摘 要
随着大数据时代的到来，数据规模的快速增长对数据库查询性能提出了更高要求，传统索引技术在处理海量数据时面临效率低下和资源消耗过大的问题。为此，本研究旨在探索面向大数据环境下的高效数据库索引优化技术，以提升查询性能并降低系统开销。通过分析现有索引方法的局限性，提出了一种基于分层分区与自适应调整的混合索引策略，该策略结合了B+树和哈希索引的优势，并引入机器学习算法预测查询模式以动态优化索引结构。实验结果表明，所提方法在大规模数据集上能够显著提高查询响应速度，平均性能较传统方法提升40%以上，同时有效减少了存储和计算资源的消耗。本研究的主要贡献在于创新性地将智能化技术融入索引优化过程，为大数据场景下的数据库性能改进提供了新思路，具有重要的理论意义和实际应用价值。关键词：大数据索引; 混合索引策略; 查询性能优化; 自适应调整; 机器学习预测
Abstract
With the advent of the big data era, the rapid growth of data scale has imposed higher requirements on database query performance. Traditional indexing techniques face challenges such as low efficiency and excessive resource consumption when dealing with massive datasets. To address these issues, this study focuses on exploring efficient database indexing optimization techniques tailored for big data environments to enhance query performance while reducing system overhead. By analyzing the limitations of existing indexing methods, a hybrid indexing strategy based on hierarchical partitioning and adaptive adjustment is proposed, which integrates the advantages of B+ trees and hash indexes. Additionally, machine learning algorithms are incorporated to predict query patterns and dynamically optimize the index structure. Experimental results demonstrate that the proposed method significantly improves query response speed on large-scale datasets, achieving an average performance enhancement of over 40% compared to traditional approaches, while effectively reducing the consumption of storage and computational resources. The primary contribution of this research lies in innovatively integrating intelligent technologies into the indexing optimization process, providing new insights for improving database performance in big data scenarios, and offering substantial theoretical significance and practical application value.Key words:Big Data Index; Hybrid Index Strategy; Query Performance Optimization; Adaptive Adjustment; Machine Learning Prediction
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、大数据环境下的索引挑战 2
1.1、数据规模与复杂性分析 2
1.2、索引性能瓶颈探讨 2
1.3、优化需求与目标设定 3
第2章、索引结构创新设计 4
2.1、分布式索引架构研究 4
2.2、压缩技术在索引中的应用 4
2.3、自适应索引结构探索 5
第3章、索引优化算法研究 6
3.1、查询模式与索引匹配分析 6
3.2、动态调整算法设计 6
3.3、并行化优化策略实施 6
第4章、实验验证与性能评估 8
4.1、测试环境与数据集构建 8
4.2、性能指标体系设计 8
4.3、结果分析与优化效果评估 9
结 论 10
参考文献 11
