Proactive Re-optimization

Authors

Shivnath Babu
Pedro Bizarro
David DeWitt

Abstract

Traditional query optimizers rely on the accuracy of estimated
statistics to choose good execution plans. This design often leads to
suboptimal plan choices for complex queries, since errors in
estimates for intermediate subexpressions grow exponentially in the
presence of skewed and correlated data distributions. Reoptimization
is a promising technique to cope with such mistakes.
Current re-optimizers first use a traditional optimizer to pick a
plan, and then react to estimation errors and resulting
suboptimalities detected in the plan during execution. The
effectiveness of this approach is limited because traditional
optimizers choose plans unaware of issues affecting reoptimization.
We address this problem using proactive reoptimization,
a new approach that incorporates three techniques:
i) the uncertainty in estimates of statistics is computed in the form of
bounding boxes around these estimates, ii) these bounding boxes are
used to pick plans that are robust to deviations of actual values from
their estimates, and iii) accurate measurements of statistics are
collected quickly and efficiently during query execution. We present
an extensive evaluation of these techniques using a prototype
proactive re-optimizer named Rio. In our experiments Rio
outperforms current re-optimizers by up to a factor of three.

Proactive Re-optimization

Authors

Abstract

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