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From bejoy.had...@gmail.com
Subject Re: modifying existing wordcount example
Date Thu, 17 Jan 2013 03:30:26 GMT
Hi Jamal

You can use Distributed Cache only if the file to be distributed is small. Mapreduce should
be dealing with larger datasets so you should expect the output file to get larger.

In simple straight forward manner. You can get the second data set processed then merge the
fist output with second output, you can use KeyValueInputFormat to load the outputs to second
MR job.

Else you can use multple Inputs here and process the new input file into 'word   1' and the
previous output file as 'word  $count' in the mapper and do its aggregation in the reducer.

Regards 
Bejoy KS

Sent from remote device, Please excuse typos

-----Original Message-----
From: jamal sasha <jamalshasha@gmail.com>
Date: Wed, 16 Jan 2013 18:54:04 
To: user@hadoop.apache.org<user@hadoop.apache.org>; <chris@embree.us>
Reply-To: user@hadoop.apache.org
Subject: Re: modifying existing wordcount example

Hi,
 Thanks for giving your thoughts.
I was reading some libraries in hadoop.. and i feel like distributed cache
might help me.
but i picked up hadoop very recently (and along it java as well) and i am
not able to think of how to actually code :(


On Wed, Jan 16, 2013 at 6:13 PM, Chris Embree <cembree@gmail.com> wrote:

> Can you instead copy intput1 and input2 together?
>
> Or process both files on the second pass?
>
> Otherwise, you'll have to read in output file, load the values and start
> your map/red job.
>
> Probably someone else will have a better answer. :)
>
>
> On Wed, Jan 16, 2013 at 9:07 PM, jamal sasha <jamalshasha@gmail.com>wrote:
>
>> Hi,
>>   In the wordcount example:
>> http://hadoop.apache.org/docs/r0.17.0/mapred_tutorial.html
>>  Lets say I run the above example and save the the output.
>> But lets say that I have now a new input file. What I want to do is..
>> basically again do the wordcount but basically modifying the previous
>> counts.
>> For example..
>> sample_input1.txt  //foo bar foo bar bar bar
>> After first run:
>> 1) foo 2
>> 2) bar 4
>>
>> Save it in output1.txt
>>
>> Now sample_input2.txt //bar hello world
>> Now the result I am looking for is:
>> 1)foo 2
>> 2)bar 5
>> 3) hello 1
>> 4) world 1
>>
>> How do i achieve this in map reduce?
>>
>>
>

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