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
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:

   1 package org.myorg;
   3 import java.io.IOException;
   4 import java.util.*;
   6 import org.apache.hadoop.fs.Path;
   7 import org.apache.hadoop.conf.*;
   8 import org.apache.hadoop.io.*;
   9 import org.apache.hadoop.mapreduce.*;
  10 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
  11 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
  12 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
  13 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
  15 public class WordCount {
  17  public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
  18     private final static IntWritable one = new IntWritable(1);
  19     private Text word = new Text();
  21     public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
  22         String line = value.toString();
  23         StringTokenizer tokenizer = new StringTokenizer(line);
  24         while (tokenizer.hasMoreTokens()) {
  25             word.set(tokenizer.nextToken());
  26             context.write(word, one);
  27         }
  28     }
  29  } 
  31  public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
  33     public void reduce(Text key, Iterable<IntWritable> values, Context context) 
  34       throws IOException, InterruptedException {
  35         int sum = 0;
  36         for (IntWritable val : values) {
  37             sum += val.get();
  38         }
  39         context.write(key, new IntWritable(sum));
  40     }
  41  }
  43  public static void main(String[] args) throws Exception {
  44     Configuration conf = new Configuration();
  46         Job job = new Job(conf, "wordcount");
  48     job.setOutputKeyClass(Text.class);
  49     job.setOutputValueClass(IntWritable.class);
  51     job.setMapperClass(Map.class);
  52     job.setReducerClass(Reduce.class);
  54     job.setInputFormatClass(TextInputFormat.class);
  55     job.setOutputFormatClass(TextOutputFormat.class);
  57     FileInputFormat.addInputPath(job, new Path(args[0]));
  58     FileOutputFormat.setOutputPath(job, new Path(args[1]));
  60     job.waitForCompletion(true);
  61  }
  63 }

WordCount (last edited 2011-06-10 02:56:55 by Garrett Wu)