摘 要
随着数据规模的快速增长，传统数据库查询优化方法在效率和适应性方面面临严峻挑战，亟需引入智能化技术提升性能。本研究旨在探索基于机器学习的数据库查询优化方法，通过构建自适应模型解决复杂查询场景下的性能瓶颈问题。研究采用深度强化学习算法对查询执行计划进行优化，并结合特征工程提取关键指标以增强模型预测能力。实验结果表明，所提出的方法能够显著降低查询响应时间，平均性能较传统方法提升35%以上，同时具备较强的泛化能力和鲁棒性。本研究的主要创新点在于将深度学习与数据库系统深度融合，提出了动态调整策略以应对多变的工作负载，为智能化数据库管理提供了新思路。研究成果不仅验证了机器学习在数据库优化领域的可行性，还为未来相关技术的发展奠定了理论与实践基础。关键词：数据库查询优化; 机器学习; 深度强化学习; 特征工程; 性能提升
Abstract
With the rapid growth of data scale, traditional database query optimization methods are facing severe challenges in terms of efficiency and adaptability, necessitating the introduction of intelligent technologies to enhance performance. This study aims to explore machine-learning-based approaches for database query optimization by constructing adaptive models to address performance bottlenecks in complex query scenarios. A deep reinforcement learning algorithm is employed to optimize query execution plans, while feature engineering is integrated to extract critical metrics, thereby enhancing the model's predictive capability. Experimental results demonstrate that the proposed method significantly reduces query response time, achieving an average performance improvement of over 35% compared to traditional methods, with strong generalization and robustness. The primary innovation of this research lies in the deep integration of deep learning with database systems, proposing dynamic adjustment strategies to handle varying workloads, thus providing new insights into intelligent database management. The findings not only validate the feasibility of machine learning in database optimization but also lay a theoretical and practical foundation for the future development of related technologies.Key words:Database Query Optimization; Machine Learning; Deep Reinforcement Learning; Feature Engineering; Performance Improvement
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、数据库查询优化的基础理论 2
1.1、查询优化的基本概念 2
1.2、传统查询优化方法分析 2
1.3、引入机器学习的必要性 3
第2章、机器学习在查询优化中的应用框架 4
2.1、查询优化的机器学习模型设计 4
2.2、特征选择与数据预处理方法 4
2.3、模型训练与评估策略 5
第3章、具体机器学习方法的研究与实现 6
3.1、基于监督学习的查询计划生成 6
3.2、非监督学习在索引优化中的应用 6
3.3、强化学习驱动的动态查询优化 6
第4章、实验验证与性能分析 8
4.1、实验环境与数据集构建 8
4.2、查询优化效果对比分析 8
4.3、性能瓶颈与改进方向探讨 9
结 论 10
参考文献 11
