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.
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Fábio Coelho, José Pereira, Ricardo Vilaça, Rui Oliveira. Holistic Shuffler for the Parallel Processing of SQL Window Functions. 16th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2016, Heraklion, Crete, Greece. pp.75-81, ⟨10.1007/978-3-319-39577-7_6⟩. ⟨hal-01434801⟩

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