Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China

Research Article

Research on the Method of Massive Data Storage Management

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  • @INPROCEEDINGS{10.4108/eai.27-10-2023.2342009,
        author={Wencheng  Zhang and Chao  Li and Junyi  Duan},
        title={Research on the Method of Massive Data Storage Management},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China},
        publisher={EAI},
        proceedings_a={ICEMBDA},
        year={2024},
        month={1},
        keywords={data storage micro-service object storage},
        doi={10.4108/eai.27-10-2023.2342009}
    }
    
  • Wencheng Zhang
    Chao Li
    Junyi Duan
    Year: 2024
    Research on the Method of Massive Data Storage Management
    ICEMBDA
    EAI
    DOI: 10.4108/eai.27-10-2023.2342009
Wencheng Zhang1,*, Chao Li1, Junyi Duan1
  • 1: The People's Liberation Army 63892
*Contact email: 19939888948@163.com

Abstract

With the deepening of informatization, the explosion of data generated by various industries presents data storage challenges due to data retention and the expectation of on-demand access to storage applications from any device.Based on the requirements of mass data storage, management and use, this paper proposes a distributed data storage architecture solution based on microservices, focusing on data storage, disaster recovery and backup capability, data consistency and data labeling.The solution is based on SpringCloud microservice architecture, MinIO object storage system, MySQL database and MangoDB database as data storage carriers, and uses SpringCloud+Vue front-end separation technology and Redis memory database high-performance data management and sharing technology.To realize the requirements of high-speed data storage and management, it provides a new idea for the storage and management of massive multi-format data.