02.Mapreduce实例——求平均值

实验原理

求平均数是MapReduce比较常见的算法，求平均数的算法也比较简单，一种思路是Map端读取数据，在数据输入到Reduce之前先经过shuffle，将map函数输出的key值相同的所有的value值形成一个集合value-list，然后将输入到Reduce端，Reduce端汇总并且统计记录数，然后作商即可。

实验步骤

1.在Linux中开启Hadoop

         start-all.sh  

2.在Linux本地新建/data/mapreduce4目录。

         mkdir -p /data/mapreduce4

3.下载hadoop2lib,解压到mapreduce文件夹下

         unzip hadoop2lib.zip

4.在HDFS上新建/mymapreduce4/in目录，然后将Linux本地/data/mapreduce4目录下的goods_click文件导入到HDFS的/mymapreduce4/in目录中。

         hadoop fs -mkdir -p /mymapreduce4/in 

         hadoop fs -put /data/mapreduce4/goods_click /mymapreduce4/in

注意：goods_click文件需要注意文件格式，数据后有隐藏的空格会导致API中读取失败，行末尾的空格应该取消掉，中间使用逗号分隔开

5.在IDEA中编写代码

package mapreduce;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class MyAverage{
    public static class Map extends Mapper<Object , Text , Text , IntWritable>{
        private static Text newKey=new Text();
        public void map(Object key,Text value,Context context) throws IOException, InterruptedException{
            String line=value.toString();
            System.out.println(line);
            String arr[]=line.split(",");
            newKey.set(arr[0]);
            int click=Integer.parseInt(arr[1]);
            context.write(newKey, new IntWritable(click));
        }
    }
    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable>{
        public void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{
            int num=0;
            int count=0;
            for(IntWritable val:values){
                num+=val.get();
                count++;
            }
            int avg=num/count;
            context.write(key,new IntWritable(avg));
        }
    }
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
        Configuration conf=new Configuration();
        System.out.println("start");
        Job job =new Job(conf,"MyAverage");
        job.setJarByClass(MyAverage.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        Path in=new Path("hdfs://192.168.149.10:9000/mymapreduce4/in/goods_click");
        Path out=new Path("hdfs://192.168.149.10:9000/mymapreduce4/out");
        FileInputFormat.addInputPath(job,in);
        FileOutputFormat.setOutputPath(job,out);
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }
}

６.创建resources文件夹，其中创建log4j.properties文件

hadoop.root.logger=DEBUG, console
log4j.rootLogger = DEBUG, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.out
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{2}: %m%n

７.导入hadoop2lib的包

8.运行结果

 

 

运行如果报权限错误，记得修改以下， root更换成你Linux中的用户名