You want to learn more about MapReduce. You want to learn what it does so you can grasp Hadoop more thoroughly. What is MapReduce?
MapReduce is a core process of Hadoop. With multi-node deployments of Hadoop, MapReduce distributes data to different datanodes (servers that are controlled by a master server). This data is retrievable thanks to MapReduce. Conceptually there are two main components to MapReduce: mapper and reducer. The mapper function creates temporary key-value pairs. The reducer is composed of three phases: shuffle, sort and reduce. Shuffle and sort happen simultaneously. Together these components leverage commodity hardware in multi-node deployments. If you want to learn more you can see this link.
After you run a MapReduce job, you'll see output that is a summary of the job. The summary will include various quantifiable aspects of the job in the "Map-Reduce Framework" section of the text output.
It is not hard to set up Hadoop and run a MapReduce job. You can learn more about the process if you do this. To deploy Hadoop on a RedHat derivative, see this link; to deploy Hadoop on Ubuntu, see this link. You can follow this link to run a MapReduce job for learning