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A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment

Authors:

Donovan A. Schneider,

David J. DeWittAuthors Info & Claims

ACM SIGMOD Record, Volume 18, Issue 2

Pages 110 - 121

https://doi.org/10.1145/66926.66937

Published: 01 June 1989 Publication History

Abstract

In this paper we analyze and compare four parallel join algorithms. Grace and Hybrid hash represent the class of hash-based join methods, Simple hash represents a looping algorithm with hashing, and our last algorithm is the more traditional sort-merge. The performance of each of the algorithms with different tuple distribution policies, the addition of bit vector filters, varying amounts of main-memory for joining, and non-uniformly distributed join attribute values is studied. The Hybrid hash-join algorithm is found to be superior except when the join attribute values of the inner relation are non-uniformly distributed and memory is limited. In this case, a more conservative algorithm such as the sort-merge algorithm should be used. The Gamma database machine serves as the host for the performance comparison.

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Cited By

Metwally AIves ZBonifati AEl Abbadi A(2022)Scaling Equi-JoinsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526042(2163-2176)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526042
Ebenstein RAgrawal G(2019)DistriPlan: an optimized join execution framework for geo-distributed scientific dataDistributed and Parallel Databases10.1007/s10619-019-07264-z38:1(127-152)Online publication date: 23-Mar-2019
https://dl.acm.org/doi/10.1007/s10619-019-07264-z
HONG GKANG SKIM CMIN J(2018)Efficient Parallel Join Processing Exploiting SIMD in Multi-Thread EnvironmentsIEICE Transactions on Information and Systems10.1587/transinf.2017EDP7300E101.D:3(659-667)Online publication date: 2018
https://doi.org/10.1587/transinf.2017EDP7300
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A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment

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In this paper we analyze and compare four parallel join algorithms. Grace and Hybrid hash represent the class of hash-based join methods, Simple hash represents a looping algorithm with hashing, and our last algorithm is the more traditional sort-merge. ...
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Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 18, Issue 2

June 1989

442 pages

ISSN:0163-5808

DOI:10.1145/66926

Issue’s Table of Contents

SIGMOD '89: Proceedings of the 1989 ACM SIGMOD international conference on Management of data
June 1989
451 pages
ISBN:0897913175
DOI:10.1145/67544
Editors:
James Clifford,
Bruce Lindsay,
David Maier

Copyright © 1989 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1989

Published in SIGMOD Volume 18, Issue 2

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Cited By

Metwally AIves ZBonifati AEl Abbadi A(2022)Scaling Equi-JoinsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526042(2163-2176)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526042
Ebenstein RAgrawal G(2019)DistriPlan: an optimized join execution framework for geo-distributed scientific dataDistributed and Parallel Databases10.1007/s10619-019-07264-z38:1(127-152)Online publication date: 23-Mar-2019
https://dl.acm.org/doi/10.1007/s10619-019-07264-z
HONG GKANG SKIM CMIN J(2018)Efficient Parallel Join Processing Exploiting SIMD in Multi-Thread EnvironmentsIEICE Transactions on Information and Systems10.1587/transinf.2017EDP7300E101.D:3(659-667)Online publication date: 2018
https://doi.org/10.1587/transinf.2017EDP7300
Li RRiedewald MDeng XDas GJermaine CBernstein P(2018)Submodularity of Distributed Join ComputationProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3183728(1237-1252)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3183728
Cheng LKotoulas SWard TTheodoropoulos G(2017)Improving the robustness and performance of parallel joins over distributed systemsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2017.06.016109:C(310-323)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.jpdc.2017.06.016
Elseidy MElguindy AVitorovic AKoch C(2014)Scalable and adaptive online joinsProceedings of the VLDB Endowment10.14778/2732279.27322817:6(441-452)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.14778/2732279.2732281
Cheng LKotoulas SWard TTheodoropoulos GLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Robust and Skew-resistant Parallel Joins in Shared-Nothing SystemsProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661888(1399-1408)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661888
Pang HDing X(2014)Privacy-Preserving Ad-Hoc Equi-Join on Outsourced DataACM Transactions on Database Systems10.1145/262950139:3(1-40)Online publication date: 7-Oct-2014
https://dl.acm.org/doi/10.1145/2629501
Thomasian A(2014)Analysis of Fork/Join and Related Queueing SystemsACM Computing Surveys10.1145/262891347:2(1-71)Online publication date: 25-Aug-2014
https://dl.acm.org/doi/10.1145/2628913
Li FOoi BÖzsu MWu S(2014)Distributed data management using MapReduceACM Computing Surveys10.1145/250300946:3(1-42)Online publication date: 1-Jan-2014
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