One dataset (RDD Pair)
val lstgItem = listings.map { lstg => (lstg.getItemId().toLong, lstg) }
Second Dataset (RDDPair)
val viEvents = viEventsRaw.map { vi => (vi.get(14).asInstanceOf[Long], vi) }
As i want to join based on item Id that is used as first element in the
tuple in both cases and i think thats what is shuffle key.
listings ==> Data set contains all the unique item ids that are ever listed
on the ecommerce site.
viEvents ===> List of items viewed by user in last day. This will always be
a subset of the total set.
So i do not understand what is data skewness. When my long running task is
working on 1591.2 MB / 98,931,767 does that mean 98 million reocrds contain
all the same item ID ? How can millions of user look at the same item in
last day ?
Or does this dataset contain records across item ids ?
Regards,
Deepak
On Mon, May 4, 2015 at 3:08 PM, Saisai Shao <sai.sai.s...@gmail.com> wrote:
> 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
>>
>>
>
--
Deepak
