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.

翻译：查询优化器的有效性依赖于选择性估计的准确性。由于这些估计存在不确定性，优化器生成的执行计划在实际中可能表现极差。本文提出了PARQO（查询优化中基于惩罚感知的鲁棒性计划选择），这是一个新颖的系统，用户可以在其中定义强大的鲁棒性度量标准，用于评估在不确定的选择性估计下，某个计划相对于真实最优计划的预期惩罚。PARQO利用工作负载信息分析构建误差模型，并采用基于原理的敏感性分析技术，以识别对惩罚影响最大、且易于人类理解的选择性维度。在三个基准测试上的实验表明，PARQO能够找到鲁棒且高性能的执行计划，并能实现高效、有效的参数化优化。