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Similarity Aware Shuffling for the Distributed Execution of SQL Window Functions

  • Fábio CoelhoEmail author
  • Miguel Matos
  • José Pereira
  • Rui Oliveira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10320)

Abstract

Window functions are extremely useful and have become increasingly popular, allowing ranking, cumulative sums and other analytic aggregations to be computed over a highly flexible and configurable sliding window. This powerful expressiveness comes naturally at the expense of heavy computational requirements which, so far, have been addressed through optimizations around centralized approaches by works both from the industry and academia. Distribution and parallelization has the potential to improve performance, but introduces several challenges associated with data distribution that may harm data locality. In this paper, we show how data similarity can be employed across partitions during the distributed execution of these operators to improve data co-locality between instances of a Distributed Query Engine and the associated data storage nodes. Our contribution can attain network gains in the average of 3 times and it is expected to scale as the number of instances increase. In the scenario with 8 nodes, we were to able attain bandwidth and time savings of 7.3 times and 2.61 times respectively.

Keywords

Hash Function Window Function Computing Node Storage Node Query Execution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research leading to these results was part-funded by (1) the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014–2020, under grant agreement No. 732051; (1) Project TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020 is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF) and by (1) the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Fábio Coelho
    • 1
    Email author
  • Miguel Matos
    • 2
  • José Pereira
    • 1
  • Rui Oliveira
    • 1
  1. 1.INESC TECUniversidade do MinhoBragaPortugal
  2. 2.INESC-ID/ISTLisboaPortugal

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