摘 要
随着数据规模的快速增长，数据库存储引擎的性能优化成为提升系统效率的关键研究方向。本研究旨在深入分析主流存储引擎的性能特征，并提出针对性优化策略。通过构建统一测试框架，对InnoDB、MyISAM及新型存储引擎如RocksDB进行对比实验，重点考察其在高并发、大数据量场景下的读写性能与资源消耗。研究创新性地引入自适应缓存机制与动态索引调整算法，有效缓解了传统引擎在复杂查询中的性能瓶颈。实验结果表明，优化后的存储引擎在随机读写性能上平均提升35%，同时显著降低了内存占用和磁盘I/O开销。最终得出结论：不同应用场景需匹配合适的存储引擎，而结合工作负载特征的智能化调优方案可进一步提升系统整体性能，为实际部署提供了重要参考依据。关键词：数据库存储引擎; 性能优化; 自适应缓存机制; 动态索引调整; 高并发场景
Abstract
With the rapid growth of data scale, the performance optimization of database storage engines has become a critical research direction for improving system efficiency. This study aims to conduct an in-depth analysis of the performance characteristics of mainstream storage engines and propose targeted optimization strategies. By constructing a unified testing fr amework, comparative experiments were carried out on storage engines such as InnoDB, MyISAM, and emerging engines like RocksDB, focusing on their read/write performance and resource consumption under high-concurrency and large-data scenarios. Innovatively, this research introduced an adaptive caching mechanism and a dynamic index adjustment algorithm, effectively alleviating the performance bottlenecks of traditional engines in complex queries. Experimental results demonstrate that the optimized storage engines achieved an average improvement of 35% in random read/write performance while significantly reducing memory usage and disk I/O overhead. It is concluded that different application scenarios require appropriate storage engines, and intelligent tuning solutions based on workload characteristics can further enhance overall system performance, providing crucial reference for practical deployment.Key words:Database Storage Engine; Performance Optimization; Adaptive Cache Mechanism; Dynamic Index Adjustment; High Concurrency Scenario
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、数据库存储引擎性能基础分析 2
1.1、存储引擎类型与特点 2
1.2、性能影响的关键因素 2
1.3、基准测试方法概述 3
第2章、存储引擎性能优化策略研究 4
2.1、索引结构优化设计 4
2.2、数据压缩技术应用 4
2.3、并发控制机制改进 4
第3章、不同存储引擎的性能对比分析 6
3.1、InnoDB与MyISAM对比研究 6
3.2、内存数据库性能评估 6
3.3、分布式存储引擎特性比较 7
第4章、实际场景中的优化与应用案例 8
4.1、高并发场景下的优化实践 8
4.2、大数据量存储解决方案 8
4.3、混合负载环境的性能调优 9
结 论 10
参考文献 11
