Before delving deep into MapReduce implementation in Hadoop, let's first understand MapReduce as a concept in parallel computing and why it is a preferred way of computing. MapReduce comprises two mutually exclusive but dependent phases, each capable of running on two different machines or nodes:
- Map: In the Map phase, the transformation of the data takes place. It splits data into key value pairs by splitting it on a keyword.
- Suppose we have a text file and we would want to do an analysis such as counting the total number of words or even the frequency with which the word has occurred in the text file. This is the classical word count problem of MapReduce. To address this problem, first we will have to identify the ...
