Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking

Modern analytical workloads, particularly those powering business intelligence dashboards, are dominated by parameterized queries, where a small number of query templates are executed repeatedly with varying predicate values. Classical cost-based optimizers frequently fail to produce efficient execution plans in this setting due to inaccurate cardinality estimation and unstable plan choices. Although numerous learning-based optimizers have been proposed, most are designed for general, ad-hoc query optimization and overlook the repetitive structure of parameterized workloads. As a result, they often incur high training or serving overheads or require substantial modifications to existing database systems, limiting their practical adoption. We propose PLARQ, a practical learned optimizer tailored for Parameterized Query Optimization (PQO). We begin by analyzing existing approaches through a unified two-stage framework: (1) Plan Candidate Generation (PCG), which forms a set of plausible execution plans, and (2) Plan Ranking (PR), which selects the most promising one. This abstraction captures the core design principles of prior work and provides a foundation for systematic comparison. Building on this framework, PLARQ employs a similarity-based PCG approach to retrieve a compact, high-quality set of plan candidates from a precomputed plan pool, and a list-wise, attention-based ranking model to effectively identify the optimal plan among them. PLARQ integrates seamlessly with PostgreSQL without modifying the optimizer internals. Extensive experiments across five benchmarks demonstrate that PLARQ improves end-to-end query performance, achieving speedups of up to 420.31x over PostgreSQL and up to 2x over existing learned methods.