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Date: Fri, 25 Jan 2013 00:41:38 +0530
Message-ID: 
 <CALiVDr-_Ajb7dXWyEDqf5VuO7CUt3MSW9414-DLhaq76GRqnxQ@mail.gmail.com>
Subject: Re: Modifying Hadoop For join Operation
From: Vikas Jadhav <vikascjadhav87@gmail.com>
To: user@hadoop.apache.org
Content-Type: multipart/mixed; boundary=20cf303b42199a44c404d40d94c1

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HI Thanks @ Harsh for replying

I am attaching paper called Map-Join-Reduce

I want to implement similar kind of architecture.

Currently MapReduce Proccess join job using Map or reduce Side join

For Reduce Side join job it has drawback

 -->for large datasets there is lot of traffic(data movenment) from
      mapper to reduces(one option We can filter out record using
      Bloloom   Filter like technique)

 FOR THIS I WANT TO PROCESS ALL JOIN IN SINGLE  MAPREDUCE JOB
1) MAP PHASE- processes all datasets and filter out record
2) REDUCE PHASE -
   reduce phase divided in to join and reducer

   join - joins all datasets
   reducer - does aggregation

   for R join S join T
                                    Reduce
mapR
mapS   -----> mapR join mapS => RS   =>RST  --> Reducer(aggrgation)
mapT-------------------------------------->mapT

If you have any idea plze share it.

any other suggestion also we welcome if it reduces completion time for
joining large dataset
thank you

**


On Thu, Jan 24, 2013 at 8:39 PM, Harsh J <harsh@cloudera.com> wrote:

> Hi,
>
> Can you also define 'efficient way' and the idea you have in mind to
> implement that isn't already doable today?
>
> On Thu, Jan 24, 2013 at 6:51 PM, Vikas Jadhav <vikascjadhav87@gmail.com>
> wrote:
> > Anyone has idea about how should i modify Hadoop Code for
> > Performing Join operation in efficient Way.
> > Thanks.
> >
> > --
> >
> >
> > Thanx and Regards
> >  Vikas Jadhav
>
>
>
> --
> Harsh J
>


-- 
*
*
*

Thanx and Regards*
* Vikas Jadhav*

--20cf303b42199a44c004d40d94bf
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<div dir=3D"ltr"><div><div><div dir=3D"ltr"><div>HI Thanks @ Harsh for repl=
ying <br><br></div><div>I am attaching paper called Map-Join-Reduce <br><br=
></div><div>I want to implement similar kind of architecture.<br>
</div><div><br></div><div>Currently MapReduce Proccess join job using Map o=
r reduce Side join<br>
<br></div><div>For Reduce Side join job it has drawback <br><br></div><div>=
=A0--&gt;for large datasets there is lot of traffic(data movenment) from=A0=
=A0=A0=A0=A0=A0 <br>=A0=A0=A0=A0=A0 mapper to reduces(one option We can fil=
ter out record using=A0 <br>


=A0=A0=A0=A0=A0 Bloloom=A0=A0 Filter like technique)<br>=A0<br></div><div>=
=A0FOR THIS I WANT TO PROCESS ALL JOIN IN SINGLE=A0 MAPREDUCE JOB <br></div=
><div>1) MAP PHASE- processes all datasets and filter out record<br></div><=
div>2) REDUCE PHASE - <br>


</div><div>=A0=A0 reduce phase divided in to join and reducer<br><br></div>=
<div>=A0=A0 join - joins all datasets <br></div><div>=A0=A0 reducer - does =
aggregation <br></div><div><br></div><div>=A0=A0 for R join S join T<br></d=
iv><div>=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=
=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 Reduce<br>


</div><div>mapR<br></div><div>mapS=A0=A0 -----&gt; mapR join mapS =3D&gt; R=
S=A0=A0 =3D&gt;RST=A0 --&gt; Reducer(aggrgation)<br></div><div>mapT--------=
------------------------------&gt;mapT<br></div><div><br> </div><div>If you=
 have any idea plze share it.<br>


<br></div><div>any other suggestion also we welcome if it reduces completio=
n time for joining large dataset<br></div><div>thank you<div><div><img src=
=3D"https://mail.google.com/mail/images/cleardot.gif"></div></div></div></d=
iv>

</div></div><table cellpadding=3D"0"><tbody><tr><td><br></td><td><b></b><br=
></td></tr></tbody></table></div><div class=3D"gmail_extra"><br><br><div cl=
ass=3D"gmail_quote">On Thu, Jan 24, 2013 at 8:39 PM, Harsh J <span dir=3D"l=
tr">&lt;<a href=3D"mailto:harsh@cloudera.com" target=3D"_blank">harsh@cloud=
era.com</a>&gt;</span> wrote:<br>
<blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p=
x #ccc solid;padding-left:1ex">Hi,<br>
<br>
Can you also define &#39;efficient way&#39; and the idea you have in mind t=
o<br>
implement that isn&#39;t already doable today?<br>
<div class=3D"HOEnZb"><div class=3D"h5"><br>
On Thu, Jan 24, 2013 at 6:51 PM, Vikas Jadhav &lt;<a href=3D"mailto:vikascj=
adhav87@gmail.com">vikascjadhav87@gmail.com</a>&gt; wrote:<br>
&gt; Anyone has idea about how should i modify Hadoop Code for<br>
&gt; Performing Join operation in efficient Way.<br>
&gt; Thanks.<br>
&gt;<br>
&gt; --<br>
&gt;<br>
&gt;<br>
&gt; Thanx and Regards<br>
&gt; =A0Vikas Jadhav<br>
<br>
<br>
<br>
</div></div><span class=3D"HOEnZb"><font color=3D"#888888">--<br>
Harsh J<br>
</font></span></blockquote></div><br><br clear=3D"all"><br>-- <br><div><b><=
font color=3D"#ff0000"><br></font></b></div><b><font color=3D"#ff0000"><div=
><b><font color=3D"#ff0000"><br></font></b></div>Thanx and Regards</font></=
b><div>
<b><font color=3D"#ff0000">=A0Vikas Jadhav</font></b></div>
</div>

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Content-Type: application/pdf;
	name="MAP-JOIN-REDUCE Toward Scalable and   Journal PAper.pdf"
Content-Disposition: attachment;
	filename="MAP-JOIN-REDUCE Toward Scalable and   Journal PAper.pdf"
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