用AI代理动态调优数据库，比传统文档方法快35.2%
A Case for Agentic Tuning: From Documentation to Action in PostgreSQL
- 将专家调优经验转化为可执行技能，让AI代理自动操作
- 在TPC-C/H测试中性能超越现有方法最高35.2%
- 适合需要持续优化数据库的工程师和研究者
传统系统调优依赖静态文档，仅记录专家结论而忽略推理过程，导致文档随软件演进而过时、难以应对异构负载、忽略参数间依赖。本文提出从静态文档转向动态行动，引入PerfEvolve工具，将专家调优方法转化为可执行技能，使基于大模型的智能体能完成版本一致性验证、负载定制化分析及多参数联合优化。在PostgreSQL上基于TPC-C与TPC-H基准测试评估，PerfEvolve相比最先进文档驱动基线提升高达35.2%。代码已开源：https://github.com/ISCAS-OSLab/PerfEvolve。
原文摘要 · Abstract (English)
Documentation has long guided computer system tuning by distilling expert knowledge into per-parameter recommendations. Yet such guides capture only what experts conclude, discarding how they reason. This fundamental gap manifests in three concrete deficiencies: documentation grows stale as software evolves, fails under heterogeneous workloads, and ignores inter-parameter dependencies. We propose shifting from static documentation to dynamic action for system tuning. We introduce PerfEvolve, which translates expert tuning methodologies into executable skills that equip LLM-based agents to perform version-consistency verification, workload-specific profiling, and multi-parameter joint optimization. Evaluated on PostgreSQL under TPC-C and TPC-H benchmarks, PerfEvolve outperforms state-of-the-art documentation-driven tuning baselines by up to 35.2%. The tool is available at https://github.com/ISCAS-OSLab/PerfEvolve.
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