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The Development of a Benchmark Tool for NoSQL Databases

Author

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  • Ion LUNGU

    (University of Economic Studies, Bucharest, Romania)

  • Bogdan George TUDORICA

    (Petroleum-Gas University, Ploiesti, Romania)

Abstract

The aim of this article is to describe a proposed benchmark methodology and software application targeted at measuring the performance of both SQL and NoSQL databases. These represent the results obtained during PhD research (being actually a part of a larger application intended for NoSQL database management). A reason for aiming at this particular subject is the complete lack of benchmarking tools for NoSQL databases, except for YCBS [1] and a benchmark tool made specifically to compare Redis to RavenDB. While there are several well-known benchmarking systems for classical relational databases (starting with the canon TPC-C, TPC-E and TPC-H), on the other side of databases world such tools are mostly missing and seriously needed.

Suggested Citation

  • Ion LUNGU & Bogdan George TUDORICA, 2013. "The Development of a Benchmark Tool for NoSQL Databases," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 4(2), pages 13-20, July.
    • Handle: RePEc:aes:dbjour:v:4:y:2013:i:2:p:13-20
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    References listed on IDEAS

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    1. George Tudorica Bogdan, 2013. "Challenges for the NoSQL systems: Directions for Further Research and Development," International Journal of Sustainable Economies Management (IJSEM), IGI Global, vol. 2(1), pages 55-64, January.
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    Cited by:

    1. Marin FOTACHE & IonuÈ› HRUBARU, 2017. "Performance Analysis Of Two Big Data Technologies On A Cloud Distributed Architecture. Results For Non-Aggregate Queries On Medium-Sized Data," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 63(3), pages 21-50, January.
    2. Fotache Marin & Hrubaru Ionuț, 2016. "Performance Analysis of Two Big Data Technologies on a Cloud Distributed Architecture. Results for Non-Aggregate Queries on Medium-Sized Data," Scientific Annals of Economics and Business, Sciendo, vol. 63(s1), pages 21-50, December.

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