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The purpose of this paper is to examine how resource usage of an analytic isaffected by the different underlying datatypes of Spark analytics - ResilientDistributed Datasets (RDDs), Datasets, and DataFrames. The resource usage of ananalytic is explored as a viable and preferred alternative of benchmarking bigdata analytics instead of the current common benchmarking performed usingexecution time. The run time of an analytic is shown to not be guaranteed to bea reproducible metric since many external factors to the job can affect theexecution time. Instead, metrics readily available through Spark including peakexecution memory are used to benchmark the resource usage of these differentdatatypes in common applications of Spark analytics, such as counting, caching,repartitioning, and KMeans.

Benchmarking Resource Usage of Underlying Datatypes of Apache Spark