GaussMaster: An LLM-based Database Copilot System

TL;DR

GaussMaster is an LLM-based system that automates comprehensive database maintenance and troubleshooting, reducing human intervention in complex scenarios within the financial industry.

Contribution

It introduces a novel LLM-powered database copilot capable of automating SQL assistance and full maintenance processes, surpassing existing autonomous platforms.

Findings

Achieved zero human intervention in over 34 real-world scenarios

Successfully automated complex database troubleshooting and maintenance tasks

Demonstrated significant improvements in database management efficiency

Abstract

In the financial industry, data is the lifeblood of operations, and DBAs shoulder significant responsibilities for SQL tuning, database deployment, diagnosis, and service repair. In recent years, both database vendors and customers have increasingly turned to autonomous database platforms in an effort to alleviate the heavy workload of DBAs. However, existing autonomous database platforms are limited in their capabilities, primarily addressing single-point issues such as NL2SQL, anomaly detection, and SQL tuning. Manual intervention remains a necessity for comprehensive database maintenance. GaussMaster aims to revolutionize this landscape by introducing an LLM-based database copilot system. This innovative solution is designed not only to assist developers in writing efficient SQL queries but also to provide comprehensive care for database services. When database instances exhibit abnormal behavior, GaussMaster is capable of orchestrating the entire maintenance process automatically. It achieves this by analyzing hundreds of metrics and logs, employing a Tree-of-thought approach to identify root causes, and invoking appropriate tools to resolve issues. We have successfully implemented GaussMaster in real-world scenarios, such as the banking industry, where it has achieved zero human intervention for over 34 database maintenance scenarios. In this paper, we present significant improvements in these tasks with code at https://gitcode.com/opengauss/openGauss-GaussMaster.