CoreBigBench

Significant effort was put into big data benchmarking with focus on end-to-end applications. While covering basic functionalities implicitly, the details of the individual contributions to the overall performance are hidden. As a result, end-to-end benchmarks could be biased toward certain basic functions. Micro-benchmarks are more explicit at covering basic functionalities but they are usually targeted at some highly specialized functions. In this paper we present CoreBigBench, a benchmark that focuses on the most common big data engines/platforms functionalities like scans, two way joins, common UDF execution and more. These common functionalities are benchmarked over relational and key-value data models which covers majority of data models. The benchmark consists of 22 queries applied to sales data and key-value web logs covering the basic functionalities. We ran CoreBigBench on Hive as a proof of concept and verified that the benchmark is easy to deploy and collected performance data. Finally, we believe that CoreBigBench is a good fit for commercial big data engines performance testing focused on basic engine functionalities not covered in end-to-end benchmarks.