Hadoop AWS Word Count 例子

在AWS裏用Elastic Map Reduce 開一個Cluster

然後登陸master node並編譯以下程序：

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);
	}

}

設置：

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

運行成功後，因爲output文件夾在Hadoop FS下，所以可以這樣查看：

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