MapReduce is a widely used framework that runs large scale data processing applications. However, there are very few systematic studies of MapReduce on large clusters and thus there is a lack of reference for expected behavior or issues while running applications in a large cluster. This paper describes our findings of running applications on Pivotal’sAnalytics Workbench, which consists of a 540-node Hadoop cluster. Our experience sheds light on how applications behave in a large-scale cluster. This paper discusses our experiences in three areas. The first describes scaling behavior of applications as the dataset size increases. The second discusses the appropriate settings for parallelism and overlap of map and reduce tasks. The third area discusses general observations.These areas have not been reported or studied previously. Our findings show that IO-intensive applications do not scale as data size increases and MapReduce applications require different amounts of parallelism and overlap to minimize completion time. Additionally, our observations also highlight the need for appropriate memory allocation for a MapReduce component and the importance of decreasing log file size.