  The great prosperity of big data systems such as Hadoop in recent years makes
the benchmarking of these systems become crucial for both research and industry
communities. The complexity, diversity, and rapid evolution of big data systems
gives rise to various new challenges about how we design generators to produce
data with the 4V properties (i.e. volume, velocity, variety and veracity), as
well as implement application-specific but still comprehensive workloads.
However, most of the existing big data benchmarks can be described as attempts
to solve specific problems in benchmarking systems. This article investigates
the state-of-the-art in benchmarking big data systems along with the future
challenges to be addressed to realize a successful and efficient benchmark.
