Proactive Re-optimization with Rio

Abstract

Traditional query optimizers rely on the accuracy of estimated statistics of intermediate subexpressions to choose good query execution plans. This design often leads to suboptimal plan choices for complex queries since errors in estimates grow exponentially in the presence of skewed and correlated data distributions. We propose to demonstrate the <i>Rio</i> prototype database system that uses <i>proactive re-optimization</i> to address the problems with traditional optimizers. Rio supports three new techniques: <ol> <li>Intervals of uncertainty are considered around estimates of statistics during plan enumeration and costing </li> <li>These intervals are used to pick execution plans that are robust to deviations of actual values of statistics from estimated values, or to defer the choice of execution plan until the uncertainty in estimates can be resolved </li> <li> Statistics of intermediate subexpressions are collected quickly, accurately, and efficiently during query execution </li> </ol> These three features are fully functional in the current Rio prototype which is built using the <i>Predator</i> open-source DBMS. In this proposal, we first describe the novel features of Rio, then we use an example query to illustrate the main aspects of our demonstration.