Comparative Analysis of Techniques for Big-data Performance Testing

TLDR: In this article , the authors present several performance testing approaches for big data and compare them with various parameters such as domain applicability, scripting interface etc., and suggest tools to improve overall performance.

Abstract: Big data has become the primary objective for any area or department on the globe, including government, healthcare, and industrial sectors. Every day, a large amount of big data is generated, and information is added based on the three characteristics of big data. Handling massive amounts of data with traditional methods has become a significant challenge. To test large amount of duplicate data is very challenging task for researchers. Big data performance testing has great promise for obtaining more optimised solutions. Better performance saves time and money while also producing more optimised results. This paper makes an attempt to present several performance testing approaches. Furthermore, we compare the performance testing techniques and analyse the performance improvement factors. Finally, various tools for performance testing of big data, such as Apache Jmeter, Apache Drill, LoadRunner, WebLoad, YCSB etc., are also compared with various parameters such as domain applicability, scripting interface etc., and tools are suggested to improve overall performance.