Versions Compared


  • This line was added.
  • This line was removed.
  • Formatting was changed.
Comment: Migration of unmigrated content due to installation of a new plugin

WordCount Example

WordCount example reads text files and counts how often words occur. The input is text files and the output is text files, each line of which contains a word and the count of how often it occured, separated by a tab.

Each mapper takes a line as input and breaks it into words. It then emits a key/value pair of the word and 1. Each reducer sums the counts for each word and emits a single key/value with the word and sum.

As an optimization, the reducer is also used as a combiner on the map outputs. This reduces the amount of data sent across the network by combining each word into a single record.

To run the example, the command syntax is

Wiki Markup
bin/hadoop jar hadoop-\*-examples.jar wordcount \[-m <#maps>\] \[-r <#reducers>\] <in-dir> <out-dir>

All of the files in the input directory (called in-dir in the command line above) are read and the counts of words in the input are written to the output directory (called out-dir above). It is assumed that both inputs and outputs are stored in HDFS (see ImportantConcepts). If your input is not already in HDFS, but is rather in a local file system somewhere, you need to copy the data into HDFS using a command like this:

bin/hadoop dfs -mkdir <hdfs-dir>

bin/hadoop dfs -copyFromLocal <local-dir> <hdfs-dir>

As of version, you only need to run a command like this:

bin/hadoop dfs -copyFromLocal <local-dir> <hdfs-dir>

Word count supports generic options : see DevelopmentCommandLineOptions

Below is the standard wordcount example implemented in Java:

Code Block
package org.myorg;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCount {
 public static class Map 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 {
        String line = value.toString();
 	StringTokenizer tokenizer = new StringTokenizer(line);
 	while (tokenizer.hasMoreTokens()) {
 	    context.write(word, one);
 public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {

    public void reduce(Text key, Iterable<IntWritable> values, Context context) 
      throws IOException, InterruptedException {
        int sum = 0;
 	for (IntWritable val : values) {
 	    sum += val.get();
 	context.write(key, new IntWritable(sum));
 public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
	Job job = new Job(conf, "wordcount");
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));