> java.util.concurrent.TimeoutException: Futures timed out after >>>>>>>>>> [30 seconds] >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> I see multiple of these >>>>>>>>>> >>>>>>>>>> Caused by: java.util.concurrent.TimeoutException: Futures timed >>>>>>>>>> out after [30 seconds] >>>>>>>>>> >>>>>>>>>> And finally i see this >>>>>>>>>> java.lang.OutOfMemoryError: Java heap space >>>>>>>>>> at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57) >>>>>>>>>> at java.nio.ByteBuffer.allocate(ByteBuffer.java:331) >>>>>>>>>> at >>>>>>>>>> org.apache.spark.network.BlockTransferService$$anon$1.onBlockFetchSuccess(BlockTransferService.scala:95) >>>>>>>>>> at >>>>>>>>>> org.apache.spark.network.shuffle.RetryingBlockFetcher$RetryingBlockFetchListener.onBlockFetchSuccess(RetryingBlockFetcher.java:206) >>>>>>>>>> at >>>>>>>>>> org.apache.spark.network.shuffle.OneForOneBlockFetcher$ChunkCallback.onSuccess(OneForOneBlockFetcher.java:72) >>>>>>>>>> at >>>>>>>>>> org.apache.spark.network.client.TransportResponseHandler.handle(TransportResponseHandler.java:124) >>>>>>>>>> at >>>>>>>>>> org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:93) >>>>>>>>>> at >>>>>>>>>> org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:44) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319) >>>>>>>>>> at >>>>>>>>>> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319) >>>>>>>>>> at >>>>>>>>>> io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:163) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:787) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:130) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468) >>>>>>>>>> at >>>>>>>>>> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382) >>>>>>>>>> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354) >>>>>>>>>> at io.netty.util.concurrent.SingleThreadEven >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Solutions >>>>>>>>>> >>>>>>>>>> 1) >>>>>>>>>> >>>>>>>>>> .set("spark.akka.askTimeout", "6000") >>>>>>>>>> >>>>>>>>>> .set("spark.akka.timeout", "6000") >>>>>>>>>> >>>>>>>>>> .set("spark.worker.timeout", "6000") >>>>>>>>>> >>>>>>>>>> 2) --num-executors 96 --driver-memory 12g --driver-java-options >>>>>>>>>> "-XX:MaxPermSize=10G" --executor-memory 12g --executor-cores 4 >>>>>>>>>> >>>>>>>>>> 12G is the limit imposed by YARN cluster, I cant go beyond this. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> ANY suggestions ? >>>>>>>>>> >>>>>>>>>> Regards, >>>>>>>>>> >>>>>>>>>> Deepak >>>>>>>>>> >>>>>>>>>> On Thu, Apr 30, 2015 at 6:48 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) < >>>>>>>>>> deepuj...@gmail.com> wrote: >>>>>>>>>> >>>>>>>>>>> Did not work. Same problem. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> On Thu, Apr 30, 2015 at 1:28 PM, Akhil Das < >>>>>>>>>>> ak...@sigmoidanalytics.com> wrote: >>>>>>>>>>> >>>>>>>>>>>> You could try increasing your heap space explicitly. like >>>>>>>>>>>> export _JAVA_OPTIONS="-Xmx10g", its not the correct approach but >>>>>>>>>>>> try. >>>>>>>>>>>> >>>>>>>>>>>> Thanks >>>>>>>>>>>> Best Regards >>>>>>>>>>>> >>>>>>>>>>>> On Tue, Apr 28, 2015 at 10:35 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) < >>>>>>>>>>>> deepuj...@gmail.com> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> I have a SparkApp that runs completes in 45 mins for 5 files >>>>>>>>>>>>> (5*750MB size) and it takes 16 executors to do so. >>>>>>>>>>>>> >>>>>>>>>>>>> I wanted to run it against 10 files of each input type (10*3 >>>>>>>>>>>>> files as there are three inputs that are transformed). [Input1 = >>>>>>>>>>>>> 10*750 MB, >>>>>>>>>>>>> Input2=10*2.5GB, Input3 = 10*1.5G], Hence i used 32 executors. >>>>>>>>>>>>> >>>>>>>>>>>>> I see multiple >>>>>>>>>>>>> 5/04/28 09:23:31 WARN executor.Executor: Issue communicating >>>>>>>>>>>>> with driver in heartbeater >>>>>>>>>>>>> org.apache.spark.SparkException: Error sending message >>>>>>>>>>>>> [message = >>>>>>>>>>>>> Heartbeat(22,[Lscala.Tuple2;@2e4c404a,BlockManagerId(22, >>>>>>>>>>>>> phxaishdc9dn1048.stratus.phx.ebay.com, 39505))] >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:209) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:427) >>>>>>>>>>>>> Caused by: java.util.concurrent.TimeoutException: Futures >>>>>>>>>>>>> timed out after [30 seconds] >>>>>>>>>>>>> at >>>>>>>>>>>>> scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) >>>>>>>>>>>>> at >>>>>>>>>>>>> scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) >>>>>>>>>>>>> at >>>>>>>>>>>>> scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107) >>>>>>>>>>>>> at >>>>>>>>>>>>> scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53) >>>>>>>>>>>>> at scala.concurrent.Await$.result(package.scala:107) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195) >>>>>>>>>>>>> ... 1 more >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> When i searched deeper, i found OOM error. >>>>>>>>>>>>> 15/04/28 09:10:15 INFO storage.BlockManagerMasterActor: >>>>>>>>>>>>> Removing block manager BlockManagerId(17, >>>>>>>>>>>>> phxdpehdc9dn2643.stratus.phx.ebay.com, 36819) >>>>>>>>>>>>> 15/04/28 09:11:26 WARN storage.BlockManagerMasterActor: >>>>>>>>>>>>> Removing BlockManager BlockManagerId(9, >>>>>>>>>>>>> phxaishdc9dn1783.stratus.phx.ebay.com, 48304) with no recent >>>>>>>>>>>>> heart beats: 121200ms exceeds 120000ms >>>>>>>>>>>>> 15/04/28 09:11:26 INFO storage.BlockManagerMasterActor: >>>>>>>>>>>>> Removing block manager BlockManagerId(9, >>>>>>>>>>>>> phxaishdc9dn1783.stratus.phx.ebay.com, 48304) >>>>>>>>>>>>> 15/04/28 09:11:26 ERROR util.Utils: Uncaught exception in >>>>>>>>>>>>> thread task-result-getter-3 >>>>>>>>>>>>> java.lang.OutOfMemoryError: Java heap space >>>>>>>>>>>>> at java.util.Arrays.copyOf(Arrays.java:2245) >>>>>>>>>>>>> at java.util.Arrays.copyOf(Arrays.java:2219) >>>>>>>>>>>>> at java.util.ArrayList.grow(ArrayList.java:242) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.ArrayList.ensureExplicitCapacity(ArrayList.java:216) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.ArrayList.ensureCapacityInternal(ArrayList.java:208) >>>>>>>>>>>>> at java.util.ArrayList.add(ArrayList.java:440) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.util.MapReferenceResolver.nextReadId(MapReferenceResolver.java:33) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readReferenceOrNull(Kryo.java:766) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:727) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read(DefaultArraySerializers.java:338) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read(DefaultArraySerializers.java:293) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.serializer.KryoSerializerInstance.deserialize(KryoSerializer.scala:173) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.DirectTaskResult.value(TaskResult.scala:79) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskSetManager.handleSuccessfulTask(TaskSetManager.scala:621) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskSchedulerImpl.handleSuccessfulTask(TaskSchedulerImpl.scala:379) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:82) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:51) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:51) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1618) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2.run(TaskResultGetter.scala:50) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>>>>>>>>>>> at java.lang.Thread.run(Thread.java:745) >>>>>>>>>>>>> Exception in thread "task-result-getter-3" >>>>>>>>>>>>> java.lang.OutOfMemoryError: Java heap space >>>>>>>>>>>>> at java.util.Arrays.copyOf(Arrays.java:2245) >>>>>>>>>>>>> at java.util.Arrays.copyOf(Arrays.java:2219) >>>>>>>>>>>>> at java.util.ArrayList.grow(ArrayList.java:242) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.ArrayList.ensureExplicitCapacity(ArrayList.java:216) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.ArrayList.ensureCapacityInternal(ArrayList.java:208) >>>>>>>>>>>>> at java.util.ArrayList.add(ArrayList.java:440) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.util.MapReferenceResolver.nextReadId(MapReferenceResolver.java:33) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readReferenceOrNull(Kryo.java:766) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:727) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read(DefaultArraySerializers.java:338) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read(DefaultArraySerializers.java:293) >>>>>>>>>>>>> at >>>>>>>>>>>>> com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.serializer.KryoSerializerInstance.deserialize(KryoSerializer.scala:173) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.DirectTaskResult.value(TaskResult.scala:79) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskSetManager.handleSuccessfulTask(TaskSetManager.scala:621) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskSchedulerImpl.handleSuccessfulTask(TaskSchedulerImpl.scala:379) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:82) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:51) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:51) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1618) >>>>>>>>>>>>> at >>>>>>>>>>>>> org.apache.spark.scheduler.TaskResultGetter$$anon$2.run(TaskResultGetter.scala:50) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>>>>>>>>>>> at >>>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>>>>>>>>>>> at java.lang.Thread.run(Thread.java:745) >>>>>>>>>>>>> >>>>>>>>>>>>> LogType: stdout >>>>>>>>>>>>> LogLength: 96 >>>>>>>>>>>>> Log Contents: >>>>>>>>>>>>> >>>>>>>>>>>>> hdfs://hostName:8020/sys/edw/dw_lstg_item/snapshot/2015/04/28/00/part-r-0000* >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> Spark Command: >>>>>>>>>>>>> >>>>>>>>>>>>> ./bin/spark-submit -v --master yarn-cluster >>>>>>>>>>>>> --driver-class-path >>>>>>>>>>>>> /apache/hadoop/share/hadoop/common/hadoop-common-2.4.1-EBAY-2.jar:/apache/hadoop/lib/hadoop-lzo-0.6.0.jar:/apache/hadoop-2.4.1-2.1.3.0-2-EBAY/share/hadoop/yarn/lib/guava-11.0.2.jar:/apache/hadoop-2.4.1-2.1.3.0-2-EBAY/share/hadoop/hdfs/hadoop-hdfs-2.4.1-EBAY-2.jar >>>>>>>>>>>>> --jars >>>>>>>>>>>>> /apache/hadoop-2.4.1-2.1.3.0-2-EBAY/share/hadoop/hdfs/hadoop-hdfs-2.4.1-EBAY-2.jar,/home/dvasthimal/spark1.3/1.3.1.lib/spark_reporting_dep_only-1.0-SNAPSHOT-jar-with-dependencies.jar >>>>>>>>>>>>> --num-executors 32 --driver-memory 12g --driver-java-options >>>>>>>>>>>>> "-XX:MaxPermSize=8G" --executor-memory 12g --executor-cores 4 >>>>>>>>>>>>> --queue >>>>>>>>>>>>> hdmi-express --class com.ebay.ep.poc.spark.reporting.SparkApp >>>>>>>>>>>>> /home/dvasthimal/spark1.3/1.3.1.lib/spark_reporting-1.0-SNAPSHOT.jar >>>>>>>>>>>>> startDate=2015-04-6 endDate=2015-04-7 >>>>>>>>>>>>> input=/user/dvasthimal/epdatasets_small/exptsession >>>>>>>>>>>>> subcommand=viewItem >>>>>>>>>>>>> output=/user/dvasthimal/epdatasets/viewItem buffersize=128 >>>>>>>>>>>>> maxbuffersize=1068 maxResultSize=200G askTimeout=1200 >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> There is 12G limit on memory that i can use as this Spark is >>>>>>>>>>>>> running over YARN. >>>>>>>>>>>>> >>>>>>>>>>>>> Spark Version: 1.3.1 >>>>>>>>>>>>> Should i increase the number of executors form 32? >>>>>>>>>>>>> -- >>>>>>>>>>>>> Deepak >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> Deepak >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> Deepak >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Deepak >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Deepak >>>>>>> >>>>>>> >>>>>> >>>>> >>>>> >>>>> -- >>>>> Deepak >>>>> >>>>> >>>> >>> >>> >>> -- >>> Deepak >>> >>> >> > > > -- > Deepak > >
