统计手机号耗费的总上行流量、下行流量、总流量（序列化）
自定义排序
- 1.需求：
- 2.数据准备：
- 3.分析：
- 4.代码实现：
-
- （1）FlowBean类：
- （2）MapWritable类：
- （3）ReduceWritable类：
- （4）MainWritable类：
- 5.运行结果：
———————— —————————— —————————— —————————— —————————— 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：
输入数据的格式：
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
输出数据的格式：
1356·0436666 1116 954 2070
手机号码 上行流量 下行流量 总流量
将此数据新创建成phone_data.txt文档，以便后期实验时使用。
1363157985066 13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157995052 13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0 200
1363157991076 13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200
1363154400022 13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200
1363157993044 18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 视频网站 15 12 1527 2106 200
1363157995074 84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200
1363157993055 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
1363157995033 15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 3156 2936 200
1363157983019 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200
1363157984041 13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4 s19.cnzz.com 站点统计 24 9 6960 690 200
1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜索引擎 28 27 3659 3538 200
1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站点统计 3 3 1938 180 200
1363157992093 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200
1363157986041 13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200
1363157984040 13602846565 5C-0E-8B-8B-B6-00:CMCC 120.197.40.4 2052.flash2-http.qq.com 综合门户 15 12 1938 2910 200
1363157995093 13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200
1363157982040 13502468823 5C-0A-5B-6A-0B-D4:CMCC-EASY 120.196.100.99 y0.ifengimg.com 综合门户 57 102 7335 110349 200
1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜索引擎 21 18 9531 2412 200
1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200
1363157988072 13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200
1363157985066 13560436666 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157993055 13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 2003.分析：
Map阶段：
（1）读取一行数据，切分字段
（2）抽取手机号、上行流量、下行流量
（3）以手机号为key，bean对象为value输出，即context.write(手机号,bean);
Reduce阶段：
（1）累加上行流量和下行流量得到总流量。
（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
所以，我们如果要实现自己需要的排序规则，则可以考虑将排序因素放到key中，让key实现接口：WritableComparable。
然后重写key的compareTo方法。
4.代码实现：
（1）FlowBean类：
import lombok.AllArgsConstructor;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* Author : 若清 and wgh
* Version : 2020/4/13 & 1.0
*/
@AllArgsConstructor
public class FlowBean implements Writable {
private long upflow;
private long downflow;
private long sumflow;
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(long sumflow) {
this.sumflow = sumflow;
}
public FlowBean(long upflow,long downflow){
this.upflow = upflow;
this.downflow = downflow;
this.sumflow = upflow + downflow;
}
public void write(DataOutput output) throws IOException {
output.writeLong(this.upflow);
output.writeLong(this.downflow);
output.writeLong(this.sumflow);
}
public void readFields(DataInput Input) throws IOException {
this.upflow = Input.readLong();
this.downflow = Input.readLong();
this.sumflow = Input.readLong();
}
@Override
public String toString() {
return this.upflow + "\t" + this.downflow + "\t" + this.sumflow;
}
}
（2）MapWritable类：
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
/**
* Author : 若清 and wgh
* Version : 2020/4/13 & 1.0
*/
public class MapWritable extends Mapper<LongWritable, Text,Text,FlowBean> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//1.获取数据
String line = value.toString();
//2.切分数据
String[] fields = line.split("\t");
//3.获取上传流量
long upflow = Long.parseLong(fields[fields.length - 3]);
long downflow = Long.parseLong(fields[fields.length - 2]);
//4.输出
context.write(new Text(fields[1]),new FlowBean(upflow,downflow));
}
}
（3）ReduceWritable类：
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
* Author : 若清 and wgh
* Version : 2020/4/13 & 1.0
*/
public class ReduceWritable extends Reducer<Text,FlowBean,Text,FlowBean> {
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
//1.定义两个计数器，计算每个用户的上传流量、下载流量
long sumupflow = 0;
long sumdownflow = 0;
//2.累加的号的流量和
for (FlowBean f: values) {
sumupflow+=f.getUpflow();
sumdownflow+=f.getDownflow();
}
//3.输出
context.write(key,new FlowBean(sumupflow,sumdownflow));
}
}
（4）MainWritable类：
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
/**
* Author : 若清 and wgh
* Version : 2020/4/13 & 1.0
*/
public class MainWritable {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
args = new String[]{"D:\\input\\plus\\input\\phone_data.txt","D:\\input\\plus\\output\\0819"};
//1.获取job信息
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
//2.加载jar包
job.setJarByClass(MainWritable.class);
//3.关联map和reduce
job.setMapperClass(MapWritable.class);
job.setReducerClass(ReduceWritable.class);
//4.设置最终输出类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
//5.设置输入和输出路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//6.提交job任务
job.waitForCompletion(true);
}
}
5.运行结果：
（1）没排序之前的数据:
（2）排序之后的数据：
由此可见，排序的效果是很明显的。