面向关系数据库的智能索引调优方法
摘 要
关系数据库系统在现代信息系统中占据核心地位，随着数据规模的急剧增长和应用场景的日益复杂，索引调优成为提升数据库性能的关键环节。本文针对传统索引调优方法存在的人工成本高、效率低及难以适应动态变化等问题，提出一种面向关系数据库的智能索引调优方法。该方法基于机器学习算法构建索引效益预测模型，通过分析查询日志挖掘潜在索引需求，并结合代价估算与收益评估实现索引方案的自动推荐。实验结果表明，所提方法能够在保证准确性的前提下有效降低索引维护开销，显著提高查询性能。与现有技术相比，本研究创新性地将智能优化理论应用于索引管理领域，实现了从静态配置向动态调整的转变，为数据库性能优化提供了新思路。此外，该方法具备良好的通用性和可扩展性，能够适应不同类型数据库系统及多变的工作负载，具有重要的理论意义和实用价值。
关键词：关系数据库；索引调优；机器学习
Abstract
Relational database systems play a central role in modern information systems. With the rapid growth of data volumes and increasingly complex application scenarios, index tuning has become a critical factor in enhancing database performance. This paper proposes an intelligent index tuning method for relational databases that addresses the limitations of traditional approaches, such as high manual costs, low efficiency, and difficulty in adapting to dynamic changes. The proposed method employs machine learning algorithms to construct an index benefit prediction model, which analyzes query logs to uncover potential index requirements. By integrating cost estimation and benefit evaluation, it achieves automatic recommendation of index configurations. Experimental results demonstrate that this method effectively reduces index maintenance overhead while ensuring accuracy, thereby significantly improving query performance. Compared with existing technologies, this study innovatively applies intelligent optimization theories to the field of index management, facilitating a transition from static configuration to dynamic adjustment. It offers new perspectives for database performance optimization. Moreover, the method exhibits excellent generality and scalability, making it adaptable to various types of database systems and varying workloads, thus possessing significant theoretical implications and practical value.
Keywords: Relational Database；Index Tuning；Machine Learning
目 录
摘 要 I
Abstract II
引言 1
一、智能索引调优的理论基础 1
（一）关系数据库索引原理 1
（二）索引调优的基本概念 2
（三）智能算法在索引中的应用 2
二、智能索引调优的需求分析 2
（一）数据库性能瓶颈识别 2
（二）索引使用效率评估 3
（三）用户查询模式分析 3
三、智能索引调优的关键技术 4
（一）机器学习模型构建 4
（二）动态索引结构调整 4
（三）实时性能监控机制 5
四、智能索引调优的应用实践 5
（一）实验环境与数据集 5
（二）调优效果对比分析 6
（三）实际案例研究总结 6
结 论 7
致 谢 8
参考文献 9
摘 要
关系数据库系统在现代信息系统中占据核心地位，随着数据规模的急剧增长和应用场景的日益复杂，索引调优成为提升数据库性能的关键环节。本文针对传统索引调优方法存在的人工成本高、效率低及难以适应动态变化等问题，提出一种面向关系数据库的智能索引调优方法。该方法基于机器学习算法构建索引效益预测模型，通过分析查询日志挖掘潜在索引需求，并结合代价估算与收益评估实现索引方案的自动推荐。实验结果表明，所提方法能够在保证准确性的前提下有效降低索引维护开销，显著提高查询性能。与现有技术相比，本研究创新性地将智能优化理论应用于索引管理领域，实现了从静态配置向动态调整的转变，为数据库性能优化提供了新思路。此外，该方法具备良好的通用性和可扩展性，能够适应不同类型数据库系统及多变的工作负载，具有重要的理论意义和实用价值。
关键词：关系数据库；索引调优；机器学习
Abstract
Relational database systems play a central role in modern information systems. With the rapid growth of data volumes and increasingly complex application scenarios, index tuning has become a critical factor in enhancing database performance. This paper proposes an intelligent index tuning method for relational databases that addresses the limitations of traditional approaches, such as high manual costs, low efficiency, and difficulty in adapting to dynamic changes. The proposed method employs machine learning algorithms to construct an index benefit prediction model, which analyzes query logs to uncover potential index requirements. By integrating cost estimation and benefit evaluation, it achieves automatic recommendation of index configurations. Experimental results demonstrate that this method effectively reduces index maintenance overhead while ensuring accuracy, thereby significantly improving query performance. Compared with existing technologies, this study innovatively applies intelligent optimization theories to the field of index management, facilitating a transition from static configuration to dynamic adjustment. It offers new perspectives for database performance optimization. Moreover, the method exhibits excellent generality and scalability, making it adaptable to various types of database systems and varying workloads, thus possessing significant theoretical implications and practical value.
Keywords: Relational Database；Index Tuning；Machine Learning
目 录
摘 要 I
Abstract II
引言 1
一、智能索引调优的理论基础 1
（一）关系数据库索引原理 1
（二）索引调优的基本概念 2
（三）智能算法在索引中的应用 2
二、智能索引调优的需求分析 2
（一）数据库性能瓶颈识别 2
（二）索引使用效率评估 3
（三）用户查询模式分析 3
三、智能索引调优的关键技术 4
（一）机器学习模型构建 4
（二）动态索引结构调整 4
（三）实时性能监控机制 5
四、智能索引调优的应用实践 5
（一）实验环境与数据集 5
（二）调优效果对比分析 6
（三）实际案例研究总结 6
结 论 7
致 谢 8
参考文献 9
