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A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method
Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each w...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176525/ https://www.ncbi.nlm.nih.gov/pubmed/34141897 http://dx.doi.org/10.7717/peerj-cs.580 |
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author | Azhir, Elham Jafari Navimipour, Nima Hosseinzadeh, Mehdi Sharifi, Arash Darwesh, Aso |
author_facet | Azhir, Elham Jafari Navimipour, Nima Hosseinzadeh, Mehdi Sharifi, Arash Darwesh, Aso |
author_sort | Azhir, Elham |
collection | PubMed |
description | Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability. |
format | Online Article Text |
id | pubmed-8176525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81765252021-06-16 A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method Azhir, Elham Jafari Navimipour, Nima Hosseinzadeh, Mehdi Sharifi, Arash Darwesh, Aso PeerJ Comput Sci Algorithms and Analysis of Algorithms Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability. PeerJ Inc. 2021-06-01 /pmc/articles/PMC8176525/ /pubmed/34141897 http://dx.doi.org/10.7717/peerj-cs.580 Text en ©2021 Azhir et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Azhir, Elham Jafari Navimipour, Nima Hosseinzadeh, Mehdi Sharifi, Arash Darwesh, Aso A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method |
title | A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method |
title_full | A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method |
title_fullStr | A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method |
title_full_unstemmed | A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method |
title_short | A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method |
title_sort | technique for parallel query optimization using mapreduce framework and a semantic-based clustering method |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176525/ https://www.ncbi.nlm.nih.gov/pubmed/34141897 http://dx.doi.org/10.7717/peerj-cs.580 |
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