Constrained physical design tuning

Constrained physical design tuning Existing solutions to the automated physical design problem in database systems attempt to minimize execution costs of input workloads for a given storage constraint. In this work, we argue that this model is not flexible enough to address several real-world situations. To overcome this limitation, we introduce a constraint language that is simple yet powerful enough to express many important scenarios. We build upon a previously proposed transformation-based framework to incorporate constraints into the search space. We then show experimentally that we are able to handle a rich class of constraints and that our proposed technique scales gracefully. Our approach generalizes previous work that assumes simpler optimization models where configuration size is the only fixed constraint. As a consequence, the process of tuning a workload not only becomes more flexible, but also more complex, and getting the best design in the first attempt becomes difficult. We propose a paradigm shift for physical design tuning, in which sessions are highly interactive, allowing DBAs to quickly try different options, identify problems, and obtain physical designs in an agile manner. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Constrained physical design tuning

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Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-009-0154-1
Publisher site
See Article on Publisher Site

Abstract

Existing solutions to the automated physical design problem in database systems attempt to minimize execution costs of input workloads for a given storage constraint. In this work, we argue that this model is not flexible enough to address several real-world situations. To overcome this limitation, we introduce a constraint language that is simple yet powerful enough to express many important scenarios. We build upon a previously proposed transformation-based framework to incorporate constraints into the search space. We then show experimentally that we are able to handle a rich class of constraints and that our proposed technique scales gracefully. Our approach generalizes previous work that assumes simpler optimization models where configuration size is the only fixed constraint. As a consequence, the process of tuning a workload not only becomes more flexible, but also more complex, and getting the best design in the first attempt becomes difficult. We propose a paradigm shift for physical design tuning, in which sessions are highly interactive, allowing DBAs to quickly try different options, identify problems, and obtain physical designs in an agile manner.

Journal

The VLDB JournalSpringer Journals

Published: Feb 1, 2010

References

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