  It is important for big data systems to identify their performance
bottleneck. However, the popular indicators such as resource utilizations, are
often misleading and incomparable with each other. In this paper, a novel
indicator framework which can directly compare the impact of different
indicators with each other is proposed to identify and analyze the performance
bottleneck efficiently. A methodology which can construct the indicator from
the performance change with the CPU frequency scaling is described. Spark is
used as an example of a big data system and two typical SQL benchmarks are used
as the workloads to evaluate the proposed method. Experimental results show
that the proposed method is accurate compared with the resource utilization
method and easy to implement compared with some white-box method. Meanwhile,
the analysis with our indicators lead to some interesting findings and valuable
performance optimization suggestions for big data systems.
