Nicholls, BrittanyAdangwa, MariamaEstes, RachelIradukunda, Hugues NelsonZhang, QingquanZhu, Ting2021-05-202021-05-202020-12-08Nicholls, Brittany; Adangwa, Mariama; Estes, Rachel; Iradukunda, Hugues Nelson; Zhang, Qingquan; Zhu, Ting; Benchmarking Resource Usage of Underlying Datatypes of Apache Spark; Systems and Control (2020); https://arxiv.org/abs/2012.04192http://hdl.handle.net/11603/21573The purpose of this paper is to examine how resource usage of an analytic is affected by the different underlying datatypes of Spark analytics - Resilient Distributed Datasets (RDDs), Datasets, and DataFrames. The resource usage of an analytic is explored as a viable, and preferred alternative of benchmarking big data analytics instead of the current common benchmarking performed using execution time. The run time of an analytic is shown to not be guaranteed to be a reproducible metric since many external factors to the job can affect the execution time. Instead, metrics readily available through Spark including peak execution memory are used to benchmark the resource usage of these different datatypes in common applications of Spark analytics, such as counting, caching, repartitioning, and KMeans.10 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Apache Spark analyticsresource usage for datatypesbenchmarking big data analyticsText