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Hadoop >> mail # user >> Distributed table processing is slower that local table processing


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Re: Distributed table processing is slower that local table processing
Hi Alexander,

If you can provide more details of the stuff you are doing then it would be
helpful. Are you sure that your cluster is running in distributed mode? Did
you ran the job with 1 node in cluster and then added 2 additional node to
the same cluster?

Thanks,
Anil

2012/3/30 Alexander Goryunov <[EMAIL PROTECTED]>

>  Hi Anil,
>
> Yes, the second table is distributed, the first is not and I have 3х better
> results for nondistrubuted table.
>
> I use distributed hadoop mode for all cases.
>
> Thanks.
>
>
>
> On Fri, Mar 30, 2012 at 3:26 AM, anil gupta <[EMAIL PROTECTED]> wrote:
>
> > Hi Alexander,
> >
> > Is data properly distributed over the cluster in Distributed Mode? If the
> > data is not then you wont get good results in distributed mode.
> >
> > Thanks,
> > Anil Gupta
> >
> > On Thu, Mar 29, 2012 at 8:37 AM, Alexander Goryunov <
> [EMAIL PROTECTED]
> > >wrote:
> >
> > > Hello,
> > >
> > > I'm running 3 data node cluster (8core Xeon, 16G) + 1 node for
> jobtracker
> > > and namenode with Hadoop and HBase and have strange performance
> results.
> > >
> > > The same map job runs with speed about 300 000 records per second for 1
> > > node table and 100 000 records per second for table  distributed to 3
> > > nodes.
> > >
> > > Scan caching is 1000, each row is about 0.2K, compression is off,
> > > setCacheBlock is false.
> > >
> > > 7 map tasks in parallel for each node. (281 for the big table in
> summary
> > > and 16 for the small table)
> > >
> > > Map job reads some sequential data and writes down a few from it. No
> > reduce
> > > tasks are set for this job.
> > >
> > >
> > > Both table have the same data and have sizes about 10M (first one)
> > records
> > > and 150M (second one) records.
> > >
> > > Do you have any idea what could be the reason of such behavior?
> > >
> > > Thanks.
> > >
> >
> >
> >
> > --
> > Thanks & Regards,
> > Anil Gupta
> >
>

--
Thanks & Regards,
Anil Gupta