The performance of modern DBMSs such as MySQL and PostgreSQL heavily depends on the configuration of performance-critical knobs. Manual tuning these knobs is laborious and inefficient due to the complex and high-dimensional nature of the configuration space. Among the automated tuning methods, reinforcement learning (RL)-based methods have recently sought to improve the DBMS knobs tuning process from several different perspectives. However, they still encounter challenges with slow convergence speed during offline training. In this paper, we mainly focus on how to leverage the valuable tuning hints contained in various textual documents such as DBMS manuals and web forums to improve the offline training of RL-based methods. To this end, we propose an efficient DBMS knobs tuning framework named DemoTuner via a novel LLM-assisted demonstration reinforcement learning method. Specifically, to comprehensively and accurately mine tuning hints from documents, we design a structured chain of thought prompt to employ LLMs to conduct a condition-aware tuning hints extraction task. To effectively integrate the mined tuning hints into RL agent training, we propose a hint-aware demonstration reinforcement learning algorithm HA-DDPGfD in DemoTuner. As far as we know, DemoTuner is the first work to introduce the demonstration reinforcement learning algorithm for DBMS knobs tuning. Experimental evaluations conducted on MySQL and PostgreSQL across various workloads demonstrate that DemoTuner achieves performance gains of up to 44.01% for MySQL and 39.95% for PostgreSQL over default configurations. Compared with three representative baseline methods, DemoTuner is able to further reduce the execution time by up to 10.03%, while always consuming the least online tuning cost. Additionally, DemoTuner also exhibits superior adaptability to application scenarios with unknown workloads.

翻译：现代数据库管理系统（如MySQL和PostgreSQL）的性能高度依赖于关键性能配置参数的设置。由于配置空间具有复杂且高维的特性，手动调整这些参数不仅耗时而且效率低下。在自动化调优方法中，基于强化学习的方法近年来试图从多个不同角度改进DBMS参数调优过程。然而，这些方法在离线训练阶段仍面临收敛速度慢的挑战。本文主要研究如何利用DBMS手册、网络论坛等各种文本文档中包含的宝贵调优提示，以改进基于强化学习方法的离线训练。为此，我们提出了一种名为DemoTuner的高效DBMS参数调优框架，该框架采用了一种新颖的大语言模型辅助演示强化学习方法。具体而言，为全面且准确地从文档中挖掘调优提示，我们设计了一种结构化思维链提示，利用大语言模型执行条件感知的调优提示提取任务。为了将挖掘出的调优提示有效整合到强化学习智能体训练中，我们在DemoTuner中提出了一种提示感知的演示强化学习算法HA-DDPGfD。据我们所知，DemoTuner是首个将演示强化学习算法引入DBMS参数调优的研究工作。在多种工作负载下对MySQL和PostgreSQL进行的实验评估表明，与默认配置相比，DemoTuner为MySQL和PostgreSQL分别实现了高达44.01%和39.95%的性能提升。与三种代表性基线方法相比，DemoTuner能够进一步将执行时间降低高达10.03%，同时始终保持最低的在线调优开销。此外，DemoTuner对未知工作负载的应用场景也展现出卓越的适应性。