Understanding MapReduce in My Way : Starting with Word Count

Word Count problem is known as the ‘Hello World’ for MapReduce. In this article I will explain, how I understand different bits of MapReduce in my way. The code provided in this article is trivial and is available in lots of places including the official MapReduce website. My concern would be to focus on how it really works.

Hadoop WordCount operation occurs in 3 stages –

Mapper Phase
Shuffle Phase
Reducer Phase

Hadoop WordCount Example- Mapper Phase Execution

The text from the input text file is tokenized into words to form a key value pair with all the words present in the input text file. The key is the word from the input file and value is ‘1’.

For instance if you consider the sentence “An elephant is an animal”. The mapper phase in the WordCount example will split the string into individual tokens i.e. words. In this case, the entire sentence will be split into 5 tokens (one for each word) with a value 1 as shown below.

In the reduce phase, all the keys are grouped together and the values for similar keys are added up to find the occurrences for a particular word. It is like an aggregation phase for the keys generated by the map phase. The reducer phase takes the output of shuffle phase as input and then reduces the key-value pairs to unique keys with values added up. In our example “An elephant is an animal.” is the only word that appears twice in the sentence. After the execution of the reduce phase of MapReduce WordCount example program, appears as a key only once but with a count of 2.

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class WordCount extends Configured implements Tool {
    public static class TokenizerMapper
    extends Mapper<LongWritable, Text, Text, IntWritable>{
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        public void map(LongWritable key, Text value, Context context
                ) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                context.write(word, one);
    public static class IntSumReducer
    extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();
        public void reduce(Text key, Iterable<IntWritable> values,
                Context context
                ) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            //sum = sum*2;
            context.write(key, result);
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new WordCount(), args);
    public int run(String[] args) throws Exception {
        Configuration conf = this.getConf();
        Job job = Job.getInstance(conf, "word count");
        TextInputFormat.addInputPath(job, new Path(args[0]));
        TextOutputFormat.setOutputPath(job, new Path(args[1]));
        return job.waitForCompletion(true) ? 0 : 1;

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