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
