GaussMaster: An LLM-based Database Copilot System

2025-06-29

AI Summary

Database operations in the financial sector remain highly manual, and existing autonomous platforms only support isolated optimizations (e.g., NL2SQL or anomaly detection), lacking end-to-end, cross-stage coordination. Method: This paper proposes GaussMaster—the first LLM-driven system for full-lifecycle, collaborative database operations. It introduces a Tree-of-Thought reasoning architecture that unifies natural language understanding, SQL generation and optimization, multi-source log/metric analysis, and dynamic tool invocation. Contribution/Results: GaussMaster enables autonomous, closed-loop execution across SQL authoring, anomaly diagnosis, root-cause localization, and service remediation. Evaluated on 34 real-world financial operational scenarios, it achieves zero-human-intervention automation in banking production environments, significantly improving operational efficiency and system stability.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Automating SQL tuning and database maintenance tasks
• Reducing DBA workload with LLM-based copilot system
• Enhancing autonomous database platforms with comprehensive care

Innovation

• LLM-based copilot for SQL and database care
• Automated root cause analysis via Tree-of-thought
• Tool orchestration for zero human intervention