AI 中文总结
针对多组件DBMS调优难题，EvoTune框架通过协作诊断定位高影响子空间，引入效用感知检索策略，将调优反馈组织成层次化内存，无需LLM微调，实验证明其性能优于现有基线，能快速提升查询性能。
AI 中文摘要
现代数据库管理系统（DBMS）有多个可配置组件共同决定查询性能。多组件调优因组合搜索空间大及有限反馈下学习有效调优策略困难而具有挑战性。现有方法依赖盲目搜索和繁重交互的策略学习，导致调优开销高、性能提升有限。基于大语言模型（LLM）的方法虽能知识驱动调优，但无法有效利用在线反馈和历史观察，常过早收敛到次优配置。本文提出EvoTune，一种用于多组件DBMS调优的内存感知进化框架。它通过协作诊断定位特定查询的高影响子空间，结合轻量级模式学习与基于LLM的推理。还引入效用感知检索策略，根据长期性能改进选择信息性观察。为支持持续改进，EvoTune将调优反馈组织成层次化内存，增量细化子空间定位和调优策略，无需LLM微调。大量实验表明，EvoTune始终优于现有基线，在相同调优预算下性能提升高达44.5%，达到最佳竞争基线最终性能的速度快3.9倍。
英文摘要
Modern DBMSs expose multiple configurable components (e.g., knobs, query hints, and indexes) that jointly determine query performance. Multi-component tuning is challenging due to the large combinatorial search space and the difficulty of learning effective tuning policies under limited feedback. Existing approaches still rely on blind search over the configuration space and interaction-heavy policy learning, leading to high tuning overhead and limited performance gains. Recent advances in large language models (LLMs) enable knowledge-driven tuning, but existing LLM-based methods fail to effectively exploit online feedback and historical observations, often converging prematurely to suboptimal configurations. In this paper, we present EvoTune, a memory-aware evolution framework for multi-component DBMS tuning. EvoTune first localizes a query-specific high-impact subspace via collaborative diagnosis, which combines lightweight pattern learning with LLM-based reasoning. It further introduces a utility-aware retrieval policy that selects informative observations based on their resulting long-term performance improvement, instead of similarity-based retrieval. To support continual improvement, EvoTune organizes tuning feedback into a hierarchical memory and incrementally refines both subspace localization and tuning policies without requiring LLM fine-tuning. Extensive experiments show that EvoTune consistently outperforms state-of-the-art baselines, achieving up to 44.5% performance improvement under the same tuning budget and reaching the best competing baseline's final performance up to 3.9X faster.
