Scalable Exploration of Physical Database Design

Abstract

Physical database design is critical to the performance of a large-scale DBMS. The corresponding automated design tuning tools need to select the best physical design from a large set of candidate designs quickly. However, for large workloads, evaluating the cost of each query in the workload for every candidate does not scale. To overcome this, we present a novel comparison primitive that only evaluates a fraction of the workload and provides an accurate estimate of the likelihood of selecting correctly. We show how to use this primitive to construct accurate and scalable selection procedures. Furthermore, we address the issue of ensuring that the estimates are conservative, even for highly skewed cost distributions. The proposed techniques are evaluated through a prototype implementation inside a commercial physical design tool.