research-article

Memory footprint matters: efficient equi-join algorithms for main memory data processing

Authors:

Spyros Blanas and

Jignesh M. PatelAuthors Info & Claims

SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing

October 2013

Article No.: 19, Pages 1 - 16

https://doi.org/10.1145/2523616.2523626

Published: 01 October 2013 Publication History

Abstract

High-performance analytical data processing systems often run on servers with large amounts of main memory. A common operation in such environments is combining data from two or more sources using some "join" algorithm. The focus of this paper is on studying hash-based and sort-based equi-join algorithms when the data sets being joined fully reside in main memory. We only consider a single node setting, which is an important building block for larger high-performance distributed data processing systems. A critical contribution of this work is in pointing out that in addition to query response time, one must also consider the memory footprint of each join algorithm, as it impacts the number of concurrent queries that can be serviced. Memory footprint becomes an important deployment consideration when running analytical data processing services on hardware that is shared by other concurrent services. We also consider the impact of particular physical properties of the input and the output of each join algorithm. This information is essential for optimizing complex query pipelines with multiple joins. Our key contribution is in characterizing the properties of hash-based and sort-based equi-join algorithms, thereby allowing system implementers and query optimizers to make a more informed choice about which join algorithm to use.

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Cited By

Li HJin HZheng LLiao X(2020)ReSQM: Accelerating Database Operations Using ReRAM-Based Content Addressable MemoryIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.301286039:11(4030-4041)Online publication date: Nov-2020
https://doi.org/10.1109/TCAD.2020.3012860
Chen RPrasanna V(2016)Accelerating Equi-Join on a CPU-FPGA Heterogeneous Platform2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)10.1109/FCCM.2016.62(212-219)Online publication date: May-2016
https://doi.org/10.1109/FCCM.2016.62
Yu CBoyd J(2016)FB+-tree for Big Data ManagementBig Data Research10.1016/j.bdr.2015.11.0034:C(25-36)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.bdr.2015.11.003
Show More Cited By

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Memory footprint matters: efficient equi-join algorithms for main memory data processing

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cover image ACM Conferences

SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing

October 2013

427 pages

ISBN:9781450324281

DOI:10.1145/2523616

General Chair:
Guy Lohman

Copyright © 2013 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 the author(s) 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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Published: 01 October 2013

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Microsoft Jim Gray Systems Lab
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SOCC '13: ACM Symposium on Cloud Computing

October 1 - 3, 2013

California, Santa Clara

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SOCC '13 Paper Acceptance Rate 23 of 114 submissions, 20%;

Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

Li HJin HZheng LLiao X(2020)ReSQM: Accelerating Database Operations Using ReRAM-Based Content Addressable MemoryIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.301286039:11(4030-4041)Online publication date: Nov-2020
https://doi.org/10.1109/TCAD.2020.3012860
Chen RPrasanna V(2016)Accelerating Equi-Join on a CPU-FPGA Heterogeneous Platform2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)10.1109/FCCM.2016.62(212-219)Online publication date: May-2016
https://doi.org/10.1109/FCCM.2016.62
Yu CBoyd J(2016)FB+-tree for Big Data ManagementBig Data Research10.1016/j.bdr.2015.11.0034:C(25-36)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.bdr.2015.11.003
Liu FBlanas SGhandeharizadeh SBalazinska MFreedman MBarahmand S(2015)Forecasting the cost of processing multi-join queries via hashing for main-memory databasesProceedings of the Sixth ACM Symposium on Cloud Computing10.1145/2806777.2806944(153-166)Online publication date: 27-Aug-2015
https://dl.acm.org/doi/10.1145/2806777.2806944
Feng ZLo EKao BXu WSellis TDavidson SIves Z(2015)ByteSliceProceedings of the 2015 ACM SIGMOD International Conference on Management of Data10.1145/2723372.2747642(31-46)Online publication date: 27-May-2015
https://dl.acm.org/doi/10.1145/2723372.2747642
Zhang HChen GOoi BTan KZhang M(2015)In-Memory Big Data Management and Processing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.242779527:7(1920-1948)Online publication date: 1-Jul-2015
https://doi.org/10.1109/TKDE.2015.2427795
Sellis TDavidson SIves Z(2015)Proceedings of the 2015 ACM SIGMOD International Conference on Management of DataundefinedOnline publication date: 27-May-2015
Blanas SWu KByna SDong BShoshani ADyreson CLi FÖzsu M(2014)Parallel data analysis directly on scientific file formatsProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2612185(385-396)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2612185

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