RELOAD让数据库查询优化更稳定高效，显著减少计划失误并加快学习速度。
RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems
- 采用强化学习框架，通过改进策略训练提升查询计划稳定性。
- 在标准基准上实现2.4倍更强的鲁棒性与3.1倍更快的收敛效率。
- 适合追求高可靠性的生产级数据库系统部署使用。
近年来，查询优化从传统的规则与代价驱动方法转向机器学习驱动范式。其中，强化学习（RL）因能通过学习查询规划策略优化长期性能而备受关注。然而，现有基于RL的查询优化器在单个查询层面常表现出不稳定，包括严重性能下降，且需长时间训练才能达到专家级代价优化器的计划质量。这些缺陷使其难以在实际中部署，成为其在生产数据库系统中应用的主要障碍。为此，我们提出RELOAD，一种鲁棒且高效的数据库查询优化器。RELOAD聚焦于（i）鲁棒性：最小化查询级别的性能退化，确保执行间行为一致；（ii）效率：加速收敛至专家级计划质量。在Join Order Benchmark、TPC-DS和Star Schema Benchmark等标准基准上的大量实验表明，相较当前最优的基于RL的查询优化技术，RELOAD在鲁棒性上最高提升2.4倍，在效率上提升3.1倍。
原文摘要 · Abstract (English)
Recent advances in query optimization have shifted from traditional rule-based and cost-based techniques towards machine learning-driven approaches. Among these, reinforcement learning (RL) has attracted significant attention due to its ability to optimize long-term performance by learning policies over query planning. However, existing RL-based query optimizers often exhibit unstable performance at the level of individual queries, including severe performance regressions, and require prolonged training to reach the plan quality of expert, cost-based optimizers. These shortcomings make learned query optimizers difficult to deploy in practice and remain a major barrier to their adoption in production database systems. To address these challenges, we present RELOAD, a robust and efficient learned query optimizer for database systems. RELOAD focuses on (i) robustness, by minimizing query-level performance regressions and ensuring consistent optimization behavior across executions, and (ii) efficiency, by accelerating convergence to expert-level plan quality. Through extensive experiments on standard benchmarks, including Join Order Benchmark, TPC-DS, and Star Schema Benchmark, RELOAD demonstrates up to 2.4x higher robustness and 3.1x greater efficiency compared to state-of-the-art RL-based query optimization techniques.
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