Title: GaussMaster: An LLM-based Database Copilot System Show Chinese title

Title: 高斯大师：一种基于大语言模型的数据库助手系统

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. Show Chinese abstract

Abstract: 在金融行业，数据是运营的生命线，数据库管理员（DBAs）承担着SQL调优、数据库部署、诊断和服务修复的重要责任。 近年来，数据库供应商和客户越来越多地转向自主数据库平台，以减轻数据库管理员的繁重工作量。 然而，现有的自主数据库平台在能力上存在局限，主要解决诸如NL2SQL、异常检测和SQL调优等单点问题。 对于全面的数据库维护，人工干预仍然是必要的。 GaussMaster旨在通过引入基于大语言模型（LLM）的数据库副驾驶系统来革新这一格局。 这一创新解决方案不仅旨在帮助开发人员编写高效的SQL查询，还为数据库服务提供全面的维护。 当数据库实例表现出异常行为时，GaussMaster能够自动协调整个维护过程。 它通过分析数百个指标和日志，采用思维树方法识别根本原因，并调用适当的工具来解决问题。 我们已在实际场景中成功实施GaussMaster，例如银行业，其中它在超过34个数据库维护场景中实现了零人工干预。 在本文中，我们在这些任务中提出了显著的改进，并提供了代码：https://gitcode.com/opengauss/openGauss-GaussMaster。