  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.

Databases;Artificial Intelligence;Computation and Language;Machine Learning