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
