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.
Type de document :
Communication dans un congrès
Márk Jelasity; Evangelia Kalyvianaki. 16th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2016, Heraklion, Crete, Greece. Lecture Notes in Computer Science, LNCS-9687, pp.75-81, 2016, Distributed Applications and Interoperable Systems. 〈10.1007/978-3-319-39577-7_6〉
https://hal.inria.fr/hal-01434801
Contributeur : Hal Ifip
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Soumis le : vendredi 13 janvier 2017 - 14:02:46
Dernière modification le : vendredi 13 janvier 2017 - 14:05:48
Document(s) archivé(s) le : vendredi 14 avril 2017 - 20:43:01
Fábio Coelho, José Pereira, Ricardo Vilaça, Rui Oliveira. Holistic Shuffler for the Parallel Processing of SQL Window Functions. Márk Jelasity; Evangelia Kalyvianaki. 16th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2016, Heraklion, Crete, Greece. Lecture Notes in Computer Science, LNCS-9687, pp.75-81, 2016, Distributed Applications and Interoperable Systems. 〈10.1007/978-3-319-39577-7_6〉. 〈hal-01434801〉
