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Consistency Models of NoSQL Databases

Author

Listed:
  • Miguel Diogo

    (Department of Informatics Engineering, University of Coimbra, Coimbra 3030-290, Portugal)

  • Bruno Cabral

    (Department of Informatics Engineering, University of Coimbra, Coimbra 3030-290, Portugal
    Centre of Informatics and Systems of University of Coimbra (CISUC), Coimbra 3030-290, Portugal)

  • Jorge Bernardino

    (Centre of Informatics and Systems of University of Coimbra (CISUC), Coimbra 3030-290, Portugal
    ISEC—Coimbra Institute of Engineering, Polytechnic of Coimbra, Coimbra 3030-199, Portugal)

Abstract

Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.

Suggested Citation

  • Miguel Diogo & Bruno Cabral & Jorge Bernardino, 2019. "Consistency Models of NoSQL Databases," Future Internet, MDPI, vol. 11(2), pages 1-19, February.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:43-:d:206015
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    Citations

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    Cited by:

    1. Giannis-Panagiotis Botilias & Spiridoula V. Margariti & Jeries Besarat & Dimitrios Salmas & George Pachoulas & Chrysostomos Stylios & Dimitris Skalkos, 2023. "Designing and Developing a Meat Traceability System: A Case Study for the Greek Meat Industry," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    2. Bernhard Standl & Nadine Schlomske-Bodenstein, 2021. "A Pattern Mining Method for Teaching Practices," Future Internet, MDPI, vol. 13(5), pages 1-14, April.
    3. Giuseppe Antonio Pierro & Roberto Tonelli & Michele Marchesi, 2020. "An Organized Repository of Ethereum Smart Contracts’ Source Codes and Metrics," Future Internet, MDPI, vol. 12(11), pages 1-15, November.

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