Hi Siddhartha,<br> you could use a Hadoop map reduce to solve your problem. <br><br>In Map-reduce, your code will be part of MAP and you can use default reduce .. Ii is easy to use. <br>If you want quicker solution do not use hadoop api but use python and pipes concept.<br>
<br>pls refer to this tutorial<br><a href="http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/">http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/</a><br>
<br><br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
Date: Sat, 21 May 2011 15:14:15 -0700<br>
From: Siddhartha Jonnalagadda <<a href="mailto:sid.kgp@gmail.com">sid.kgp@gmail.com</a>><br>
Subject: [Corpora-List] Simple instructions to scale a java<br>
application?<br>
To: corpora <<a href="mailto:corpora@uib.no">corpora@uib.no</a>><br>
<br>
I have a single threaded java (NLP) application that processes 1000<br>
sentences in 1 hour. I obviously can't wait for 1000 hours to process<br>
million sentences. Are there any simple instructions to make my program run<br>
in 100 servers at a time? This involves migrating the project workspace into<br>
each of them (or create them from a snapshot that contains it) and<br>
concatenate the output that each server produces.<br>
<br>
Any quick pointers, please? I spent couple of hours browsing through Amazon<br>
MapReduce documentation, but that didn't take me as far...<br>
<br>
Since I don't own shares in Amazon, I am open to non-Amazon solutions too.<br>
<br>
Sincerely,<br>
Siddhartha Jonnalagadda,<br>
Text mining Researcher, Lnx Research, LLC, Orange, CA<br>
<a href="http://sjonnalagadda.wordpress.com" target="_blank">sjonnalagadda.wordpress.com</a><br>
<br>
<br>
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</blockquote></div><br><br clear="all"><br>-- <br>Ashish Almeida<br>--<span></span><span></span>-------------------------------<br>