Mapreduce案例之---统计手机号耗费的总上行流量、下行流量、总流量_本关任务:根据手机流量数据,编写 mapreduce 程序来统计出每个手机号码的一年总流

1.需求：

统计每一个手机号耗费的总上行流量、下行流量、总流量

2.数据准备：

2.1 输入数据格式：

时间戳、电话号码、基站的物理地址、访问网址的ip、网站域名、数据包、接包数、上行/传流量、下行/载流量、响应码

2.2 最终输出的数据格式：

手机号码       上行流量        下行流量           总流量

3.基本思路：

3.1 Map阶段：

（1） 读取一行数据，转换为字符串类型

（2） 切分字段

（3） 抽取手机号、上行流量、下行流量

（4）以手机号为key，bean对象（上行流量、下行流量、总流量）为value 进行封装 

（5）文件写出，即context.write(手机号,bean)

3.2 Reduce阶段

（1） 遍历集合上行流量和下行流量总和得到总流量

（2）实现自定义的bean来封装流量信息，并将bean作为map输出的key来传输

（3）MR程序在处理数据的过程中会对数据排序(map输出的kv对传输到reduce之前，会排序)，排序的依据是map输出的key

4.代码实现

4.1 编写流量统计的bean对象--FlowBean.java

package com.jike.hdfs;
 
import org.apache.hadoop.io.Writable;
 
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
 
//
/*
* 1.定义实现writable接口
* 2.重写序列化和反序列化方法
* 3.重写空参构造
* 4.tostring方法
* */
public class FlowBean implements Writable{
    private long upFlow;
    private long downFlow;
    private long sumFlow;
 
 
    //空参构造
    public FlowBean() {
    }
 
    public long getUpFlow() {
        return upFlow;
    }
 
    public void setUpFlow(long upFlow) {
        this.upFlow = upFlow;
    }
 
    public long getDownFlow() {
        return downFlow;
    }
 
    public void setDownFlow(long downFlow) {
        this.downFlow = downFlow;
    }
 
    public long getSumFlow() {
        return sumFlow;
    }
 
    public void setSumFlow() {
        this.sumFlow = this.upFlow + this.downFlow;
    }
 
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);
    }
 
    public void readFields(DataInput dataInput) throws IOException {
        this.upFlow = dataInput.readLong();
        this.downFlow = dataInput.readLong();
        this.sumFlow = dataInput.readLong();
    }
 
    @Override
    public String toString() {
        return upFlow +"\t" + downFlow +"\t" + sumFlow ;
    }
}
 

4.2 Mapper阶段--FlowBeanMapper.java

package com.jike.hdfs;
 
 
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
 
public class FlowBeanMapper  extends Mapper<LongWritable,Text, Text,FlowBean> {
    private Text outK =   new Text();
    private FlowBean outV =   new FlowBean();
 
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //1.获取和一行信息
        String line = value.toString();
 
        //2.切割
        //时间戳、            电话号码、         基站的物理地址、       访问网址的ip、         网站域名、  数据包、接包数、上行/传流量、下行/载流量、响应码
        // 1363157985066 	13726230503	00-FD-07-A4-72-B8:CMCC	120.196.100.82	i02.c.aliimg.com 24	   27	2481	   24681	200
        String[] split = line.split("\t");
 
        //3.抓取数据
        String phoneno = split[1];
        String upflow = split[split.length-3];
        String downflow = split[split.length-2];
 
        //4.封装
        outK.set(phoneno);
        outV.setUpFlow(Long.parseLong(upflow));
        outV.setDownFlow(Long.parseLong(downflow));
        outV.setSumFlow();
 
        //5.写出
        context.write(outK,outV);
    }
}

4.3 Reduce阶段--FlowBeanReducer.java

package com.jike.hdfs;
 
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
 
import java.io.IOException;
 
public class FlowBeanReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
    private FlowBean outV = new FlowBean();
 
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
        //1.遍历集合累加值
        long totalup = 0;
        long totaldown = 0;
        for (FlowBean value : values) {
            totalup += value.getUpFlow();
            totaldown += value.getDownFlow();
        }
        //2.封装
        outV.setUpFlow(totalup);
        outV.setDownFlow(totaldown);
        outV.setSumFlow();
 
        //3.写出
        context.write(key,outV);
    }
 
}

4.4 Driver 阶段--FlowBeanDriver.java

package com.jike.hdfs;
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
/*import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;*/ //这个是老的包，废弃了，需要用新的包才可以
import org.apache.hadoop.mapreduce.Job;
import java.io.IOException;
 
public class FlowBeanDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //1.获取job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
 
        //2.设置jar
        job.setJarByClass(FlowBeanDriver.class);
        //3.关联mapper Reducer
        job.setMapperClass(FlowBeanMapper.class);
        job.setReducerClass(FlowBeanReducer.class);
        //4.设置 mapper 输出key 和 value 类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
        //5.设置最终数据输出key 和value 类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
        //6.设置数据的输入和输出路径
        FileInputFormat.setInputPaths(job, new Path("D:\\BigData\\input"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\BigData\\output1"));
        //7.提交job
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

5.运行结果

参考文章： https://blog.csdn.net/zhao2chen3/article/details/110201664