摘 要
随着信息技术的迅猛发展，大数据已成为推动社会进步和技术创新的重要资源，而分布式存储与处理技术作为应对海量数据挑战的核心手段，其研究意义日益凸显。本文以分布式大数据存储与处理技术为研究对象，旨在探索高效、可靠的数据管理方案，解决传统集中式架构在扩展性、性能和容错能力方面的局限性。研究基于Hadoop、Spark等主流框架，结合实际应用场景，提出了一种优化的分布式存储策略和并行计算模型。通过引入数据分片机制和智能调度算法，显著提升了系统的吞吐量和响应速度，同时增强了对大规模异构数据的适应能力。实验结果表明，所提出的方案在数据存储效率、查询性能以及系统稳定性等方面均优于现有方法。此外，本文还针对数据一致性问题设计了改进的共识协议，并在跨地域分布式部署中验证了其有效性。研究的主要贡献在于提出了适用于多场景的分布式技术优化方案，为大数据环境下的高效存储与处理提供了理论支持和技术参考，同时为未来相关领域的深入研究奠定了基础。
关键词：分布式存储；大数据处理；优化策略；智能调度；数据一致性
ABSTRACT
With the rapid development of information technology, big data has become a critical resource driving social progress and technological innovation. Distributed storage and processing technologies, as core approaches to addressing the challenges posed by massive data volumes, have gained increasing research significance. This study focuses on distributed big data storage and processing technologies, aiming to explore efficient and reliable data management solutions that overcome the limitations of traditional centralized architectures in scalability, performance, and fault tolerance. Based on mainstream fr ameworks such as Hadoop and Spark, and combined with practical application scenarios, an optimized distributed storage strategy and parallel computing model are proposed. By introducing data sharding mechanisms and intelligent scheduling algorithms, the system's throughput and response speed are significantly improved, while its adaptability to large-scale heterogeneous data is enhanced. Experimental results demonstrate that the proposed solution outperforms existing methods in terms of data storage efficiency, query performance, and system stability. Furthermore, an improved consensus protocol is designed to address data consistency issues and validated in cross-regional distributed deployments. The primary contribution of this research lies in proposing a multi-scenario applicable optimization scheme for distributed technologies, providing theoretical support and technical references for efficient storage and processing in big data environments, and laying a foundation for future in-depth studies in related fields.
Keywords: Distributed Storage; Big Data Processing; Optimization Strategy; Intelligent Scheduling; Data Consistency
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 分布式大数据研究背景与意义 1
1.2 国内外研究现状分析 1
1.3 本文研究方法与技术路线 2
第2章 分布式存储关键技术研究 3
2.1 分布式文件系统架构分析 3
2.2 数据分片与冗余机制研究 3
2.3 存储一致性模型探讨 4
2.4 容错与恢复策略设计 4
第3章 大数据处理框架研究 6
3.1 并行计算模型与实现机制 6
3.2 数据流处理技术研究 6
3.3 查询优化与性能提升策略 7
3.4 资源调度与负载均衡分析 7
第4章 分布式存储与处理的融合研究 9
4.1 存储与计算协同优化研究 9
4.2 实时数据处理技术探索 9
4.3 数据安全与隐私保护机制 10
4.4 应用场景与案例分析 10
结论 12
参考文献 13
致 谢 14
