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MapReduce >> mail # user >> Java Heap memory error : Limit to 2 Gb of ShuffleRamManager ?


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Re: Java Heap memory error : Limit to 2 Gb of ShuffleRamManager ?
here is the Jira issue,

and the beginning of a patch
https://issues.apache.org/jira/browse/MAPREDUCE-4866
there is indeed a limitation on the byte array size (around Integer.MAX_VALUE)

Maybe we could use BigArrays to overcome this limitation ?

What do you think ?

regards
Olivier

Le 6 déc. 2012 à 19:41, Arun C Murthy a écrit :

> Oliver,
>
>  Sorry, missed this.
>
>  The historical reason, if I remember right, is that we used to have a single byte buffer and hence the limit.
>
>  We should definitely remove it now since we don't use a single buffer. Mind opening a jira?
>
>  http://wiki.apache.org/hadoop/HowToContribute
>
> thanks!
> Arun
>
> On Dec 6, 2012, at 8:01 AM, Olivier Varene - echo wrote:
>
>> anyone ?
>>
>> Début du message réexpédié :
>>
>>> De : Olivier Varene - echo <[EMAIL PROTECTED]>
>>> Objet : ReduceTask > ShuffleRamManager : Java Heap memory error
>>> Date : 4 décembre 2012 09:34:06 HNEC
>>> À : [EMAIL PROTECTED]
>>> Répondre à : [EMAIL PROTECTED]
>>>
>>>
>>> Hi to all,
>>> first many thanks for the quality of the work you are doing : thanks a lot
>>>
>>> I am facing a bug with the memory management at shuffle time, I regularly get
>>>
>>> Map output copy failure : java.lang.OutOfMemoryError: Java heap space
>>> at org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1612)
>>>
>>>
>>> reading the code in org.apache.hadoop.mapred.ReduceTask.java file
>>>
>>> the "ShuffleRamManager" is limiting the maximum of RAM allocation to Integer.MAX_VALUE * maxInMemCopyUse ?
>>>
>>> maxSize = (int)(conf.getInt("mapred.job.reduce.total.mem.bytes",
>>>            (int)Math.min(Runtime.getRuntime().maxMemory(), Integer.MAX_VALUE))
>>>          * maxInMemCopyUse);
>>>
>>> Why is is so ?
>>> And why is it concatened to an Integer as its raw type is long ?
>>>
>>> Does it mean that you can not have a Reduce Task taking advantage of more than 2Gb of memory ?
>>>
>>> To explain a little bit my use case,
>>> I am processing some 2700 maps (each working on 128 MB block of data), and when the reduce phase starts, it sometimes stumbles with java heap memory issues.
>>>
>>> configuration is : java 1.6.0-27
>>> hadoop 0.20.2
>>> -Xmx1400m
>>> io.sort.mb 400
>>> io.sort.factor 25
>>> io.sort.spill.percent 0.80
>>> mapred.job.shuffle.input.buffer.percent 0.70
>>> ShuffleRamManager: MemoryLimit=913466944, MaxSingleShuffleLimit=228366736
>>>
>>> I will decrease
>>> mapred.job.shuffle.input.buffer.percent to limit the errors, but I am not fully confident for the scalability of the process.
>>>
>>> Any help would be welcomed
>>>
>>> once again, many thanks
>>> Olivier
>>>
>>>
>>> P.S: sorry if I misunderstood the code, any explanation would be really welcomed
>>>
>>> --
>>>  
>>>  
>>>  
>>>
>>>
>>
>
> --
> Arun C. Murthy
> Hortonworks Inc.
> http://hortonworks.com/
>
>