Database administrators (DBAs) play an important role in managing,
maintaining and optimizing database systems. However, it is hard and tedious
for DBAs to manage a large number of databases and give timely response
(waiting for hours is intolerable in many online cases). In addition, existing
empirical methods only support limited diagnosis scenarios, which are also
labor-intensive to update the diagnosis rules for database version updates.
Recently large language models (LLMs) have shown great potential in various
fields. Thus, we propose D-Bot, an LLM-based database diagnosis system that can
automatically acquire knowledge from diagnosis documents, and generate
reasonable and well-founded diagnosis report (i.e., identifying the root causes
and solutions) within acceptable time (e.g., under 10 minutes compared to hours
by a DBA). The techniques in D-Bot include (i) offline knowledge extraction
from documents, (ii) automatic prompt generation (e.g., knowledge matching,
tool retrieval), (iii) root cause analysis using tree search algorithm, and
(iv) collaborative mechanism for complex anomalies with multiple root causes.
We verify D-Bot on real benchmarks (including 539 anomalies of six typical
applications), and the results show that D-Bot can effectively analyze the root
causes of unseen anomalies and significantly outperforms traditional methods
and vanilla models like GPT-4.

D-Bot 是基于大型语言模型的数据库诊断系统，能够从诊断文档中自动获取知识，并在可接受的时间范围内生成合理和有根据的诊断报告，包括确定根本原因和解决方案。通过在真实基准测试中验证，D-Bot 可以有效地分析未见异常的根本原因，明显优于传统方法和 GPT-4 等基准模型。

D-Bot: 使用大型语言模型的数据库诊断系统

D-Bot: Database Diagnosis System using Large Language Models

Database administrators (DBAs) play a crucial role in managing, maintaining
and optimizing a database system to ensure data availability, performance, and
reliability. However, it is hard and tedious for DBAs to manage a large number
of database instances (e.g., millions of instances on the cloud databases).
Recently large language models (LLMs) have shown great potential to understand
valuable documents and accordingly generate reasonable answers. Thus, we
propose D-Bot, a LLM-based database administrator that can continuously acquire
database maintenance experience from textual sources, and provide reasonable,
well-founded, in-time diagnosis and optimization advice for target databases.
This paper presents a revolutionary LLM-centric framework for database
maintenance, including (i) database maintenance knowledge detection from
documents and tools, (ii) tree of thought reasoning for root cause analysis,
and (iii) collaborative diagnosis among multiple LLMs. Our preliminary
experimental results that D-Bot can efficiently and effectively diagnose the
root causes and our code is available at
github.com/TsinghuaDatabaseGroup/DB-GPT.

提出了一种基于大型语言模型的数据库管理员框架 D-Bot，可以从文本源获得数据库维护经验，并为目标数据库提供合理、有根据的实时诊断和优化建议。