Evaluating The Scalability of Big Data Frameworks | Zendy

David Sánchez | Zendy; Oswaldo Solarte | Zendy; Víctor Bucheli | Zendy; Hugo Ordóñez | Zendy

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Evaluating The Scalability of Big Data Frameworks

Author(s) -

David Sánchez,

Oswaldo Solarte,

Víctor Bucheli,

Hugo Ordóñez

Publication year - 2018

Publication title -

scalable computing practice and experience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.192

H-Index - 18

ISSN - 1895-1767

DOI - 10.12694/scpe.v19i3.1402

Subject(s) - scalability , spark (programming language) , big data , computer science , sort , constant (computer programming) , distributed computing , data mining , database , programming language

The aim of this paper is to present a method based on the isoefficiency model for assessing the scalability in big data environments. The programs word count and sort were implemented and compared in Hadoop and Spark. The results confirm that isoefficiency presented a linear growth as the size of the data sets was increased. They were checked experimentally to ensure that the evaluated frameworks are scalable and a sublinear function was obtained. This paper discusses how scalability in big data is governed by a constant of scalability (β ).

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