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From Amar Kamat <ama...@yahoo-inc.com>
Subject Re: Jobtracker is out of memory with 100,000 dummy map tasks job
Date Fri, 26 Sep 2008 07:41:49 GMT
Amar Kamat wrote:
> Ted Dunning wrote:
>> Why do you try to do 100,000 map tasks?  Also, do you mean that you 
>> had 100
>> nodes, each with 2GB?  If so, that is much too small a machine to try 
>> to run
>> 1000 tasks on.  It is much better to run about the same number of 
>> tasks per
>> machine as you have cores (2-3 in your case).   Then you can easily 
>> split
>> your input into 100,000 pieces which will run in sequence.  For most
>> problems, however, it is better to let the system split your data so 
>> that
>> you get a few tens of seconds of work per split.  It is inefficient 
>> to have
>> very short tasks and it is inconvenient to have long-running tasks.
>>
>> On Thu, Sep 25, 2008 at 11:26 PM, 심탁길 <1004shi@nhncorp.com> wrote:
>>
>>  
>>> Hi all
>>>
>>> Recently I tried 100,000 dummy map tasks job on the 100ea node(2GB, 
>>> Dual
>>> Core, 64Bit machine, Version: 0.16.4) cluster
>>>     
> I assume you are using hadoop-0.16.4. This issue got fixed in 
> hadoop-0.17 where the JT was made a bit more efficient in terms of 
> handling large number of fast finishing maps. See  HADOOP-2119 for 
> more details.
I meant http://issues.apache.org/jira/browse/HADOOP-2119.
Amar
> Amar
>>> Map task does nothing but sleeping one minute
>>>
>>> I found that Jobtracker(1GB Heap) consumes about 650MB of heap 
>>> memory when
>>> the job is 50% done.
>>>
>>> After all, the job failed at the 90% of progress because Jobtracker 
>>> hanged
>>> up(?) due to out of memory.
>>>
>>> how do you handle this kind of issue?
>>>
>>> another related issue:
>>>
>>> while the above job was being processed, I clicked on the "Pending" on
>>> jobdatails.jsp of web UI
>>>
>>> then, Jobtracker consumed 100% of CPU. and 100% CPU status lasted a 
>>> couple
>>> of minutes
>>>
>>>
>>>     
>>
>>
>>   
>


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