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From Fatih Haltas <fatih.hal...@nyu.edu>
Subject Re: Need help optimizing reducer
Date Tue, 05 Mar 2013 09:59:54 GMT
I mean,
while trying to add newcoming reducer input value to already merged input
values,to construct whole input values of corresponding key value to
reducer, you might be reading every input values(which are output value of
mapper) from beginning to end.


On Tue, Mar 5, 2013 at 1:46 PM, Fatih Haltas <fatih.haltas@nyu.edu> wrote:

> Hi Austin,
>
> I am not sure whether you had  this kind of mistake or not but in any
> case, I would like to state:
> that you might be trying to read whole input values,(corresponding key
> values) to reducer function from beginning to end(which is the output value
> of mapper) while merging them into one output.
>
> If you can send reducer code, you may get more useful replies.
>
>
>
>
> On Tue, Mar 5, 2013 at 1:00 PM, Mahesh Balija <balijamahesh.mca@gmail.com>wrote:
>
>> The reason why the reducer is fast upto 66% is be because of the Sorting
>> and Shuffling phase of the reduce and when the actual task is NOT yet
>> started.
>>
>> The reduce side is divided into 3 phases of 33~% each -> shuffle (fetch
>> data), sort and finally user-code (reduce). That is why your reduce
>> might be faster upto 66%. In order to speed up your program you may either
>> have to have more number of reducers or make your reducer code as optimized
>> as possible.
>>
>> Best,
>> Mahesh Balija,
>> Calsoft Labs.
>>
>>
>> On Tue, Mar 5, 2013 at 1:27 AM, Austin Chungath <austincv@gmail.com>wrote:
>>
>>> Hi all,
>>>
>>> I have 1 reducer and I have around 600 thousand unique keys coming to
>>> it. The total data is only around 30 mb.
>>> My logic doesn't allow me to have more than 1 reducer.
>>> It's taking too long to complete, around 2 hours. (till 66% it's fast
>>> then it slows down/ I don't really think it has started doing anything till
>>> 66% but then why does it show like that?).
>>> Are there any job execution parameters that can help improve reducer
>>> performace?
>>> Any suggestions to improve things when we have to live with just one
>>> reducer?
>>>
>>> thanks,
>>> Austin
>>>
>>
>>
>

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