Now we will move forward with MapReduce by learning a very common and easy example of word count. The goal of this example is to calculate how many times each word occurs in the provided documents. These documents can be considered as input to MapReduce's file.
In this example, we already have a set of text files—we want to identify the frequency of all the unique words existing in the files. We will get this by designing the Hadoop MapReduce phase.
In this section, we will see more on Hadoop MapReduce programming using Hadoop MapReduce's old API. Here we assume that the reader has already set up the Hadoop environment as described in Chapter 1, Getting Ready to Use R and Hadoop. Also, keep in mind that we are not going to use R to count words; only Hadoop will be used here.
Basically, Hadoop MapReduce has three main objects: Mapper, Reducer, and Driver. They can be developed with three Java classes; they are the Map class, Reduce class, and Driver class,...