用大模型打造数据库全链路自动运维系统，银行场景已实现零人工干预。
GaussMaster: An LLM-based Database Copilot System
- 基于大模型构建树状思维推理框架，综合分析数百项指标与日志
- 在真实银行场景中实现34个维护任务全程自动处理，无需人工介入
- 适合需要降低数据库运维成本的金融、企业级用户参考
在金融行业，数据是运营的生命线，数据库管理员（DBA）承担着SQL调优、部署、诊断与故障修复等重大职责。近年来，数据库厂商和客户纷纷转向自治数据库平台以缓解DBA的工作压力。然而，现有平台能力有限，仅能解决诸如自然语言转SQL、异常检测、SQL调优等单一问题，全面维护仍需人工介入。GaussMaster旨在革新这一局面，提出一个基于大语言模型的数据库协作者系统。该系统不仅辅助开发者编写高效SQL，更能对数据库服务提供全周期支持。当实例出现异常时，GaussMaster可自动完成整个维护流程：通过分析数百项指标与日志，采用树状思维（Tree-of-thought）方法定位根本原因，并调用相应工具进行修复。我们已在银行业等真实场景成功部署GaussMaster，实现了超过34种数据库维护任务的全程自动化，无需人工干预。相关代码已开源：https://gitcode.com/opengauss/openGauss-GaussMaster。
原文摘要 · Abstract (English)
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.
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