Queries slower with more mappers

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This topic contains 1 reply, has 2 voices, and was last updated by  Carter Shanklin 1 year, 6 months ago.

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  • #47956

    Bill Smith

    Our platform has a 40GB raw data file that was compressed lzo (12GB compressed) to reduce network IO between S3.
    Without indexing the file is unsplittable resulting in 1 map task and poor cluster utilisation.
    After indexing the file to be splitable the hive query produces 120 map tasks.
    However, with the 120 tasks distributed over a small 4 node cluster it takes longer to process the data than when it wasn’t splitable and processing done by a single node (1h20mins vs 17mins). This was with a fairly simple select from where query, without distinct, group by or order.
    I’d like to utilise all nodes in the cluster, to reduce query time. Whats the best way to have the data crunched in parallel but with fewer mappers?

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  • #47987

    Carter Shanklin


    As a first step I would probably try it against uncompressed text as a reference point.
    Second step I would try it against a splittable format like bzip2 or a read-optimized format like ORCFile (best performance here + high compression). We have heard from some users that ORCFile works well on S3 FWIW.

    I’m not sure what you mean by indexing. Indexing in Hive doesn’t necessarily work the way it works in other systems so this could explain the strange behavior you saw.

    (P.S. c24? Apologies if that makes no sense)

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