In the  we saw how to write a MapReduce program for finding the top-n items of a dataset. 

The code in the mapper emits a pair key-value for every word found, passing the word as the key and 1 as the value. Since the book has roughly 38,000 words, this means that the information transmitted from mappers to reducers is proportional to that number. A way to improve network performance of this program is to rewrite the mapper as follows:

public static class TopNMapper extends Mapper
    
      { private Map
     
       countMap = new HashMap<>(); @Override public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String cleanLine = value.toString().toLowerCase().replaceAll("[_|$#<>\\^=\\[\\]\\*/\\\\,;,.\\-:()?!\"']", " "); StringTokenizer itr = new StringTokenizer(cleanLine); while (itr.hasMoreTokens()) { String word = itr.nextToken().trim(); if (countMap.containsKey(word)) { countMap.put(word, countMap.get(word)+1); } else { countMap.put(word, 1); } } } @Override protected void cleanup(Context context) throws IOException, InterruptedException { for (String key: countMap.keySet()) { context.write(new Text(key), new IntWritable(countMap.get(key))); } } }
     
    

As we can see, we define an HashMap that uses words as the keys and the number of occurrences as the values; inside the loop, instead of emitting every word to the reducer, we put it into the map: if the word was already put, we increase its value, otherwise we set it to one. We also overrode the cleanup method, which is a method that Hadoop calls when the mapper has finished computing its input; in this method we now can emit the words to the reducers: doing this way, we can save a lot of network transmissions because we send to the reducers every word only once. 

The complete code of this class is available on . 

In the next post we'll see how to use combiners to leverage this approach.

from: http://andreaiacono.blogspot.com/2014/03/enhanced-mapreduce-for-top-n-items.html