MapReduce Algorithm is a very important algorithm which is increase data efficiently and generating large data sets on clusters of computers. It is developed by the Google. The MapReduce Algorithm work on the two main tasks first one is namely Map and Secondly Reduce. The Map task is done by the Mapper Class and the Reduce task is done by means of Reducer Class. These two classes are play an important role for the processing the large amount of data. Google first formulated the framework for the purpose of serving Google’s Web page indexing, and the new framework replaced earlier indexing algorithms. MapReduce Algorithm beneficial because library routines can be used to create parallel programs without any worries about infra cluster communication, task monitoring or failure handling processes. MapReduce Algorithm class takes the input, tokenizes it maps and sorts it. The output of Mapper class is used as input by Reducer class, which in turn searches matching pairs and reduces them. MapReduce Algorithm implements various mathematical algorithms to divide a task into small parts and assign them to multiple systems. In technical terms, MapReduce Algorithm helps in sending the Map & Reduce tasks to appropriate servers in a cluster. Sorting, Searching, Indexing and TF-IDF.