Mapreduce实例——求平均值

现有某电商关于商品点击情况的数据文件，表名为goods_click，包含两个字段（商品分类，商品点击次数），分隔符“\t”，内容如下：

商品分类    商品点击次数
52127    5
52120    93
52092    93
52132    38
52006    462
52109    28
52109    43
52132    0
52132    34
52132    9
52132    30
52132    45
52132    24
52009    2615
52132    25
52090    13
52132    6
52136    0
52090     10
52024    347

使用mapreduce统计出每类商品的平均点击次数

package mapreduce4;

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;
import org.omg.CORBA.PUBLIC_MEMBER;


//02.Mapreduce实例——求平均值
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("\t");
            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://192.168.51.100:8020/mymapreduce4/in/goods_click");
        Path out=new Path("hdfs://192.168.51.100:8020/mymapreduce4/out");
        FileInputFormat.addInputPath(job,in);
        FileOutputFormat.setOutputPath(job,out);
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }
}

结果:

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