Modern database management systems (DBMSs) expose hundreds of configuration knobs that critically influence performance. Existing automated tuning methods either adopt a data-driven paradigm, which incurs substantial overhead, or rely on manual-driven heuristics extracted from database documentation, which are often limited and overly generic. Motivated by the fact that the control logic of configuration knobs is inherently encoded in the DBMS source code, we argue that promising tuning strategies can be mined directly from the code, uncovering fine-grained insights grounded in system internals. To this end, we propose SysInsight, a code-driven database tuning system that automatically extracts fine-grained tuning knowledge from DBMS source code to accelerate and stabilize the tuning process. SysInsight combines static code analysis with LLM-based reasoning to identify knob-controlled execution paths and extract semantic tuning insights. These insights are then transformed into quantitative and verifiable tuning rules via association rule mining grounded in tuning observations. During online tuning, system diagnosis is applied to identify critical knobs, which are adjusted under the rule guidance. Evaluations demonstrate that compared to the SOTA baseline, SysInsight converges to the best configuration on average 7.11X faster while achieving a 19.9% performance improvement.

翻译：现代数据库管理系统（DBMS）暴露了数百个对性能有关键影响的配置旋钮。现有的自动调优方法要么采用数据驱动范式（会产生显著开销），要么依赖从数据库文档中提取的手动驱动启发式方法（通常有限且过于通用）。基于配置旋钮的控制逻辑本质上编码在DBMS源代码中的事实，我们认为可以直接从代码中挖掘有前景的调优策略，从而获取源于系统内部的细粒度洞察。为此，我们提出SysInsight，一种代码驱动的数据库调优系统，能够从DBMS源代码中自动提取细粒度调优知识，以加速并稳定调优过程。SysInsight结合静态代码分析与基于LLM的推理，识别旋钮控制的执行路径并提取语义调优洞察。进而通过基于调优观测的关联规则挖掘，将这些洞察转化为可量化验证的调优规则。在线调优时，系统诊断用于识别关键旋钮，并在规则指导下进行调整。评估表明，与最先进基线相比，SysInsight平均快7.11倍收敛至最优配置，同时实现19.9%的性能提升。