• DocumentCode
    1667017
  • Title

    Application-Specific Evaluation of No SQL Databases

  • Author

    Klein, John ; Gorton, Ian ; Ernst, Neil ; Donohoe, Patrick ; Pham, Kim ; Matser, Chrisjan

  • Author_Institution
    Software Solutions Div., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • fYear
    2015
  • Firstpage
    526
  • Lastpage
    534
  • Abstract
    The selection of a particular NoSQL database for use in a big data system imposes a specific distributed software architecture and data model, making the technology selection difficult to defer and expensive to change. This paper reports on the selection of a NoSQL database for use in an Electronic Healthcare Record system being developed by a large healthcare provider. We performed application-specific prototyping and measurement to identify NoSQL products that fit data model and query use cases, and meet performance requirements. We found that database throughput varied by a factor of 10, read operation latency varied by a factor of 5, and write latency by a factor of 4 (with the highest throughput product delivering the highest latency). We also found that the throughput for workloads using strong consistency was 10-25% lower than workloads using eventual consistency. We conclude by reflecting on some of the fundamental difficulties of performing detailed technical evaluations of NoSQL databases specifically, and big data systems in general, that have become apparent during our study.
  • Keywords
    Big Data; SQL; data models; distributed databases; electronic health records; query processing; software architecture; Big Data system; NoSQL database; application-specific evaluation; data model; data query; distributed software architecture; electronic healthcare record system; Big data; Data models; Distributed databases; Scalability; Servers; Throughput; NoSQL; distributed databases; technology evaluation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2015 IEEE International Congress on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4673-7277-0
  • Type

    conf

  • DOI
    10.1109/BigDataCongress.2015.83
  • Filename
    7207267