下面来讲一个hadoop的实例，统计文件words.txt中单词的个数。
一、环境需求：
1、虚拟机itcast01
2、在虚拟机itcast安装eclipse(注意是linux版本的，安装过程参考前面的博客)
二、过程
1、在itcast01虚拟机上创建文件words.txt文件，并写入一定数据，保存。
2、start-all.sh开启Hadoop服务，hadoop fs -put words.txt hdfs://itcast01:9000/把文件words.txt上传到hdfs服务器的更目录下。
3、打开eclipse，创建java工程。导入hadoop包和相关配置文件。
进入/hadoop安装目录下/share/hadoop/，(1)、把hdfs文件夹下的jar包和hdfs/bin目录下的jar包导入工程）。(2)、把mapreduce文件夹下的jar包和mapreduce/bin目录下的jar包导入工程。(3)、把yarn文件夹下的jar包和yarn/bin目录下的jar包导入工程。(4)、把common文件夹下的jar包和common/bin下的jar包导入工程。(5)、把hadoop安装目录下/etc/hadoop中core-site.xml和hdfs-site.xml文件配置到java工程的src目录下。
4、编写代码程序
建一个WcMapper类

package cn.itcast.hadoop.mr;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WcMapper extends Mapper<LongWritable, Text, Text,LongWritable> {//必须继承Mapper
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
            throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        String line=value.toString();//拿出一行
        String [] words=line.split(" ");
        for(String word:words)
        {
            context.write(new Text(word),new LongWritable(1));
        }
    }
}

建一个WcReducer类

package cn.itcast.hadoop.mr;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WcReducer extends Reducer<Text, LongWritable, Text, LongWritable> {//必须继承Reducer

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values,
            Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
        long count = 0;
        for (LongWritable number : values) {
            count += number.get();
        }
        context.write(new Text(key), new LongWritable(count));
    }
}

package cn.itcast.hadoop.mr;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCountRunner {
    public static void main(String [] args) throws IOException, ClassNotFoundException, InterruptedException
    {
        Job job=Job.getInstance(new Configuration());

        //组建Job类，提交给mapreduce
        job.setJarByClass(WordCountRunner.class);
        job.setMapperClass(WcMapper.class);
        job.setReducerClass(WcReducer.class);

        //设置输入输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);


        //构建输入输出的文件
        FileInputFormat.setInputPaths(job,"hdfs://itcast01:9000/words.txt");
        FileOutputFormat.setOutputPath(job,new Path("hdfs://itcast01:9000/wordcount821"));

        //提交job到mapreduce处理
        job.waitForCompletion(true);        
    }
}

5、将java文件打包成jar文件wordcountRunner.jar。
6、hadoop jar wordcountRunner.jar运行该文件。
7 、输入hadoop fs -ls /发现该目录下生成一个文件wordcount821.txt。

8、再输入hadoop fs -ls /wordcount821/发现出现success，表示java程序运行成功。
9、输入hadoop fs -cat /wordcount821/part-r-00000查看统计结果。