使用MapReduce实现单词计数

1  描述

       假定有一组文件，以空格为分隔符存放着数据。计算一组文件中字符所出现的次数。

       输入：

Hello World
Hello my love
Hello World , i love you

     输出：

Hello 3
World 2
love 2
...

2  实现思路

Map函数：
        以字符作为key值，value为1，生成键值对。

Reduce函数：
        获取Map输出的键值对，将各个键中的值相加，输出。

 

逻辑图：

 
 

3   代码实现

public class WordCount {

	public static class TokenizerMapper extends
			Mapper<Object, Text, Text, IntWritable> {

		private final static IntWritable one = new IntWritable(1);
		private Text word = new Text();

		public void map(Object key, Text value, Context context)
				throws IOException, InterruptedException {
			StringTokenizer itr = new StringTokenizer(value.toString());
			while (itr.hasMoreTokens()) {
				word.set(itr.nextToken());
				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();
			}
			result.set(sum);
			context.write(key, result);
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		
		Job job = new Job(conf, "word count");
		job.setJarByClass(WordCount.class);
		
		job.setMapperClass(TokenizerMapper.class);
		job.setCombinerClass(IntSumReducer.class);
		job.setReducerClass(IntSumReducer.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		FileInputFormat.addInputPath(job, new Path("input"));
		FileOutputFormat.setOutputPath(job, new Path("output"));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

4  总结：

         这个例子是Hadoop官网给出的例子，比较简单，是MapReduce的HelloWorld小程序。