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From Dieter De Witte <drdwi...@gmail.com>
Subject Re: Is it wrong to bypass HDFS?
Date Sun, 09 Nov 2014 19:52:00 GMT
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>:

> 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
> machines.
> 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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