The effectiveness of a cost-based 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 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 a model of uncertainty in selectivity estimates. PARQO uses workload-informed profiling to build error models, and employs principled sensitivity analysis techniques to identify selectivity dimensions with the largest impact on penalty. Experimental evaluation on three benchmarks demonstrates how PARQO is able to find robust, performant plans, and how it enables efficient and effective parametric optimization.

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