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From Renato Marroquín Mogrovejo <renatoj.marroq...@gmail.com>
Subject Re: Hadoop Data Sharing
Date Tue, 11 May 2010 20:19:56 GMT
Hi Aaron,

The thing is that I had a data structure that is saved into a vector, and
this vector needs to be available for my MapReduce jobs while iterating. So
would you think it would a good and easy way to serialize this objects? It's
a vector that each node contains another user define data structure. Maybe I
will try to do it first just using files, and see how the throughput goes.
Hey do you know where I can find some examples of serializing objects for
Hadoop to save them into SequenceFiles?
Thanks in advance.

Renato M.


2010/5/11 Aaron Kimball <aaron@cloudera.com>

> Perhaps this is guidance in the area you were hoping for: If your data is
> in
> objects that implement the interface 'Writable', then you can use the
> SequenceFileOutputFormat and SequenceFileInputFormat to store your
> intermediate data in binary form in disk-backed files called SequenceFiles.
> The serialization will happen through the write() and readFields() methods
> of your objects, which will automatically be called by the
> OutputFormat/InputFormat as they move through the system. So your
> subsequent
> MR pass will receive objects back in the same form as they were emitted.
> This is a considerably better idea (from both a throughput and a sanity
> perspective) in a chained MapReduce job.
>
> - Aaron
>
> On Tue, May 11, 2010 at 10:31 AM, Aaron Kimball <aaron@cloudera.com>
> wrote:
>
> > What objects are you referring to? I'm not sure I understand your
> question.
> > - Aaron
> >
> >
> > On Tue, May 11, 2010 at 6:38 AM, Renato Marroquín Mogrovejo <
> > renatoj.marroquin@gmail.com> wrote:
> >
> >> Thanks Aaron! I was thinking the same after doing some reading.
> >> Man what about serialize the objects? Would you think that is a good
> idea?
> >> Thanks again.
> >>
> >> Renato M.
> >>
> >>
> >> 2010/5/5 Aaron Kimball <aaron@cloudera.com>
> >>
> >> > Renato,
> >> >
> >> > In general if you need to perform a multi-pass MapReduce workflow,
> each
> >> > pass
> >> > materializes its output to files. The subsequent pass then reads those
> >> same
> >> > files back in as input. This allows the workflow to start at the last
> >> > "checkpoint" if it gets interrupted. There is no persistent in-memory
> >> > distributed storage feature in Hadoop that would allow a MapReduce job
> >> to
> >> > post results to memory for consumption by a subsequent job.
> >> >
> >> > So you would just read your initial data from /input, and write your
> >> > interim
> >> > results to /iteration0. Then the next pass reads from /iteration0 and
> >> > writes
> >> > to /iteration1, etc..
> >> >
> >> > If your data is reasonably small and you think it could fit in memory
> >> > somewhere, then you could experiment with using other distributed
> >> key-value
> >> > stores (memcached[b], hbase, cassandra, etc..) to hold intermediate
> >> > results.
> >> > But this will require some integration work on your part.
> >> > - Aaron
> >> >
> >> > On Wed, May 5, 2010 at 8:29 AM, Renato Marroquín Mogrovejo <
> >> > renatoj.marroquin@gmail.com> wrote:
> >> >
> >> > > Hi everyone, I have recently started to play around with hadoop, but
> I
> >> am
> >> > > getting some into some "design" problems.
> >> > > I need to make a loop to execute the same job several times, and in
> >> each
> >> > > iteration get the processed values (not using a file because I would
> >> need
> >> > > to
> >> > > read it). I was using an static vector in my main class (the one
> that
> >> > > iterates and executes the job in each iteration) to retrieve those
> >> > values,
> >> > > and it did work while I was using a standalone mode. Now I tried to
> >> test
> >> > it
> >> > > on a pseudo-distributed manner and obviously is not working.
> >> > > Any suggestions, please???
> >> > >
> >> > > Thanks in advance,
> >> > >
> >> > >
> >> > > Renato M.
> >> > >
> >> >
> >>
> >
> >
>

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