Total sales hadoop MapReduce calculate total sales, monthly sales of each brand of fear

Today's entry to practice what mapreduce little knowledge
to prepare data the following screenshot

has IP xiaomi Huawei's three mobile phone brands, sales in 2019 are every day in every month
1 year seeking three brands total number of mobile phone sales

package com.sheng.hdfs;


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.LongWritable;
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.output.FileOutputFormat;
//计算
//maper计算框架，输出：
class Mapper1 extends Mapper<LongWritable, Text,Text, IntWritable>{
	@Override
	protected void map(LongWritable key,Text values,Context context) throws IOException, InterruptedException {
		//得到每一行的值
		String lines =values.toString();
		//对每一行的字符串按逗号来分
		String[] s= values.toString().split(",");
		//输出：key值和value值
		 context.write(new Text("总销售量"), new IntWritable(Integer.parseInt(s[1])));
	}
}
//Reducer输出到Hadoop中：他的输入是mapper的输出
class WcReduce1 extends Reducer<Text, IntWritable,Text, IntWritable> {
	@Override
	protected void reduce(Text key,Iterable<IntWritable>values,Context context)throws IOException, InterruptedException{
		int sum=0;
		for (IntWritable val:values) {
			sum+=val.get();
		}
		context.write(key,new IntWritable(sum));
		
	}
	
}

public class Home1 {
	public static void main(String[] args)throws IOException, ClassNotFoundException, InterruptedException {
		Configuration conf = new Configuration();
   		// conf.set("HADOOP_USER_NAME","ambow");

   		// Job对像
   		Job job = Job.getInstance(conf);
   		// 注册Jar驱动类
   		job.setJarByClass(Home1.class);
   		// 注册Mapper驱动类
   		job.setMapperClass(Mapper1.class);
           //注册Reducer驱动类
   		job.setReducerClass(WcReduce1.class);

   		// 设置MapOutPut输出的的类型
   		job.setMapOutputKeyClass(Text.class);
   		job.setMapOutputValueClass(IntWritable.class);

   		// 设置最终输出的类型
   		job.setOutputKeyClass(Text.class);
   		job.setOutputValueClass(IntWritable.class);

   		// 设置输入输出路径
   		// org.apache.hadoop.mapred.FileInputFormat 老版本
   		// org.apache.hadoop.mapreduce.lib.input.FileInputFormat 新版本

   		FileInputFormat.setInputPaths(job, new Path("/user/test/data.csv"));
   		FileOutputFormat.setOutputPath(job, new Path("/user/test/data1.csv"));
   //		FileInputFormat.setInputPaths(job, new Path(args[0]));
   //		FileOutputFormat.setOutputPath(job, new Path(args[1]));

   		// 设置reduce任务数为0 分区多少个？？？
   		// job.setNumReduceTasks(0);

   		// 提交作业
   		boolean result = job.waitForCompletion(true);

   		System.exit(result ? 0 : 1);

   	}
}

In Hadoop view the results as follows:

2 to calculate the number three brand mobile phone handsets were sold this year (calculated partition)

package com.sheng.hdfs;
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.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

//计算三个手机这一年的总销量
class Mapper2 extends Mapper<LongWritable, Text, Text, IntWritable>{
	@Override
	protected void map(LongWritable key,Text value,Context context)throws IOException,InterruptedException {
		String lines=value.toString();
		String[] s=lines.split(",");
		//key值是手机品牌名字，vaule值是销售量
		context.write(new Text(s[0]),new IntWritable(Integer.parseInt(s[1])));
		
	}	
}
//Reduce
class Reduce1 extends Reducer<Text, IntWritable, Text, IntWritable>{
	@Override
	protected void reduce(Text key,Iterable<IntWritable>value,Context context) throws IOException,InterruptedException {
		int sum=0;
		for(IntWritable val:value) {
			sum+=val.get();
		}
		context.write(key, new IntWritable(sum));
	}
}
//自定义分区
//注意:分区字段要和value字段相同
class Mypartitioner extends Partitioner<Text, IntWritable>{

	@Override
	public int getPartition(Text key, IntWritable value, int numPrtitons) {
		if (key.toString().equals("xiaomi")) {
			return 0;
		}
		if (key.toString().equals("华为")) {
			return 1;
		}
		 if (key.toString().equals("IP"))
	            return 2;
	        return 3;
	}
	
	
}
public class Home2 {

   	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
   		//
   		Configuration conf = new Configuration();
   		// conf.set("HADOOP_USER_NAME","ambow");

   		// Job对像
   		Job job = Job.getInstance(conf);

   		// 注册Jar驱动类
   		job.setJarByClass(Home2.class);
   		// 注册Mapper驱动类
   		job.setMapperClass(Mapper2.class);
           //注册Reducer驱动类
   		job.setReducerClass(Reduce1.class);

   		// 设置MapOutPut输出的的类型
   		job.setMapOutputKeyClass(Text.class);
   		job.setMapOutputValueClass(IntWritable.class);

   		// 设置最终输出的类型
   		job.setOutputKeyClass(Text.class);
   		job.setOutputValueClass(IntWritable.class);

   		// 设置输入输出路径
   		// org.apache.hadoop.mapred.FileInputFormat 老版本
   		// org.apache.hadoop.mapreduce.lib.input.FileInputFormat 新版本

   		FileInputFormat.setInputPaths(job, new Path("/user/test/data.csv"));
   		FileOutputFormat.setOutputPath(job, new Path("/user/test/data5.csv"));
   //		FileInputFormat.setInputPaths(job, new Path(args[0]));
   //		FileOutputFormat.setOutputPath(job, new Path(args[1]));

   		// 设置reduce任务数为0 分区多少个？？？
   	 job.setPartitionerClass(Mypartitioner.class);
   	 job.setNumReduceTasks(3);
   		// 提交作业
   		boolean result = job.waitForCompletion(true);

   		System.exit(result ? 0 : 1);

   	}
   }

: The results

of the total sales of 300 was calculated for each brand mobile phone each month

package com.sheng.hdfs;



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.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/*
 * 统计每个牌子的每个月的销售总量
 */
class WcMapper2 extends Mapper<LongWritable, Text, Text, IntWritable> {
	/*
	 * KeyIn:LongWritable 行的偏移量    ValueIn:Text 这一行的值 TextInputformat
	 * 
	 */

	@Override
	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

		// 得到每一行的值，反序化为字符串
		String lines = value.toString();
		// 对每一行的字符串按空格来拆分
		String[] s = value.toString().split(",");
		String str1=s[2];
		
		String[] s1 = str1.toString().split("月");
		String str3=s1[0];
		
		String str2=str3.substring(5);
		
		// 对每个单词写入Hadoop中 写入的数据必须是Hadoop的序列化
		context.write(new Text(str2+"月"+s[0]), new IntWritable(Integer.parseInt(s[1])));		
         // hello:1    word:1  aaaa:1  空格 :1  空格 :1    空格 :1 
	}
}
class WcReduce2 extends Reducer<Text, IntWritable, Text, IntWritable> {
	// reduce(单词key, 指定的单词mapper统计的List, Context context)
	@Override
	protected void reduce(Text key, Iterable<IntWritable> values, Context context)
			throws IOException, InterruptedException {

		int sum = 0;
         for (IntWritable val : values) {
             sum += val.get();
         }
         context.write(key, new IntWritable(sum));

}
}
class MyPartitioner1 extends Partitioner<Text, IntWritable> {

    //转发给4个不同的reducer
	  //转发给4个不同的reducer
    @Override
    public int getPartition(Text key, IntWritable value, int numPartitons) {
        if (key.toString().equals("xiaomi"))
            return 0;
        if (key.toString().equals("huawei"))
            return 1;
        if (key.toString().equals("iphone7"))
            return 2;
        return 3;
    }
}
public class Home3 {

	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
		//
		Configuration conf = new Configuration();
		// conf.set("HADOOP_USER_NAME","ambow");

		// Job对像
		Job job = Job.getInstance(conf);
		// 注册Jar驱动类
		job.setJarByClass(Home3.class);
		// 注册Mapper驱动类
		job.setMapperClass(WcMapper2.class);
       //注册Reducer驱动类
		job.setReducerClass(WcReduce2.class);

		// 设置MapOutPut输出的的类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);

		// 设置最终输出的类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);

		// 设置输入输出路径
		// org.apache.hadoop.mapred.FileInputFormat 老版本
		// org.apache.hadoop.mapreduce.lib.input.FileInputFormat 新版本

		FileInputFormat.setInputPaths(job, new Path("/user/test/data.csv"));
		FileOutputFormat.setOutputPath(job, new Path("/user/test/data6.csv"));
//		FileInputFormat.setInputPaths(job, new Path(args[0]));
//		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 设置reduce任务数为0 分区多少个？？？
		job.setPartitionerClass(MyPartitioner1.class);
		//job.setNumReduceTasks(3);
		 

		// 提交作业
		boolean result = job.waitForCompletion(true);

		System.exit(result ? 0 : 1);

	}
}

Calculation results: