In the previous chapter, we downloaded and ran our first Hadoop MapReduce job that used the ES-Hadoop library. Let's get inside the WordCount job to understand how it is developed.
Here is how WordsMapper.java looks:
package com.packtpub.esh;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
import java.util.StringTokenizer;
public class WordsMapper extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
Text word = new Text();
word.set(itr.nextToken());
context.write(word, one);
}
}
}To all the MapReduce developers, this Mapper is very well known...