Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/52870

TitleSimilarity aware shuffling for the distributed execution of SQL window functions
Author(s)Coelho, Fábio
Matos, Miguel Ângelo Marques
Pereira, José
Oliveira, Rui Carlos Mendes de
Issue date2017
PublisherSpringer Verlag
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/52870
ISBN9783319596648
DOI10.1007/978-3-319-59665-5_1
ISSN0302-9743
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:HASLab - Artigos em atas de conferências internacionais (texto completo)

Files in This Item:
File Description SizeFormat 
dais-shuffle.pdf
  Restricted access
811,63 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID