摘 要
随着信息技术的快速发展，数据库系统在现代企业中的核心地位日益凸显，其性能表现直接影响业务效率与用户体验。然而，面对复杂多变的工作负载和动态环境，传统静态调优方法已难以满足需求。为此，本文聚焦于数据库性能监控与动态调优技术的研究，旨在通过智能化手段提升数据库系统的运行效率与稳定性。研究基于实时监控数据采集与分析，提出了一种结合机器学习算法的动态调优框架，能够自动识别性能瓶颈并生成优化策略。实验结果表明，该框架可显著降低响应时间，提高吞吐量，并有效减少人工干预成本。此外，本文创新性地引入了自适应学习机制，使系统能够在运行过程中不断优化自身参数配置，从而更好地适应不同场景需求。总体而言，本研究为数据库性能管理提供了新的思路，对推动智能化运维技术发展具有重要意义。关键词：数据库性能监控; 动态调优; 机器学习; 自适应学习机制; 智能化运维
Abstract
With the rapid development of information technology, the core role of database systems in modern enterprises has become increasingly prominent, and their performance directly affects business efficiency and user experience. However, traditional static tuning methods struggle to meet the demands posed by complex and dynamic workloads in ever-changing environments. To address this challenge, this study focuses on the research of database performance monitoring and dynamic tuning techniques, aiming to enhance the operational efficiency and stability of database systems through intelligent approaches. Based on real-time monitoring data collection and analysis, a dynamic tuning fr amework incorporating machine learning algorithms is proposed, which can automatically identify performance bottlenecks and generate optimization strategies. Experimental results demonstrate that this fr amework significantly reduces response time, increases throughput, and effectively minimizes manual intervention costs. Furthermore, an innovative adaptive learning mechanism is introduced, enabling the system to continuously optimize its parameter configurations during operation, thereby better accommodating diverse scenario requirements. Overall, this research provides new insights into database performance management and holds significant implications for advancing intelligent operations and maintenance technologies.Key words:Database Performance Monitoring; Dynamic Tuning; Machine Learning; Adaptive Learning Mechanism; Intelligent Operations And Maintenance
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、数据库性能监控基础 2
1.1、性能监控的核心概念 2
1.2、监控技术的分类与特点 2
1.3、监控工具的选择与应用 3
第2章、性能瓶颈识别与分析 4
2.1、瓶颈识别的关键指标 4
2.2、数据采集与分析方法 4
2.3、典型场景下的问题诊断 5
第3章、动态调优策略与实践 6
3.1、调优的基本原则与流程 6
3.2、查询优化的技术手段 6
3.3、存储与索引的动态调整 6
第4章、自适应调优与未来趋势 8
4.1、自适应调优的理论基础 8
4.2、人工智能在调优中的应用 8
4.3、动态调优技术的发展方向 9
结 论 10
参考文献 11
