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MapReduce >> mail # dev >> running Combiner for all the task on the node.


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Re: running Combiner for all the task on the node.
Definitely it will cost some overload. This lead to less intermediate
movement and less time for reduce phase.  This benefit may improve the
overall performance.
*Regards*
*S.Suresh,*
*Research Scholar,*
*Department of Computer Applications,*
*National Institute of Technology,*
*Tiruchirappalli - 620015.*
*+91-9941506562*
On Wed, Jan 2, 2013 at 6:20 PM, Mahesh Balija <[EMAIL PROTECTED]>wrote:

> Continued,
>
> Also one more shuffle and sort phase should occur so that you can
> merge/combine them properly.
> So you should decide whether additional shuffle and sort phase will be
> overhead in contrast with combine per node.
>
> Best,
> Mahesh Balija,
> Calsoft Labs.
>
> On Wed, Jan 2, 2013 at 6:14 PM, Mahesh Balija <[EMAIL PROTECTED]
> >wrote:
>
> > Hi Suresh,
> >
> >                The combiner function will aggregate the data from a
> single
> > map instance. But NOT for all the maps running in a given node.
> >                AFAIK As the maps will be running in the individual child
> > JVMs, still the intermediate data need to be serialized (moved) so that
> > your combiner can aggregate the data at Node level.
> >
> > Best,
> > Mahesh Balija,
> > Calsoft Labs.
> >
> >
> >
> > On Wed, Jan 2, 2013 at 5:53 PM, Suresh S <[EMAIL PROTECTED]> wrote:
> >
> >> Hello,
> >>
> >>       I think, running combiner function at node level (to combine all
> the
> >> map task output of the node) may reduce the intermediate data movement.
> >>
> >>      I don't know this technique is already available or not. Is it
> worth
> >> for working in this direction?
> >> Any suggestions? Thanks in advance.
> >> *Regards*
> >> *S.Suresh,*
> >> *Research Scholar,*
> >> *Department of Computer Applications,*
> >> *National Institute of Technology,*
> >> *Tiruchirappalli - 620015.*
> >> *+91-9941506562*
> >>
> >
> >
>