使用MapReduce实现单词计数_JAVA_编程开发_程序员俱乐部

描述

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

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：

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代码实现：

class="java" name="code">import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

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 count11");
		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(otherArgs[0]));
		// FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
		
		FileInputFormat.addInputPath(job, new Path("input"));
		FileOutputFormat.setOutputPath(job, new Path("output"));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

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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。

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