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Random access with a distributed Bitmap Join Index for Star Joins
Indices improve the performance of relational databases, especially on queries that return a small portion of the data (i.e., low-selectivity queries). Star joins are particularly expensive operations that commonly rely on indices for improved performance at scale. The development and support of ind...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7030998/ https://www.ncbi.nlm.nih.gov/pubmed/32099915 http://dx.doi.org/10.1016/j.heliyon.2020.e03342 |
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author | Brito, Jaqueline J. Mosqueiro, Thiago Ciferri, Ricardo R. Ciferri, Cristina D.A. |
author_facet | Brito, Jaqueline J. Mosqueiro, Thiago Ciferri, Ricardo R. Ciferri, Cristina D.A. |
author_sort | Brito, Jaqueline J. |
collection | PubMed |
description | Indices improve the performance of relational databases, especially on queries that return a small portion of the data (i.e., low-selectivity queries). Star joins are particularly expensive operations that commonly rely on indices for improved performance at scale. The development and support of index-based solutions for Star Joins are still at very early stages. To address this gap, we propose a distributed Bitmap Join Index (dBJI) and a framework-agnostic strategy to solve join predicates in linear time. For empirical analysis, we used common Hadoop technologies (e.g., HBase and Spark) to show that dBJI significantly outperforms full scan approaches by a factor between 59% and 88% in queries with low selectivity from the Star Schema Benchmark (SSB). Thus, distributed indices may significantly enhance low-selectivity query performance even in very large databases. |
format | Online Article Text |
id | pubmed-7030998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-70309982020-02-25 Random access with a distributed Bitmap Join Index for Star Joins Brito, Jaqueline J. Mosqueiro, Thiago Ciferri, Ricardo R. Ciferri, Cristina D.A. Heliyon Article Indices improve the performance of relational databases, especially on queries that return a small portion of the data (i.e., low-selectivity queries). Star joins are particularly expensive operations that commonly rely on indices for improved performance at scale. The development and support of index-based solutions for Star Joins are still at very early stages. To address this gap, we propose a distributed Bitmap Join Index (dBJI) and a framework-agnostic strategy to solve join predicates in linear time. For empirical analysis, we used common Hadoop technologies (e.g., HBase and Spark) to show that dBJI significantly outperforms full scan approaches by a factor between 59% and 88% in queries with low selectivity from the Star Schema Benchmark (SSB). Thus, distributed indices may significantly enhance low-selectivity query performance even in very large databases. Elsevier 2020-02-17 /pmc/articles/PMC7030998/ /pubmed/32099915 http://dx.doi.org/10.1016/j.heliyon.2020.e03342 Text en © 2020 The Authors. Published by Elsevier Ltd. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Brito, Jaqueline J. Mosqueiro, Thiago Ciferri, Ricardo R. Ciferri, Cristina D.A. Random access with a distributed Bitmap Join Index for Star Joins |
title | Random access with a distributed Bitmap Join Index for Star Joins |
title_full | Random access with a distributed Bitmap Join Index for Star Joins |
title_fullStr | Random access with a distributed Bitmap Join Index for Star Joins |
title_full_unstemmed | Random access with a distributed Bitmap Join Index for Star Joins |
title_short | Random access with a distributed Bitmap Join Index for Star Joins |
title_sort | random access with a distributed bitmap join index for star joins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7030998/ https://www.ncbi.nlm.nih.gov/pubmed/32099915 http://dx.doi.org/10.1016/j.heliyon.2020.e03342 |
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