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大数据学习——mapreduce汇总手机号上行流量下行流量总流量

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时间戳                手机号            MAC地址                ip                    域名         上行流量包个数 下行    上行流量  下行流量   http状态码
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     13726238888    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    200


 作业：1/统计每一个用户（手机号）所耗费的总上行流量、下行流量，总流量
 map
 读一行，切分字段
 抽取手机号，上行流量 下行流量
 context.write(手机号,bean)

 reduce
 
 2/得出上题结果的基础之上再加一个需求：将统计结果按照总流量倒序排序
 
 
 3/将流量汇总统计结果按照手机归属地不同省份输出到不同文件中
  map
  读一行，切分字段
 抽取手机号，上行流量 下行流量
 context.write(手机号,bean)
 
 map输出的数据要分成6个区
 重写partitioner，让相同归属地的号码返回相同的分区号int
 
6省    跑6个reduce task 
reduce
拿到一个号码所有数据
遍历，累加
输出
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新建一个maven项目

项目结构如下

 

pom.xml

 

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <groupId>com.cyf</groupId>
  <artifactId>FlowSum</artifactId>
  <packaging>jar</packaging>
  <version>1.0</version>


  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>2.6.4</version>
    </dependency>
  </dependencies>

  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-jar-plugin</artifactId>
        <version>2.4</version>
        <configuration>
          <archive>
            <manifest>
              <addClasspath>true</addClasspath>
              <classpathPrefix>lib/</classpathPrefix>
              <mainClass>cn.itcast.mapreduce.FlowSum</mainClass>
            </manifest>
          </archive>
        </configuration>
      </plugin>
    </plugins>
  </build>


</project>
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FlowBean.java

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package cn.itcast.mapreduce;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

/**
 * Created by Administrator on 2019/1/4.
 */
public class FlowBean implements Writable {

    private long upFlow;
    private long downFlow;
    private long sumFlow;

    //序列化框架在反序列化的时候创建对象的实例会去调用我们的无参构造函数
    public FlowBean() {

    }

    public FlowBean(long upFlow, long downFlow, long sumFlow) {
        super();
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = sumFlow;
    }

    public FlowBean(long upFlow, long downFlow) {
        super();
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow + downFlow;
    }

    public void set(long upFlow, long downFlow) {
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow + downFlow;
    }

    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;
    }


    //序列化的方法
    public void write(DataOutput out) throws IOException {
        out.writeLong(upFlow);
        out.writeLong(downFlow);
        out.writeLong(sumFlow);

    }

    //反序列化的方法
    //注意：字段的反序列化的顺序跟序列化的顺序必须保持一致
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readLong();
        this.downFlow = in.readLong();
        this.sumFlow = in.readLong();

    }


    public String toString() {
        return upFlow + "\t" + downFlow + "\t" + sumFlow;
    }

    /**
     * 这里进行我们自定义比较大小的规则
     */

    public int compareTo(FlowBean o) {

        return (int) (o.getSumFlow() - this.getSumFlow());
    }
}
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FlumSum.java

 

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package cn.itcast.mapreduce;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;


/**
 * Created by Administrator on 2019/1/4.
 */
public class FlowSum {
    //在kv中传输我们的自定义的对象是可以的 ，不过必须要实现hadoop的序列化机制  也就是implement Writable
    public static class FlowSumMapper extends Mapper<LongWritable, Text, Text, FlowBean> {

        Text k = new Text();
        FlowBean v = new FlowBean();

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

            //将读取到的每一行数据进行字段的切分
            String line = value.toString();
            String[] fields = StringUtils.split(line, "\t");

            //抽取我们业务所需要的字段
            String phoneNum = fields[1];
            long upFlow = Long.parseLong(fields[fields.length - 3]);
            long downFlow = Long.parseLong(fields[fields.length - 2]);

            k.set(phoneNum);
            v.set(upFlow, downFlow);

            context.write(k, v);

        }
    }


    public static class FlowSumReducer extends Reducer<Text, FlowBean, Text, FlowBean> {

        FlowBean v = new FlowBean();

        //这里reduce方法接收到的key就是某一组《a手机号，bean》《a手机号，bean》   《b手机号，bean》《b手机号，bean》当中的第一个手机号
        //这里reduce方法接收到的values就是这一组kv对中的所以bean的一个迭代器
        @Override
        protected void reduce(Text key, Iterable<FlowBean> values, Context context)
                throws IOException, InterruptedException {

            long upFlowCount = 0;
            long downFlowCount = 0;

            for (FlowBean bean : values) {
                upFlowCount += bean.getUpFlow();
                downFlowCount += bean.getDownFlow();
            }
            v.set(upFlowCount, downFlowCount);
            context.write(key, v);
        }
    }


    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(FlowSum.class);

        //告诉程序，我们的程序所用的mapper类和reducer类是什么
        job.setMapperClass(FlowSumMapper.class);
        job.setReducerClass(FlowSumReducer.class);

        //告诉框架，我们程序输出的数据类型
//        job.setMapOutputKeyClass(Text.class);
//        job.setMapOutputValueClass(FlowBean.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        //告诉框架，我们程序使用的数据读取组件 结果输出所用的组件是什么
        //TextInputFormat是mapreduce程序中内置的一种读取数据组件  准确的说 叫做 读取文本文件的输入组件
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        //告诉框架，我们要处理的数据文件在那个路劲下
        FileInputFormat.setInputPaths(job, new Path("/flowsum/input"));

        //告诉框架，我们的处理结果要输出到什么地方
        FileOutputFormat.setOutputPath(job, new Path("/flowsum/output"));

        boolean res = job.waitForCompletion(true);

        System.exit(res ? 0 : 1);
    }

}
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新建 /flowsum/input

hadoop fs -mkdir -p /flowsum/input   

数据为3.txt
复制代码
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     13726238888    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    200
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把数据放在 /flowsum/input 目录下

hadoop fs -put 3.txt /flowsum/input   

 

把打包后的jar上传

运行

 hadoop jar FlowSum-1.0.jar cn.itcast.mapreduce.FlowSum

 

结果如下

 

 

 

 

 

倒序排列效果

 

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