  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.
