标题： LIMAO：一种终身模块化学习查询优化框架 显示英文标题

标题： LIMAO: A Framework for Lifelong Modular Learned Query Optimization

摘要： 查询优化器对于数据库系统的性能至关重要。最近，许多学习型查询优化器（LQOs）在传统优化器上展示了显著的性能提升。然而，它们大多基于一个有限的假设：静态查询环境。这一限制使它们难以有效处理现实场景中的复杂、动态查询环境。广泛的重新训练会导致众所周知的灾难性遗忘问题，这会随着时间的推移降低LQO的泛化能力。在本文中，我们解决了这一限制，并引入了LIMAO（终身模块化学习查询优化器），这是一个用于计划成本预测终身学习的框架，可以无缝集成到现有的LQOs中。LIMAO利用了一种模块化终身学习技术、一种基于注意力的神经网络组合架构，以及一种高效的训练范式，旨在保留先前知识的同时持续适应新环境。我们在两个LQOs中实现了LIMAO，表明我们的方法对底层引擎具有无关性。实验结果表明，LIMAO显著提高了LQOs的性能，在动态工作负载下，查询执行时间最多提高了40%，执行时间的方差最多减少了60%。通过利用精确且自洽的设计，LIMAO有效地缓解了灾难性遗忘，确保了计划质量随时间的稳定和可靠。与Postgres相比，LIMAO在选定基准测试中实现了高达4倍的速度提升，突显了其在现实查询优化中的实际优势。 显示英文摘要

摘要： Query optimizers are crucial for the performance of database systems. Recently, many learned query optimizers (LQOs) have demonstrated significant performance improvements over traditional optimizers. However, most of them operate under a limited assumption: a static query environment. This limitation prevents them from effectively handling complex, dynamic query environments in real-world scenarios. Extensive retraining can lead to the well-known catastrophic forgetting problem, which reduces the LQO generalizability over time. In this paper, we address this limitation and introduce LIMAO (Lifelong Modular Learned Query Optimizer), a framework for lifelong learning of plan cost prediction that can be seamlessly integrated into existing LQOs. LIMAO leverages a modular lifelong learning technique, an attention-based neural network composition architecture, and an efficient training paradigm designed to retain prior knowledge while continuously adapting to new environments. We implement LIMAO in two LQOs, showing that our approach is agnostic to underlying engines. Experimental results show that LIMAO significantly enhances the performance of LQOs, achieving up to a 40% improvement in query execution time and reducing the variance of execution time by up to 60% under dynamic workloads. By leveraging a precise and self-consistent design, LIMAO effectively mitigates catastrophic forgetting, ensuring stable and reliable plan quality over time. Compared to Postgres, LIMAO achieves up to a 4x speedup on selected benchmarks, highlighting its practical advantages in real-world query optimization.