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Holistic Shuffler for the Parallel Processing of SQL Window Functions
Abstract : Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. Currently, systems bypass parallelization opportunities which become especially relevant when considering Big Data as data is naturally partitioned. We present a shuffling technique to improve the parallel execution of window functions when data is naturally partitioned when the query holds a partitioning clause that does not match the natural partitioning of the relation. We evaluated this technique with a non-cumulative ranking function and we were able to reduce data transfer among parallel workers in 85 % when compared to a naive approach.
Cited literature [6 references]
https://hal.inria.fr/hal-01434801
Contributor : Hal Ifip <>
Submitted on : Friday, January 13, 2017 - 2:02:46 PM
Last modification on : Friday, January 13, 2017 - 2:05:48 PM
Long-term archiving on: : Friday, April 14, 2017 - 8:43:01 PM
Contributor : Hal Ifip <>
Submitted on : Friday, January 13, 2017 - 2:02:46 PM
Last modification on : Friday, January 13, 2017 - 2:05:48 PM
Long-term archiving on: : Friday, April 14, 2017 - 8:43:01 PM
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