在AWS里用Elastic Map Reduce 开一个Cluster

然后登陆master node并编译下面程序：

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.LongWritable;

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 WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

		private final IntWritable one = new IntWritable(1);

		private Text word = new Text();

		@Override

		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

			String line = value.toString();

			StringTokenizer tokenizer = new StringTokenizer(line);

			while(tokenizer.hasMoreTokens()) {

				word.set(tokenizer.nextToken());

				context.write(word, one);

			}

		}

	}

	public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

		@Override

		protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

			int sum = 0;

			for(IntWritable value : values) {

				sum += value.get();

			}

			context.write(key, new IntWritable(sum));

		}

	}

	public static void main(String[] args) throws Exception {

		Configuration conf = new Configuration();

		Job job = new Job(conf, "Word Count hadoop-0.20");

        //setting the class names

        job.setJarByClass(WordCount.class);

        job.setMapperClass(WordCountMapper.class);

        job.setReducerClass(WordCountReducer.class);

        //setting the output data type classes

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(IntWritable.class);

        //to accept the hdfs input and outpur dir at run time

        FileInputFormat.addInputPath(job, new Path(args[0]));

        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ?

0 : 1);

	}

}

设置：

export CLASSPATH=$CLASSPATH:/home/hadoop/*:/home/hadoop/lib/*:'.'

javac WordCount.java

jar cvf WordCount.jar *.class

hadoop jar WordCount.jar WordCount s3://15-319-s13/book-dataset/pg_00 /output

执行成功后，由于output目录在Hadoop FS下，所以能够这样查看：

hadoop fs -cat /output/part-r-00000  | less

主要參考：

http://kickstarthadoop.blogspot.com/2011/04/word-count-hadoop-map-reduce-example.html

http://kickstarthadoop.blogspot.com/2011/05/word-count-example-with-hadoop-020.html