# PARQO: Penalty-Aware Robust Plan Selection in Query Optimization

arXiv: https://arxiv.org/abs/2406.01526 · HTML: https://mlsanity.com/paper/2406.01526/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [A Practical Theory of Generalization in Selectivity Learning](https://mlsanity.com/paper/2409.07014/) (2024)
- [InferF: Declarative Factorization of AI/ML Inferences over Joins](https://mlsanity.com/paper/2511.20489/) (2025)

## Abstract (arXiv)

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.