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Parametric Query Optimization (PQO) is an important problem in database systems, yet existing approaches suffer from high training costs, sensitivity to estimation errors, and vulnerability to severe performance regressions. This paper introduces PAR²QO (PARametric Penalty-Aware Robust Query Optimization), a system that integrates robust query optimization into PQO. PAR²QO strategically obtains plans from a well-balanced set of probe locations informed by the workload, and caches them as plan-penalty profiles. At runtime, PAR²QO selects the plan with the lowest expected penalty, explicitly accounting for selectivity uncertainties. Extensive experiments show that PAR²QO delivers significant speedups over existing methods while ensuring robustness against performance degradation. Additionally, we introduceCARVER, a workload generator aimed at covering possible cardinalities of subqueries. Not only does CARVER provide a more comprehensive way to evaluate PQO methods, but when used for training learned methods, it can also enhance their generalizability and stability. 

Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduceHint-QPT(Hints for RobustQueryPerformanceTuning), an interactive tool designed to help users diagnose and improve query performance. Hint-QPT proactively recommends robust plans that are resilient to uncertainty in selectivity estimates, identifies sensitive subqueries for which selectivity estimation errors greatly affect plan quality, and provides intuitive interfaces for targeted selectivity adjustments. Users can either choose the recommended robust plans for execution, or acquire additional statistics on the identified sensitive subqueries to tune query performance. Moreover, Hint-QPT visualizes the alternative execution plans and their costs under uncertainty, helping users to better understand their robustness.