Configuration tuning is critical to database performance, yet automatic database tuning remains challenging due to high-dimensional knob spaces, substantial online tuning cost, unreliable textual hints derived from Large Language Models (LLMs) or community documents, and the difficulty of exploiting the remaining optimization room after initialization. Hence, we propose PLRTune, a staged database tuning system that leverages workload-specific domain knowledge to identify a reduced search space and further optimize within this promising region. First, we develop an importance pre-sampling and reranking strategy to identify the dominant knob subset in a workload-specific manner and derive a compact state representation. Second, we design an execution-guided hint refinement technique to improve the initialization quality of documentation-guided tuning. Finally, we propose a post-tuning refinement stage that leverages Twin Delayed Deep Deterministic Policy Gradient (TD3) to explore the dominant knob subset and further exploit the remaining optimization room. We evaluate PLRTune on MySQL and PostgreSQL across diverse benchmark workloads. Compared with state-of-the-art approaches, PLRTune achieves the best final result on all tested workloads, improving over the corresponding best-performing alternative by 9.50% on average. Moreover, PLRTune reaches the strongest baseline's best performance level 9.03 times faster on average across workloads, demonstrating its practical runtime efficiency without sacrificing final tuning quality.

翻译：配置调优对数据库性能至关重要，但由于存在高维参数空间、高昂的在线调优成本、源自大语言模型或社区文档的不可靠文本提示，以及初始化后剩余优化空间难以充分利用等问题，自动数据库调优仍具挑战性。为此，我们提出PLRTune——一种采用分阶段调优的数据库系统，通过利用工作负载特定领域知识确定精简搜索空间，并在此优化区域内进一步调优。首先，我们设计了重要性预采样与重排序策略，以工作负载特定方式识别主导参数子集，并推导出紧凑状态表示。其次，我们提出执行引导的提示精炼技术，提升文档引导调优的初始化质量。最后，我们提出调优后精炼阶段，利用双延迟深度确定性策略梯度算法探索主导参数子集，进一步挖掘剩余优化空间。我们在MySQL和PostgreSQL上使用多种基准工作负载评估PLRTune。与最新方法相比，PLRTune在所有测试工作负载上均取得最佳终值，较对应最优替代方法平均提升9.50%。此外，PLRTune在各工作负载上达到最强基线最佳性能水平的速度平均快9.03倍，证明其在实际运行时效率不受终值调优质量影响。