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From Arun Luthra <arun.lut...@gmail.com>
Subject Re: Problem getting program to run on 15TB input
Date Sun, 01 Mar 2015 17:56:38 GMT
I tried a shorter simper version of the program, with just 1 RDD,
 essentially it is:

sc.textFile(..., N).map().filter().map( blah => (id,
1L)).reduceByKey().saveAsTextFile(...)

Here is a typical GC log trace from one of the yarn container logs:

54.040: [GC [PSYoungGen: 9176064K->28206K(10704896K)]
9176064K->28278K(35171840K), 0.0234420 secs] [Times: user=0.15 sys=0.01,
real=0.02 secs]
77.864: [GC [PSYoungGen: 9204270K->150553K(10704896K)]
9204342K->150641K(35171840K), 0.0423020 secs] [Times: user=0.30 sys=0.26,
real=0.04 secs]
79.485: [GC [PSYoungGen: 9326617K->333519K(10704896K)]
9326705K->333615K(35171840K), 0.0774990 secs] [Times: user=0.35 sys=1.28,
real=0.08 secs]
92.974: [GC [PSYoungGen: 9509583K->193370K(10704896K)]
9509679K->193474K(35171840K), 0.0241590 secs] [Times: user=0.35 sys=0.11,
real=0.02 secs]
114.842: [GC [PSYoungGen: 9369434K->123577K(10704896K)]
9369538K->123689K(35171840K), 0.0201000 secs] [Times: user=0.31 sys=0.00,
real=0.02 secs]
117.277: [GC [PSYoungGen: 9299641K->135459K(11918336K)]
9299753K->135579K(36385280K), 0.0244820 secs] [Times: user=0.19 sys=0.25,
real=0.02 secs]

So ~9GB is getting GC'ed every few seconds. Which seems like a lot.

Question: The filter() is removing 99% of the data. Does this 99% of the
data get GC'ed?

Now, I was able to finally get to reduceByKey() by reducing the number of
executor-cores (to 2), based on suggestions at
http://apache-spark-user-list.1001560.n3.nabble.com/java-lang-OutOfMemoryError-java-lang-OutOfMemoryError-GC-overhead-limit-exceeded-td9036.html
. This makes everything before reduceByKey() run pretty smoothly.

I ran this with more executor-memory and less executors (most important
thing was fewer executor-cores):

--num-executors 150 \
--driver-memory 15g \
--executor-memory 110g \
--executor-cores 32 \

But then, reduceByKey() fails with:

java.lang.OutOfMemoryError: Java heap space




On Sat, Feb 28, 2015 at 12:09 PM, Arun Luthra <arun.luthra@gmail.com> wrote:

> The Spark UI names the line number and name of the operation (repartition
> in this case) that it is performing. Only if this information is wrong
> (just a possibility), could it have started groupByKey already.
>
> I will try to analyze the amount of skew in the data by using reduceByKey
> (or simply countByKey) which is relatively inexpensive. For the purposes of
> this algorithm I can simply log and remove keys with huge counts, before
> doing groupByKey.
>
> On Sat, Feb 28, 2015 at 11:38 AM, Aaron Davidson <ilikerps@gmail.com>
> wrote:
>
>> All stated symptoms are consistent with GC pressure (other nodes timeout
>> trying to connect because of a long stop-the-world), quite possibly due to
>> groupByKey. groupByKey is a very expensive operation as it may bring all
>> the data for a particular partition into memory (in particular, it cannot
>> spill values for a single key, so if you have a single very skewed key you
>> can get behavior like this).
>>
>> On Sat, Feb 28, 2015 at 11:33 AM, Paweł Szulc <paul.szulc@gmail.com>
>> wrote:
>>
>>> But groupbykey will repartition according to numer of keys as I
>>> understand how it works. How do you know that you haven't reached the
>>> groupbykey phase? Are you using a profiler or do yoi base that assumption
>>> only on logs?
>>>
>>> sob., 28 lut 2015, 8:12 PM Arun Luthra użytkownik <arun.luthra@gmail.com>
>>> napisał:
>>>
>>> A correction to my first post:
>>>>
>>>> There is also a repartition right before groupByKey to help avoid
>>>> too-many-open-files error:
>>>>
>>>>
>>>> rdd2.union(rdd1).map(...).filter(...).repartition(15000).groupByKey().map(...).flatMap(...).saveAsTextFile()
>>>>
>>>> On Sat, Feb 28, 2015 at 11:10 AM, Arun Luthra <arun.luthra@gmail.com>
>>>> wrote:
>>>>
>>>>> The job fails before getting to groupByKey.
>>>>>
>>>>> I see a lot of timeout errors in the yarn logs, like:
>>>>>
>>>>> 15/02/28 12:47:16 WARN util.AkkaUtils: Error sending message in 1
>>>>> attempts
>>>>> akka.pattern.AskTimeoutException: Timed out
>>>>>
>>>>> and
>>>>>
>>>>> 15/02/28 12:47:49 WARN util.AkkaUtils: Error sending message in 2
>>>>> attempts
>>>>> java.util.concurrent.TimeoutException: Futures timed out after [30
>>>>> seconds]
>>>>>
>>>>> and some of these are followed by:
>>>>>
>>>>> 15/02/28 12:48:02 ERROR executor.CoarseGrainedExecutorBackend: Driver
>>>>> Disassociated [akka.tcp://sparkExecutor@...] ->
>>>>> [akka.tcp://sparkDriver@...] disassociated! Shutting down.
>>>>> 15/02/28 12:48:02 ERROR executor.Executor: Exception in task 421027.0
>>>>> in stage 1.0 (TID 336601)
>>>>> java.io.FileNotFoundException:
>>>>> ..../hadoop/yarn/local/......../spark-local-20150228123450-3a71/36/shuffle_0_421027_0
>>>>> (No such file or directory)
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Sat, Feb 28, 2015 at 9:33 AM, Paweł Szulc <paul.szulc@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I would first check whether  there is any possibility that after
>>>>>> doing groupbykey one of the groups does not fit in one of the executors'
>>>>>> memory.
>>>>>>
>>>>>> To back up my theory, instead of doing groupbykey + map try
>>>>>> reducebykey + mapvalues.
>>>>>>
>>>>>> Let me know if that helped.
>>>>>>
>>>>>> Pawel Szulc
>>>>>> http://rabbitonweb.com
>>>>>>
>>>>>> sob., 28 lut 2015, 6:22 PM Arun Luthra użytkownik <
>>>>>> arun.luthra@gmail.com> napisał:
>>>>>>
>>>>>> So, actually I am removing the persist for now, because there is
>>>>>>> significant filtering that happens after calling textFile()...
but I will
>>>>>>> keep that option in mind.
>>>>>>>
>>>>>>> I just tried a few different combinations of number of executors,
>>>>>>> executor memory, and more importantly, number of tasks... *all
>>>>>>> three times it failed when approximately 75.1% of the tasks were
completed
>>>>>>> (no matter how many tasks resulted from repartitioning the data
in
>>>>>>> textfile(..., N))*. Surely this is a strong clue to something?
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Fri, Feb 27, 2015 at 1:07 PM, Burak Yavuz <brkyvz@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> Not sure if it can help, but `StorageLevel.MEMORY_AND_DISK_SER`
>>>>>>>> generates many small objects that lead to very long GC time,
causing the
>>>>>>>> executor losts, heartbeat not received, and GC overhead limit
exceeded
>>>>>>>> messages.
>>>>>>>> Could you try using `StorageLevel.MEMORY_AND_DISK` instead?
You
>>>>>>>> can also try `OFF_HEAP` (and use Tachyon).
>>>>>>>>
>>>>>>>> Burak
>>>>>>>>
>>>>>>>> On Fri, Feb 27, 2015 at 11:39 AM, Arun Luthra <
>>>>>>>> arun.luthra@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> My program in pseudocode looks like this:
>>>>>>>>>
>>>>>>>>>     val conf = new SparkConf().setAppName("Test")
>>>>>>>>>       .set("spark.storage.memoryFraction","0.2") // default
0.6
>>>>>>>>>       .set("spark.shuffle.memoryFraction","0.12") //
default 0.2
>>>>>>>>>       .set("spark.shuffle.manager","SORT") // preferred
setting
>>>>>>>>> for optimized joins
>>>>>>>>>       .set("spark.shuffle.consolidateFiles","true") //
helpful for
>>>>>>>>> "too many files open"
>>>>>>>>>       .set("spark.mesos.coarse", "true") // helpful for
>>>>>>>>> MapOutputTracker errors?
>>>>>>>>>       .set("spark.akka.frameSize","500") // helpful when
using
>>>>>>>>> consildateFiles=true
>>>>>>>>>       .set("spark.akka.askTimeout", "30")
>>>>>>>>>       .set("spark.shuffle.compress","false") //
>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787p20811.html
>>>>>>>>>       .set("spark.file.transferTo","false") //
>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787p20811.html
>>>>>>>>>       .set("spark.core.connection.ack.wait.timeout","600")
//
>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787p20811.html
>>>>>>>>>       .set("spark.speculation","true")
>>>>>>>>>       .set("spark.worker.timeout","600") //
>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Heartbeat-exceeds-td3798.html
>>>>>>>>>       .set("spark.akka.timeout","300") //
>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Heartbeat-exceeds-td3798.html
>>>>>>>>>       .set("spark.storage.blockManagerSlaveTimeoutMs","120000")
>>>>>>>>>       .set("spark.driver.maxResultSize","2048") // in
response to
>>>>>>>>> error: Total size of serialized results of 39901 tasks
(1024.0 MB) is
>>>>>>>>> bigger than spark.driver.maxResultSize (1024.0 MB)
>>>>>>>>>       .set("spark.serializer",
>>>>>>>>> "org.apache.spark.serializer.KryoSerializer")
>>>>>>>>>
>>>>>>>>> .set("spark.kryo.registrator","com.att.bdcoe.cip.ooh.MyRegistrator")
>>>>>>>>>       .set("spark.kryo.registrationRequired", "true")
>>>>>>>>>
>>>>>>>>> val rdd1 = sc.textFile(file1).persist(StorageLevel
>>>>>>>>> .MEMORY_AND_DISK_SER).map(_.split("\\|", -1)...filter(...)
>>>>>>>>>
>>>>>>>>> val rdd2 =
>>>>>>>>> sc.textFile(file2).persist(StorageLevel.MEMORY_AND_DISK_SER).map(_.split("\\|",
>>>>>>>>> -1)...filter(...)
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> rdd2.union(rdd1).map(...).filter(...).groupByKey().map(...).flatMap(...).saveAsTextFile()
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I run the code with:
>>>>>>>>>   --num-executors 500 \
>>>>>>>>>   --driver-memory 20g \
>>>>>>>>>   --executor-memory 20g \
>>>>>>>>>   --executor-cores 32 \
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I'm using kryo serialization on everything, including
broadcast
>>>>>>>>> variables.
>>>>>>>>>
>>>>>>>>> Spark creates 145k tasks, and the first stage includes
everything
>>>>>>>>> before groupByKey(). It fails before getting to groupByKey.
I have tried
>>>>>>>>> doubling and tripling the number of partitions when calling
textFile, with
>>>>>>>>> no success.
>>>>>>>>>
>>>>>>>>> Very similar code (trivial changes, to accomodate different
input)
>>>>>>>>> worked on a smaller input (~8TB)... Not that it was easy
to get that
>>>>>>>>> working.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Errors vary, here is what I am getting right now:
>>>>>>>>>
>>>>>>>>> ERROR SendingConnection: Exception while reading SendingConnection
>>>>>>>>> ... java.nio.channels.ClosedChannelException
>>>>>>>>> (^ guessing that is symptom of something else)
>>>>>>>>>
>>>>>>>>> WARN BlockManagerMasterActor: Removing BlockManager
>>>>>>>>> BlockManagerId(...) with no recent heart beats: 120030ms
exceeds 120000ms
>>>>>>>>> (^ guessing that is symptom of something else)
>>>>>>>>>
>>>>>>>>> ERROR ActorSystemImpl: Uncaught fatal error from thread
(...)
>>>>>>>>> shutting down ActorSystem [sparkDriver]
>>>>>>>>> *java.lang.OutOfMemoryError: GC overhead limit exceeded*
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Other times I will get messages about "executor lost..."
about 1
>>>>>>>>> message per second, after ~~50k tasks complete, until
there are almost no
>>>>>>>>> executors left and progress slows to nothing.
>>>>>>>>>
>>>>>>>>> I ran with verbose GC info; I do see failing yarn containers
that
>>>>>>>>> have multiple (like 30) "Full GC" messages but I don't
know how to
>>>>>>>>> interpret if that is the problem. Typical Full GC time
taken seems
>>>>>>>>> ok: [Times: user=23.30 sys=0.06, real=1.94 secs]
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Suggestions, please?
>>>>>>>>>
>>>>>>>>> Huge thanks for useful suggestions,
>>>>>>>>> Arun
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>
>

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