MapReduce is a framework used to compute a large amount of data in a Hadoop cluster. MapReduce uses YARN to schedule the mappers and reducers as tasks, using the containers. The MapReduce framework enables you to write distributed applications to process large amounts of data from a filesystem, such as a Hadoop Distributed File System (HDFS), in a reliable and fault-tolerant manner. When you want to use the MapReduce framework to process data, it works through the creation of a job, which then runs on the framework to perform the tasks needed. A MapReduce job usually works by splitting the input data across worker nodes, running the mapper tasks in a parallel manner.
At this time, any failures that happen, either at the HDFS level or the failure of a mapper task, are handled automatically, to be fault-tolerant. Once the mappers have completed, in the results are copied over the network to other machines running the reducer tasks.
An example of using a MapReduce job...