Semantic characterization of MapReduce workloads

Abstract

MapReduce is a platform for analyzing large amounts of data on clusters of commodity machines. MapReduce is popular, in part thanks to its apparent simplicity. However, there are unstated requirements for the semantics of MapReduce applications that can affect their correctness and performance. MapReduce implementations do not check whether user code satisfies these requirements, leading to time-consuming debugging sessions, performance problems, and, worst of all, silently corrupt results. This paper makes these requirements explicit, framing them as semantic properties and assumed outcomes. It describes a black-box approach for testing for these properties, and uses the approach to characterize the semantics of 23 non-trivial MapReduce workloads. Surprisingly, we found that for most requirements, there is at least one workload that violates it. This means that MapReduce may be simple to use, but it is not as simple to use correctly. Based on our results, we provide insights to users on how to write higher-quality MapReduce code, and insights to system and language designers on ways to make their platforms more robust.