MRBS: A Comprehensive MapReduce Benchmark Suite

MapReduce is a promising programming model for distributed data processing. Extensive research has been conducted on the scalability of MapReduce, and several systems have been proposed in the literature, ranging from job scheduling to data placement and replication. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce techniques have been evaluated using microbenchmarks in an overly simplified setting, which may not be representative of real-world applications. This paper presents MRBS, a comprehensive benchmark suite for evaluating the performance of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to characterize application workload and dataload, and produces extensive high-level and low-level performance statistics. We illustrate the use of MRBS with Hadoop clusters running on Amazon EC2. Keywords-Benchmark; Performance; MapReduce; Hadoop; Cloud Computing