Nicolas Bruno and Surajit Chaudhuri
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 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.
|Published in||VLDB Journal|
|Publisher||Very Large Data Bases Endowment Inc.|
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