PLRTune: Importance Pre-Sampling and LLM-Guided Reinforcement Learning for Automatic Database Tuning
For database administrators and automated tuning systems, PLRTune addresses the high cost and limited effectiveness of existing tuning methods by combining domain knowledge and reinforcement learning for more efficient and effective configuration optimization.
PLRTune introduces a staged database tuning system that uses workload-specific knob importance pre-sampling and LLM-guided hint refinement to reduce search space and improve initialization, achieving 9.50% better final performance and 9.03x faster convergence than state-of-the-art methods on MySQL and PostgreSQL.
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.