Hadoop示例程序WordCount详解及实例

部分参考：http://www.javaeye.com/topic/606962

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：

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
}
}