Hadoop示例程序WordCount详解及实例（转）

1.图解MapReduce

2.简历过程：

Input:

Hello World Bye World

Hello Hadoop Bye Hadoop

Bye Hadoop Hello Hadoop

Map:

<Hello,1>

<World,1>

<Bye,1>

<World,1>

<Hello,1>

<Hadoop,1>

<Bye,1>

<Hadoop,1>

<Bye,1>

<Hadoop,1>

<Hello,1>

<Hadoop,1>

Sort:

<Bye,1>

<Bye,1>

<Bye,1>

<Hadoop,1>

<Hadoop,1>

<Hadoop,1>

<Hadoop,1>

<Hello,1>

<Hello,1>

<Hello,1>

<World,1>

<World,1>

Combine:

<Bye,1,1,1>

<Hadoop,1,1,1,1>

<Hello,1,1,1>

<World,1,1>

Reduce:

<Bye,3>

<Hadoop,4>

<Hello,3>

<World,2>

3.代码实例：

[c-sharp] view plaincopy

package com.felix;

import java.io.IOException;

import java.util.Iterator;

import java.util.StringTokenizer;

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.mapred.FileInputFormat;

import org.apache.hadoop.mapred.FileOutputFormat;

import org.apache.hadoop.mapred.JobClient;

import org.apache.hadoop.mapred.JobConf;

import org.apache.hadoop.mapred.MapReduceBase;

import org.apache.hadoop.mapred.Mapper;

import org.apache.hadoop.mapred.OutputCollector;

import org.apache.hadoop.mapred.Reducer;

import org.apache.hadoop.mapred.Reporter;

import org.apache.hadoop.mapred.TextInputFormat;

import org.apache.hadoop.mapred.TextOutputFormat;

/**

*

* 描述：WordCount explains by Felix

* @author Hadoop Dev Group

*/

public class WordCount

{

/**

* MapReduceBase类:实现了Mapper和Reducer接口的基类（其中的方法只是实现接口，而未作任何事情）

* Mapper接口：

* WritableComparable接口：实现WritableComparable的类可以相互比较。所有被用作key的类应该实现此接口。

* Reporter 则可用于报告整个应用的运行进度，本例中未使用。

*

*/

public static class Map extends MapReduceBase implements

Mapper<LongWritable, Text, Text, IntWritable>

{

/**

* LongWritable, IntWritable, Text 均是 Hadoop 中实现的用于封装 Java 数据类型的类，这些类实现了WritableComparable接口，

* 都能够被串行化从而便于在分布式环境中进行数据交换，你可以将它们分别视为long,int,String 的替代品。

*/

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

private Text word = new Text();

/**

* Mapper接口中的map方法：

* void map(K1 key, V1 value, OutputCollector<K2,V2> output, Reporter reporter)

* 映射一个单个的输入k/v对到一个中间的k/v对

* 输出对不需要和输入对是相同的类型，输入对可以映射到0个或多个输出对。

* OutputCollector接口：收集Mapper和Reducer输出的<k,v>对。

* OutputCollector接口的collect(k, v)方法:增加一个(k,v)对到output

*/

public void map(LongWritable key, Text value,

OutputCollector<Text, IntWritable> output, Reporter reporter)

throws IOException

{

String line = value.toString();

StringTokenizer tokenizer = new StringTokenizer(line);

while (tokenizer.hasMoreTokens())

{

word.set(tokenizer.nextToken());

output.collect(word, one);

}

}

}

public static class Reduce extends MapReduceBase implements

Reducer<Text, IntWritable, Text, IntWritable>

{

public void reduce(Text key, Iterator<IntWritable> values,

OutputCollector<Text, IntWritable> output, Reporter reporter)

throws IOException

{

int sum = 0;

while (values.hasNext())

{

sum += values.next().get();

}

output.collect(key, new IntWritable(sum));

}

}

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

{

/**

* JobConf：map/reduce的job配置类，向hadoop框架描述map-reduce执行的工作

* 构造方法：JobConf()、JobConf(Class exampleClass)、JobConf(Configuration conf)等

*/

JobConf conf = new JobConf(WordCount.class);

conf.setJobName("wordcount"); //设置一个用户定义的job名称

conf.setOutputKeyClass(Text.class); //为job的输出数据设置Key类

conf.setOutputValueClass(IntWritable.class); //为job输出设置value类

conf.setMapperClass(Map.class); //为job设置Mapper类

conf.setCombinerClass(Reduce.class); //为job设置Combiner类

conf.setReducerClass(Reduce.class); //为job设置Reduce类

conf.setInputFormat(TextInputFormat.class); //为map-reduce任务设置InputFormat实现类

conf.setOutputFormat(TextOutputFormat.class); //为map-reduce任务设置OutputFormat实现类

/**

* InputFormat描述map-reduce中对job的输入定义

* setInputPaths():为map-reduce job设置路径数组作为输入列表

* setInputPath()：为map-reduce job设置路径数组作为输出列表

*/

FileInputFormat.setInputPaths(conf, new Path(args[0]));

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

JobClient.runJob(conf); //运行一个job

}

}