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From ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>
Subject Re: Spark - Timeout Issues - OutOfMemoryError
Date Sun, 03 May 2015 15:22:44 GMT
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 <deanwampler@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 (๏̯͡๏) <deepujain@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 (๏̯͡๏) <deepujain@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 (๏̯͡๏) <deepujain@gmail.com>
>>> wrote:
>>>
>>>> Did not work. Same problem.
>>>>
>>>>
>>>>
>>>> On Thu, Apr 30, 2015 at 1:28 PM, Akhil Das <akhil@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 (๏̯͡๏) <deepujain@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

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