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From Trevor Harmon <tre...@vocaro.com>
Subject Re: Is it wrong to bypass HDFS?
Date Sun, 09 Nov 2014 21:41:22 GMT
You’re right, 100MB is small, but if there are 100,000 jobs, the overhead of copying data
to HDFS adds up. I guess my main concern was whether allowing mappers to fetch the input data
would violate some technical rule or map-reduce principle.

I have considered alternative solutions like OpenMP, but the Hadoop ecosystem seems richer
and better supported among cloud providers such as Heroku and AWS.


> On Nov 9, 2014, at 11:52 AM, Dieter De Witte <drdwitte@gmail.com> wrote:
> 100MB is very small, so the overhead of putting the data in hdfs is also very small.
Does it even make sense to optimize this? (reading/writing will only take a second or so)
If you don't want to stream data to hdfs and you have very little data then you should look
in to alternative high performance paradigms such as OpenMP or MPI I think..
> Regards, D
> 2014-11-09 18:16 GMT+01:00 Trevor Harmon <trevor@vocaro.com <mailto:trevor@vocaro.com>>:
> Hi,
> I’m trying to model an "embarrassingly parallel" problem as a map-reduce job. The amount
of data is small -- about 100MB per job, and about 0.25MB per work item -- but the reduce
phase is very CPU-intensive, requiring about 30 seconds to reduce each mapper's output to
a single value. The goal is to speed up the computation by distributing the tasks across many
> I am not sure how the mappers would work in this scenario. My initial thought was that
there would be one mapper per reducer, and each mapper would fetch its input directly from
the source database, using an input key provided by Hadoop. (Remember it’s only about 0.25MB
per work item.) It would then do some necessary fix-up and massaging of the data to prepare
it for the reduction phase.
> However, none of the tutorials and example code I’ve seen do it this way. They always
copy the data from the source database to HDFS first. For my use case, this seems wasteful.
The data per task is very small and can fit entirely in the mapper’s and reducer’s main
memory, so I don’t need “big data” redundant storage. Also, the data is read only once
per task, so there’s nothing to be gained by the data locality optimizations of HDFS. Having
to copy the data to an intermediate data store seems unnecessary and just adds overhead in
this case.
> Is it okay to bypass HDFS for certain types of problems, such as this one? Or is there
some reason mappers should never perform external I/O? I am very new to Hadoop so I don’t
have much experience to go on here. Thank you,
> Trevor

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