摘 要：随着大数据时代的到来，传统数据库架构在处理海量数据时面临性能瓶颈，难以满足实时性与高吞吐量的需求。为此，本文提出一种基于内存计算的高性能数据库架构设计，旨在通过优化数据存储与访问模式，显著提升数据库的查询效率和并发处理能力。研究采用分层内存管理策略，结合列式存储与索引技术，同时引入数据压缩算法以降低内存占用，并通过分布式内存计算框架实现任务并行化处理。实验结果表明，该架构在大规模数据集上的查询响应时间较传统磁盘数据库减少约70%，且支持更高的并发用户数。本研究的创新点在于将内存计算与数据库系统深度融合，突破了传统架构中I/O瓶颈的限制，为实时数据分析场景提供了高效解决方案，具有重要的理论价值和实际应用前景。
关键词：内存计算；高性能数据库架构；分层内存管理
Abstract：With the advent of the big data era, traditional database architectures encounter performance bottlenecks when handling massive datasets, struggling to meet the demands for real-time processing and high throughput. To address these challenges, this paper proposes a high-performance database architecture based on in-memory computing, which aims to significantly enhance query efficiency and concurrent processing capabilities by optimizing data storage and access patterns. The study employs a hierarchical memory management strategy, integrating columnar storage and indexing techniques while introducing data compression algorithms to reduce memory consumption. Furthermore, a distributed in-memory computing fr amework is utilized to enable parallel task processing. Experimental results demonstrate that the proposed architecture reduces query response time by approximately 70% compared to traditional disk-based databases on large-scale datasets, while supporting a higher number of concurrent users. The innovation of this research lies in the deep integration of in-memory computing with database systems, effectively overcoming I/O bottlenecks inherent in conventional architectures. This provides an efficient solution for real-time data analysis scenarios, offering significant theoretical value and promising practical application prospects.
引言 1
一、内存计算技术概述 1
(一)内存计算的基本原理 1
(二)内存计算的优势分析 2
(三)内存计算在数据库中的应用 2
二、高性能数据库架构需求分析 3
(一)数据库性能瓶颈剖析 3
(二)内存计算对性能的提升作用 3
(三)架构设计的核心目标 4
三、基于内存计算的架构设计方法 4
(一)数据存储与管理策略 4
(二)并行处理与优化机制 5
(三)容错与数据一致性保障 5
四、实验验证与性能评估 6
(一)测试环境与实验设计 6
(二)性能指标与结果分析 6
结论 7
参考文献 8
致谢 8
关键词：内存计算；高性能数据库架构；分层内存管理
Abstract：With the advent of the big data era, traditional database architectures encounter performance bottlenecks when handling massive datasets, struggling to meet the demands for real-time processing and high throughput. To address these challenges, this paper proposes a high-performance database architecture based on in-memory computing, which aims to significantly enhance query efficiency and concurrent processing capabilities by optimizing data storage and access patterns. The study employs a hierarchical memory management strategy, integrating columnar storage and indexing techniques while introducing data compression algorithms to reduce memory consumption. Furthermore, a distributed in-memory computing fr amework is utilized to enable parallel task processing. Experimental results demonstrate that the proposed architecture reduces query response time by approximately 70% compared to traditional disk-based databases on large-scale datasets, while supporting a higher number of concurrent users. The innovation of this research lies in the deep integration of in-memory computing with database systems, effectively overcoming I/O bottlenecks inherent in conventional architectures. This provides an efficient solution for real-time data analysis scenarios, offering significant theoretical value and promising practical application prospects.
Keywords: In-Memory Computing；High-Performance Database Architecture；Hierarchical Memory Management
引言 1
一、内存计算技术概述 1
(一)内存计算的基本原理 1
(二)内存计算的优势分析 2
(三)内存计算在数据库中的应用 2
二、高性能数据库架构需求分析 3
(一)数据库性能瓶颈剖析 3
(二)内存计算对性能的提升作用 3
(三)架构设计的核心目标 4
三、基于内存计算的架构设计方法 4
(一)数据存储与管理策略 4
(二)并行处理与优化机制 5
(三)容错与数据一致性保障 5
四、实验验证与性能评估 6
(一)测试环境与实验设计 6
(二)性能指标与结果分析 6
结论 7
参考文献 8
致谢 8
