PARQO: Penalty-Aware Robust Plan Selection in Query Optimization

TL;DR

PARQO is a system that improves query plan robustness by modeling selectivity estimate errors and selecting plans with minimal expected penalties, leading to more reliable query performance.

Contribution

It introduces a novel approach combining workload-informed error modeling and sensitivity analysis for robust plan selection in query optimization.

Findings

PARQO finds plans with lower expected penalties on benchmarks.

It enables efficient parametric optimization for query plans.

The system improves robustness against selectivity estimate errors.

Abstract

The effectiveness of a query optimizer relies on the accuracy of selectivity estimates. The execution plan generated by the optimizer can be extremely poor in reality due to uncertainty in these estimates. This paper presents PARQO (Penalty-Aware Robust Plan Selection in Query Optimization), a novel system where users can define powerful robustness metrics that assess the expected penalty of a plan with respect to true optimal plans under uncertain selectivity estimates. PARQO uses workload-informed profiling to build error models, and employs principled sensitivity analysis techniques to identify human-interpretable selectivity dimensions with the largest impact on penalty. Experiments on three benchmarks demonstrate that PARQO finds robust, performant plans, and enables efficient and effective parametric optimization.