Yaoyang Hou

Xinyue Yang, Chen Zheng, Yaoyang Hou et al.

Configuration tuning is critical for database performance. Although recent advancements in database tuning have shown promising results in throughput and latency improvement, challenges remain. First, the vast knob space makes direct optimization unstable and slow to converge. Second, reinforcement learning pipelines often lack effective warm-start guidance and require long offline training. Third, transferability is limited: when hardware or workloads change, existing models typically require substantial retraining to recover performance. To address these limitations, we propose L2T-Tune, a new LLM-guided hybrid database tuning framework that features a three-stage pipeline: Stage one performs a warm start that simultaneously generates uniform samples across the knob space and logs them into a shared pool; Stage two leverages a large language model to mine and prioritize tuning hints from manuals and community documents for rapid convergence. Stage three uses the warm-start sample pool to reduce the dimensionality of knobs and state features, then fine-tunes the configuration with the Twin Delayed Deep Deterministic Policy Gradient algorithm. We conduct experiments on L2T-Tune and the state-of-the-art models. Compared with the best-performing alternative, our approach improves performance by an average of 37.1% across all workloads, and by up to 73% on TPC-C. Compared with models trained with reinforcement learning, it achieves rapid convergence in the offline tuning stage on a single server. Moreover, during the online tuning stage, it only takes 30 steps to achieve best results.

Xinyue Yang, Chen Zheng, Yaoyang Hou et al.

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.