摘要
随着档案数据量的迅速增加,传统的数据库系统在处理高并发请求和大规模数据存储方面面临巨大挑战,难以满足数据一致性、查询效率和系统扩展性的需求。本文探讨了综合档案平台背景下分布式数据库的优化策略,重点关注如何有效应对大规模档案数据存储和访问的挑战。本文提出了水平分片技术、基于权重的负载均衡、乐观并发控制以及B+树索引的优化策略。实验结果表明,本文提出的优化策略大幅提高了系统的并发处理能力、档案数据存储效率和查询性能,为档案平台在高并发和大数据场景下的稳定运行提供了有力支持。
As the volume of archival data increases rapidly,traditional database systems face significant challenges in handling high concurrency requests and large-scale data storage,making it difficult to meet requirements for data consistency,query efficiency,and system scalability.This paper explores the optimization strategies for distributed databases in the context of the integrated archival platform,focusing on how to effectively address the challenges of large-scale archival data storage and access.The paper proposes optimization strategies,including horizontal sharding,weighted load balancing,optimistic concurrency control,and B+tree indexing.Experimental results show that the proposed strategies significantly improve the system’s concurrency handling capability,archival data storage efficiency,and query performance,providing strong support for the stable operation of archival platforms in high-concurrency and large-data scenarios.
作者
杜厚勤
DU Houqin(Jinan Urban and Rural Construction Development Service Center,Jinan Shandong 250014,China)
出处
《信息与电脑》
2024年第17期10-13,共4页
Information & Computer
关键词
档案一体化平台
分布式数据库
负载均衡
B+树索引优化
integrated archival platform
distributed database
load balancing
B+tree index optimization
