CORADD: correlation aware database designer for materialized views and indexes

  • Authors:
  • Hideaki Kimura;George Huo;Alexander Rasin;Samuel Madden;Stanley B. Zdonik

  • Affiliations:
  • Brown University;Google, Inc.;Brown University;MIT CSAIL;Brown University

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2010

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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.