In Hadoop, HashPartitioner is used by default to partition the datasets among different reducers. This is done by hashing the keys to distribute the data evenly between all reducers and then sorting the data within each reducer accordingly. While this works just fine, if you were running a map reduce job that writes data into an existing Accumulo table, HashPartitioner would lead to each reducer writing to every tablet (sequentially). In this case, you might want to configure your MapReduce job to use RangePartitioner for an improved job’s write performance and therefore increase its speed. In this blog, we are going to discuss how to configure your job to use RangePartitioner as its partitioner, and, – assuming that you are running the job as part of a worklow – , how to incorporate it in Oozie.