Databases are fundamental to contemporary information systems, yet traditional rule-based configuration methods struggle to manage the complexity of real-world applications with hundreds of tunable parameters. Deep reinforcement learning (DRL), which combines perception and decision-making, presents a potential solution for intelligent database configuration tuning. However, due to black-box property of RL-based method, the generated database tuning strategies still face the urgent problem of lack explainability. Besides, the redundant parameters in large scale database always make the strategy learning become unstable. This paper proposes KnobTree, an interpertable framework designed for the optimization of database parameter configuration. In this framework, an interpertable database tuning algorithm based on RL-based differentatial tree is proposed, which building a transparent tree-based model to generate explainable database tuning strategies. To address the problem of large-scale parameters, We also introduce a explainable method for parameter importance assessment, by utilizing Shapley Values to identify parameters that have significant impacts on database performance. Experiments conducted on MySQL and Gbase8s databases have verified exceptional transparency and interpretability of the KnobTree model. The good property makes generated strategies can offer practical guidance to algorithm designers and database administrators. Moreover, our approach also slightly outperforms the existing RL-based tuning algorithms in aspects such as throughput, latency, and processing time.

翻译：数据库是当代信息系统的基石，然而基于规则的传统配置方法难以应对具有数百个可调参数的实际应用复杂性。深度强化学习（DRL）融合了感知与决策能力，为智能数据库配置调优提供了潜在解决方案。但由于基于强化学习的方法存在黑箱特性，其生成的数据库调优策略仍面临缺乏可解释性的紧迫问题。此外，大规模数据库中的冗余参数常导致策略学习过程不稳定。本文提出KnobTree——一个面向数据库参数配置优化的可解释框架。该框架提出一种基于强化学习差分树的可解释数据库调优算法，通过构建透明的树状模型来生成可解释的数据库调优策略。针对大规模参数问题，我们同时引入基于Shapley值的参数重要性评估方法，以识别对数据库性能具有显著影响的参数。在MySQL和Gbase8s数据库上进行的实验验证了KnobTree模型卓越的透明性与可解释性。这一优良特性使得生成策略能为算法设计者与数据库管理员提供实际指导。此外，本方法在吞吐量、延迟和处理时间等指标上也略优于现有基于强化学习的调优算法。