Join and Semijoin Algorithms for a Multiprocessor Database Machine

Author:

Valduriez Patrick1,Gardarin Georges1

Affiliation:

1. INRIA and University of Paris VI

Abstract

This paper presents and analyzes algorithms for computing joins and semijoins of relations in a multiprocessor database machine. First, a model of the multiprocessor architecture is described, incorporating parameters defining I/O, CPU, and message transmission times that permit calculation of the execution times of these algorithms. Then, three join algorithms are presented and compared. It is shown that, for a given configuration, each algorithm has an application domain defined by the characteristics of the operand and result relations. Since a semijoin operator is useful for decreasing I/O and transmission times in a multiprocessor system, we present and compare two equi-semijoin algorithms and one non-equi-semijoin algorithm. The execution times of these algorithms are generally linearly proportional to the size of the operand and result relations, and inversely proportional to the number of processors. We then compare a method which consists of joining two relations to a method whereby one joins their semijoins. Finally, it is shown that the latter method, using semijoins, is generally better. The various algorithms presented are implemented in the SABRE database system; an evaluation model selects the best algorithm for performing a join according to the results presented here. A first version of the SABRE system is currently operational at INRIA.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems

Reference28 articles.

1. Implementing a relational database by means of specialzed hardware

2. Design of a backend processor for a data base machine

3. Concepts and capabilities of a database computer\

4. BERNSTEIN P.A.; AND CHIU D.M. Using semijoins to solve relational queries. BERNSTEIN P.A.; AND CHIU D.M. Using semijoins to solve relational queries.

Cited by 118 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis;Proceedings of the VLDB Endowment;2023-07

2. Bitvector-aware Query Optimization for Decision Support Queries;Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data;2020-06-11

3. Data Search Using Hash Join Query and Nested Join Query;Journal of Physics: Conference Series;2019-11-01

4. Distributed Spatial and Spatio-Temporal Join on Apache Spark;ACM Transactions on Spatial Algorithms and Systems;2019-06-28

5. A Comparative Study of Join Algorithms in Mapreduce;SSRN Electronic Journal;2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3