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