article

The Sort-Merge-Shrink join

Authors:

Christopher Jermaine,

Subramanian Arumugam,

Shantanu Joshi,

Abhijit PolAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 31, Issue 4

Pages 1382 - 1416

https://doi.org/10.1145/1189769.1189775

Published: 01 December 2006 Publication History

Abstract

One of the most common operations in analytic query processing is the application of an aggregate function to the result of a relational join. We describe an algorithm called the Sort-Merge-Shrink (SMS) Join for computing the answer to such a query over large, disk-based input tables. The key innovation of the SMS join is that if the input data are clustered in a statistically random fashion on disk, then at all times, the join provides an online, statistical estimator for the eventual answer to the query as well as probabilistic confidence bounds. Thus, a user can monitor the progress of the join throughout its execution and stop the join when satisfied with the estimate's accuracy or run the algorithm to completion with a total time requirement that is not much longer than that of other common join algorithms. This contrasts with other online join algorithms, which either do not offer such statistical guarantees or can only offer guarantees so long as the input data can fit into main memory.

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Published In

cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 31, Issue 4

December 2006

357 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/1189769

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Copyright © 2006 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2006

Published in TODS Volume 31, Issue 4

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Mei HChen WWei YHu YZhou SLin BZhao YXia J(2020)RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual QueryIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293480026:1(1161-1171)Online publication date: Jan-2020
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Li YWen YYuan X(2018)Online Aggregation: A ReviewWeb Information Systems and Applications10.1007/978-3-030-02934-0_10(103-114)Online publication date: 14-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-02934-0_10
Kamat NNandi AChoudhary AWu KDong B(2017)A Unified Correlation-based Approach to Sampling Over JoinsProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085524(1-12)Online publication date: 27-Jun-2017
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Cheng YZhao WRusu FChoudhary AWu KDong B(2017)Bi-Level Online Aggregation on Raw DataProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085514(1-12)Online publication date: 27-Jun-2017
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