MapReduce-based systems are increasingly being used for large-scale data analysis applications. Apache Hadoop is one of the most common open-source implementations of such paradigm. Minimizing the execution time is vital for MapReduce as well as for all data-processing applications, and the accurate estimation of execution time is essential for optimization. In this study, the author created a MapReduce performance model for Hadoop2.x that can precisely estimate the execution time of workload in MapReduce. This model combines a precedence tree model that can capture dependencies between different tasks in one MapReduce job, and a queueing network model that can capture the intra-job synchronization constraints. Such an analytical performance model is a particularly attractive tool as it might provide reasonably accurate job response time at significantly lower cost than the simulation experiment of real data-analysis systems. Furthermore, a clear understanding of systematic job response time under different circumstances is key to making decisions in MapReduce workload management and resource capacity planning.