CORADD: Correlation Aware Database Designer for Materialized Views and Indexes
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Madden_CORADD.pdf
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Author(s)
Kimura, Hideaki
Huo, George
Rasin, Alexander
Madden, Samuel R.
Zdonik, Stanley B.
Date Issued
September 2010
Journal
Proceedings of the VLDB Endowment
Publisher
Association for Computing Machinery (ACM)
Citation
Hideaki Kimura, George Huo, Alexander Rasin, Samuel Madden, and Stanley B. Zdonik. 2010. CORADD: correlation aware database designer for materialized views and indexes. Proc. VLDB Endow. 3, 1-2 (September 2010), 1103-1113.
Version
Author's final manuscript
Abstract
We describe an automatic database design tool that exploits correlations between attributes when recommending materialized views (MVs) and indexes. Although there is a substantial body of related work exploring how to select an appropriate set of MVs and indexes for a given workload, none of this work has explored the effect of correlated attributes (e.g., attributes encoding related geographic information) on designs. Our tool identifies a set of MVs and secondary indexes such that correlations between the clustered attributes of the MVs and the secondary indexes are enhanced, which can dramatically improve query performance. It uses a form of Integer Linear Programming (ILP) called ILP Feedback to pick the best set of MVs and indexes for given database size constraints. We compare our tool with a state-of-the-art commercial database designer on two workloads, APB-1 and SSB (Star Schema Benchmark---similar to TPC-H). Our results show that a correlation-aware database designer can improve query performance up to 6 times within the same space budget when compared to a commercial database designer.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.14778/1920841.1920979
