Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/52870
Título: | Similarity aware shuffling for the distributed execution of SQL window functions |
Autor(es): | Coelho, Fábio Matos, Miguel Ângelo Marques Pereira, José Oliveira, Rui Carlos Mendes de |
Data: | 2017 |
Editora: | Springer Verlag |
Revista: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Resumo(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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/52870 |
ISBN: | 9783319596648 |
DOI: | 10.1007/978-3-319-59665-5_1 |
ISSN: | 0302-9743 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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dais-shuffle.pdf Acesso restrito! | 811,63 kB | Adobe PDF | Ver/Abrir |