Hello Yuhendar,I'll add as much as I can at a high level from what I have
learned so far about map-reduce to answer your questions:
1) The goal behind map-reduce is to perform a distributed computation which
breaks up a large computation intensive problem into smaller chunks and solve
those individual chunks and finally combine the result, the problem in this
case being search, in this problem you have a master node and a set of slave
nodes, the master (or in the hadoop domain I believe its known as the name
node) takes input from the client in the form of a job and forwards this job
out to the slaves which go off and solve smaller pieces of the problem and
return the results. The master then uses a combine approach to gather the
results from all the slaves and present it back to the client. A more
concrete example is the distributed grep problem which is a form of searching
for a particular word (or document) in a huge dataset. Take a look at the
hadoop examples or the hadoop webpage to learn more about this.
2) Google to my understanding is using their internal implementation of the
general algorithm for mapreduce to store data in their datastore known as
bigtable which is a multi-dimensional sorted map.
My 2 cents.Regards.
> Date: Fri, 30 Jul 2010 11:53:49 +0530
> Subject: Re: MapReduce Usage in Search Engines
> From: yuhen...@tce.edu
> To: common-dev@hadoop.apache.org
>
> Hi all,
> I have a basic query regarding Mapreduce usage in search
> engines. My queries are:
>
> 1.How Map-Reduce is used in search?
> 2.Is Google uses Mapreduce algorithm for its search engine? Then how they
> use it? Explain the architecture or flow of how google or other search
> engines work and what is the part of mapreduce in it.....................
>
> Please Explain.........
>
> With Regards,
> B.Yuhendar
>
>
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