基于检索增强语言模型的数据库配置自动调试
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models
AI总结：
针对数据库配置调试繁琐困难的问题，提出基于检索增强大语言模型的Andromeda框架，通过多源领域上下文检索与异构文档、遥测分析机制，实现自动配置调试，性能显著优于现有方案。
摘要：
数据库管理系统（DBMS）配置调试，例如诊断配置不当的DBMS调节旋钮并生成故障排除建议，对于优化DBMS性能至关重要。然而，即使是拥有丰富DBMS配置经验、深入理解DBMS内部机制（如MySQL或Oracle）的资深数据库管理员（DBA），配置调试过程也十分繁琐，有时还颇具挑战性。为解决这一难题，我们提出Andromeda框架，该框架利用大语言模型（LLM）实现DBMS配置的自动调试。Andromeda可作为DBA的天然替代者，解答各类关于DBMS配置问题的自然语言（NL）提问，并生成修复这些问题的诊断建议。不过，直接向LLM提示这些专业问题可能会得到过于笼统、往往不尽人意的答案。为此，我们提出一种检索增强生成（RAG）策略，可从多个来源为问题有效提供匹配的领域特定上下文，这些来源包括相关历史问题、故障排除手册以及DBMS遥测数据，显著提升了配置调试的性能。为支撑该RAG策略，我们开发了一种适配异构文档的文档检索机制，并设计了一种有效的遥测数据分析方法。在真实世界DBMS配置调试数据集上开展的大量实验表明，Andromeda的性能显著优于现有解决方案。
英文摘要：
Database management system (DBMS) configuration debugging, e.g., diagnosing poorly configured DBMS knobs and generating troubleshooting recommendations, is crucial in optimizing DBMS performance. However, the configuration debugging process is tedious and, sometimes challenging, even for seasoned database administrators (DBAs) with sufficient experience in DBMS configurations and good understandings of the DBMS internals (e.g., MySQL or Oracle). To address this difficulty, we propose Andromeda, a framework that utilizes large language models (LLMs) to enable automatic DBMS configuration debugging. Andromeda serves as a natural surrogate of DBAs to answer a wide range of natural language (NL) questions on DBMS configuration issues, and to generate diagnostic suggestions to fix these issues. Nevertheless, directly prompting LLMs with these professional questions may result in overly generic and often unsatisfying answers. To this end, we propose a retrieval-augmented generation (RAG) strategy that effectively provides matched domain-specific contexts for the question from multiple sources. They come from related historical questions, troubleshooting manuals and DBMS telemetries, which significantly improve the performance of configuration debugging. To support the RAG strategy, we develop a document retrieval mechanism addressing heterogeneous documents and design an effective method for telemetry analysis. Extensive experiments on real-world DBMS configuration debugging datasets show that Andromeda significantly outperforms existing solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
