Hadoop最基本的wordcount(统计词频)

package com.uniclick.dapa.dstest;



import java.io.IOException;

import java.net.URI;



import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.FileSystem;

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 void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

		String inputFilePath = "/user/zhouyuanlong/wordcount/input/wordTest*.txt";

		String outputFilePath = "/user/zhouyuanlong/wordcount/output/";

		String queue = "default";

		String jobName = "wordCount";

		if(args == null || args.length < 2){

			System.out.println("[-INPUT <inputFilePath>"

					+ "[-OUTPUT <outputFilePath>");

		}else{

			for(int i=0;i<args.length;i++){

				if("-Q".equals(args[i])){

					queue = args[++i];

				}

			}

		}

		Configuration conf = new Configuration();

		conf.set("mapred.job.queue.name", queue);

		Job job = new Job(conf, jobName);

		job.setJarByClass(WordCount.class);

		job.setMapperClass(WordCountMapper.class);

//		job.setCombinerClass(cls);

		job.setReducerClass(WordCountReducer.class);

		job.setOutputKeyClass(Text.class);

		job.setOutputValueClass(IntWritable.class);

		FileInputFormat.addInputPath(job, new Path(inputFilePath));

		Path path = new Path(outputFilePath);

		FileSystem fs = FileSystem.get(URI.create(outputFilePath), conf);

		if(fs.exists(path)){

//			fs.delete(path);

			fs.delete(path, true);

		}

		FileOutputFormat.setOutputPath(job, new Path(outputFilePath));

		System.exit(job.waitForCompletion(true) ? 1 : 0);

	}

	

	public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{

		private Text kt = new Text();

		private final static IntWritable vt = new IntWritable(1);



		public void map(LongWritable key, Text value, Context context)

				throws IOException, InterruptedException {

			String[] arr = value.toString().split("\t");

			for(int i = 0; i < arr.length; i++){

				kt.set(arr[i]);

				context.write(kt, vt);

			}

		}

	}

	

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

		private IntWritable vt = new IntWritable();

		

		public void reduce(Text key, Iterable<IntWritable> values, Context context) 

				throws IOException, InterruptedException{

			int sum = 0;

			for(IntWritable intVal : values){

				sum += intVal.get();

			}

			vt.set(sum);

			context.write(key, vt);

		}

	}

	

}

input目录中文件wordTest1.txt的内容(每行以table键分隔):

hello    world
hello    hadoop
hello    mapredruce

 

input目录中文件wordTest2.txt的内容(每行以table键分隔):

hello    world
hello    hadoop
hello    mapredruce

hdfs输出结果:

web     2
mapredruce      1
python  1
hadoop  1
hello   6
clojure 2
world   1
java    2

PS:对Hadoop自带的wordcount的例子略有改变