This is a standard cartesian product pattern. Although I don’t know how it’s
solved in MapReduce, currently I’m working on
cartesian product in Apache Tez
(https://issues.apache.org/jira/browse/TEZ-2104). There you can have
the following computation:
Map1 Map2
\ /
Reducer
where Map1 tasks handle splits (say A1,A2) from file1, Map2 tasks handle
splits(B1, B2) from file2, and Reducer tasks got
all combinations of splits generated by Map1 and Map2 (say A1B1, A1B2, A2B1,
A2B2).
Mapper only read file once, and their outputs are sent to Reducer multiple
times over network. Also, you can control the speed
of processing by controlling number of reducers.
Thanks!
Zhiyuan
> From: Madhav Sharan <[email protected]>
> Subject: Pairwise similarity using map reduce
> Date: August 10, 2016 at 12:25:46 PM PDT
> To: user <[email protected]>
> Cc: "Mattmann, Chris A (3980)" <[email protected]>, "Zimdars,
> Paul A (398C-Affiliate)" <[email protected]>, [email protected]
>
> Hi hadoop users,
>
> I have a set of vectors stored in .txt files on HDFS. Goal is to take every
> pair of vector and compute similarity between them.
> • We generate pairs of vectors by a python script and give it as a
> input to MR jobs. Input file has comma separated path to vector files.
> "/path/to/vec1, path/to/vec2" .
> • Then mapper tasks gets (Path1, Path2) and computes similarity.
> To do this Mapper reads file at Path1 using HDFS API, reads File at Path2
> using HDFS API. So, each file is read many many times due to the pairwise
> calculation.
>
> I am trying find a way so that I read file only once and my mapper jobs
> receive contents of file rather than file path.
>
> Can someone please share any technique they have used in past that might help?
>
> Thanks
> --
> Madhav Sharan
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