第一次写MapReduce之WordCount实例

步骤如下：

1. 安装JDK，目前我的版本是1.7



2. 安装eclipse



3. 创建项目firstWordCountInstance



4. 导入开发需要的jar包

这步需要下载hadoop源码，然后在share目录下可以找到很多相关jar包，我的目录是hadoop-2.6.0-x64\hadoop-2.6.0\share\hadoop，有如下文件夹，为保险起见，全部导入到项目中去：



如下是导入后的jar包：



5.配置HADOOP_HOME

5.1.下载winutils的windows版本

GitHub上，有人提供了winutils的windows的版本，项目地址是：https://github.com/srccodes/hadoop-common-2.2.0-bin 直接下载此项目的zip包，下载后是文件名是hadoop-common-2.2.0-bin-master.zip，随便解压到一个目录

5.2.配置环境变量

增加用户变量HADOOP_HOME，值是下载的zip包解压的目录，然后在系统变量path里增加%HADOOP_HOME%\bin 即可。

6. 编写WordCount程序

package com.lyh;

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.Text;
import org.apache.hadoop.mapreduce.*;
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;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {
//嵌套类 Mapper
//Mapper<keyin,valuein,keyout,valueout>
public static class WordCountMapper extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

@Override
protected void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while(itr.hasMoreTokens()){
word.set(itr.nextToken());
context.write(word, one);//Context机制
}
}
}

//嵌套类Reducer
//Reduce<keyin,valuein,keyout,valueout>
//Reducer的valuein类型要和Mapper的va lueout类型一致,Reducer的valuein是Mapper的valueout经过shuffle之后的值
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
private IntWritable result = new IntWritable();

@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Context context)
throws IOException, InterruptedException {
int sum  = 0;
for(IntWritable i:values){
sum += i.get();
}
result.set(sum);
context.write(key,result);//Context机制
}

}

public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();//获得Configuration配置 Configuration: core-default.xml, core-site.xml
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();//获得输入参数 [hdfs://localhost:9000/user/dat/input, hdfs://localhost:9000/user/dat/output]
if(otherArgs.length != 2){//判断输入参数个数，不为两个异常退出
System.err.println("Usage:wordcount <in> <out>");
System.exit(2);
}

////设置Job属性
Job job = new Job(conf,"word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);//将结果进行局部合并
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//传入input path
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//传入output path，输出路径应该为空，否则报错org.apache.hadoop.mapred.FileAlreadyExistsException。

System.exit(job.waitForCompletion(true)?0:1);//是否正常退出
}
}

正常运行
7. 打jar包并上传到linux服务器

如下图：



8. 把readme.txt文件上传到hadoop文件目录下

参考命令如下：

hadoop fs -put /home/mart_cmo/task/lyh/readme.txt hdfs://ns1/user/mart_cmo/test

其中readme.txt文件内容如下：

configuration options from Spyder versions previous to They way did

9. 运行jar包

hadoop jar /home/mart_cmo/task/lyh/wordcount.jar com.lyh.WordCount hdfs://ns1/user/mart_cmo/test/readme.txt hdfs://ns1/user/mart_cmo/test/output2

并从output2中查看结果：



至此，运行结束