(大数据方向）分布式实验六：mapreduce编程实践

注意：此次编程实践，在伪分布式上实践，所以首先需要启动伪分布式的节点

进阶任务代码已上传资源中

start-dfs.sh

目录

一.新建项目

 二.编写代码（包：com.jxxy.mr.test）

（1）WordCount主类

（2） MyMapper类

三.制作jar包

 四.运行程序

---

 

一.新建项目

使用xftp导入相关配置文件

配置文件	mapred-site.xml	yarn-site.xml	hdfs-site.xml	core-site.xml
jar包	hadoop_jars（在实验eclipse中已创建好）

                                使用xftp导入到自己所建项目的src目录下

 二.编写代码（包：com.jxxy.mr.test）

（1）WordCount主类

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.mapred.TestMerge.MyMapper;
import org.apache.hadoop.mapred.TestMiniMRClientCluster.MyReducer;
//import org.apache.hadoop.mapred.WordCount; 
import org.apache.hadoop.mapreduce.Job; 
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) {
		// TODO Auto-generated method stub
		Configuration conf=new Configuration(); 
		 try{
		 //创建一个新作业
		 Job job=Job.getInstance(conf);
		 job.setJarByClass(WordCount.class); //jar包 
		 job.setJobName("myjob");
		 
		 //job.setInputPath(new Path());
		 //job.setOutputPath(new Path());
		 Path inPath=new Path("/user/root/hjq20192697.txt");
		 FileInputFormat.addInputPath(job,inPath);
		 
		//org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
		 
		 Path outPath=new Path("/output/wordcount");
		 //如果输出路径存在，则先删除
		 if(outPath.getFileSystem(conf).exists(outPath))
		 
		outPath.getFileSystem(conf).delete(outPath,true);
		 FileOutputFormat.setOutputPath(job,outPath); 
		 
		//org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
		 //创建MyMapper,MyReducer两个类
		 job.setMapperClass(MyMapper.class);
		 job.setMapOutputKeyClass(Text.class);
		 job.setOutputValueClass(IntWritable.class);
		 job.setReducerClass(MyReducer.class);
		 //提交作业
		 job.waitForCompletion(true);
		 }
		 catch(Exception e){
		 } 
	}
}

（2） MyMapper类

import java.io.IOException; 
import java.util.StringTokenizer; 
import org.apache.hadoop.io.IntWritable; 
import org.apache.hadoop.io.Text; 
import org.apache.hadoop.mapreduce.Mapper; 
public class MyMapper extends Mapper { 
	private final static IntWritable one=new IntWritable(1); 
		private Text word=new Text(); 
public void map(Object key,Text value,Context context) throws IOException,InterruptedException{ 
		StringTokenizer str=new 
				StringTokenizer(value.toString()); 
					while(str.hasMoreTokens()){ 
						word.set(str.nextToken()); 
						context.write(word,one); 
					} 
	} 
}

（3）MyReducer类

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class MyReducer extends Reducer{
			//迭代计算
	private IntWritable result=new IntWritable();
	public void reduce(Text key,Iterable values,Context context) throws IOException,InterruptedException{
		int sum=0;
		for(IntWritable val:values){
			sum+=val.get();
		}
		result.set(sum);
		context.write(key,result);
	} 
}

三.制作jar包

1.右击项目——>Export——>java——>jre file

2.选择 jre 打包后保存的路径

 3.使用xftp，将这个 wc.jar 传到 root/hjq 目录下

 四.运行程序

start-dfs.sh
start-yarn.sh
#如果启动了这处忽略

hadoop jar 3.jar com.jxxy.mr.test.WordCount