大数据技术之流量汇总案例

7.2 流量汇总程序案例

7.2.1 需求1：统计手机号耗费的总上行流量、下行流量、总流量（序列化）

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 phone_date.txt

    13726230503    00-FD-07-A4-72-B8:CMCC    120.196.100.82    i02.c.aliimg.com        24    27    2481    24681    200
    13826544101    5C-0E-8B-C7-F1-E0:CMCC    120.197.40.4            4    0    264    0    200
    13926435656    20-10-7A-28-CC-0A:CMCC    120.196.100.99            2    4    132    1512    200
    13926251106    5C-0E-8B-8B-B1-50:CMCC    120.197.40.4            4    0    240    0    200
    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
    84138413    5C-0E-8B-8C-E8-20:7DaysInn    120.197.40.4    122.72.52.12        20    16    4116    1432    200
    13560439658    C4-17-FE-BA-DE-D9:CMCC    120.196.100.99            18    15    1116    954    200
    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
    13719199419    68-A1-B7-03-07-B1:CMCC-EASY    120.196.100.82            4    0    240    0    200
    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
    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
    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
    13560439658    C4-17-FE-BA-DE-D9:CMCC    120.196.100.99            15    9    918    4938    200
    13480253104    5C-0E-8B-C7-FC-80:CMCC-EASY    120.197.40.4            3    3    180    180    200
    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
    13922314466    00-FD-07-A2-EC-BA:CMCC    120.196.100.82    img.qfc.cn        12    12    3008    3720    200
    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
    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
    13925057413    00-1F-64-E1-E6-9A:CMCC    120.196.100.55    t3.baidu.com    搜索引擎    69    63    11058    48243    200
    13760778710    00-FD-07-A4-7B-08:CMCC    120.196.100.82            2    2    120    120    200
    13560436666    00-FD-07-A4-72-B8:CMCC    120.196.100.82    i02.c.aliimg.com        24    27    2481    24681    200
    13560436666    C4-17-FE-BA-DE-D9:CMCC    120.196.100.99            18    15    1116    954    200

输入数据格式：      

1363157993055     13560436666    C4-17-FE-BA-DE-D9:CMCC    120.196.100.99        18    15    1116        954        200
                   手机号码                                                                   上行流量    下行流量

 输出数据格式

13560436666     1116              954         2070
手机号码        上行流量        下行流量        总流量

3）分析

基本思路：

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）编写mapreduce程序

（1）编写流量统计的bean对象

package com.xyg.mr.flowsum;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;

// bean对象要实例化
public class FlowBean implements Writable {

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

    // 反序列化时，需要反射调用空参构造函数，所以必须有
    public FlowBean() {
        super();
    }

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

    public long getSumFlow() {
        return sumFlow;
    }

    public void setSumFlow(long sumFlow) {
        this.sumFlow = sumFlow;
    }

    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;
    }

    /**
     * 序列化方法
     * 
     * @param out
     * @throws IOException
     */
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeLong(upFlow);
        out.writeLong(downFlow);
        out.writeLong(sumFlow);
    }

    /**
     * 反序列化方法 
注意反序列化的顺序和序列化的顺序完全一致
     * 
     * @param in
     * @throws IOException
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        upFlow = in.readLong();
        downFlow = in.readLong();
        sumFlow = in.readLong();
    }

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

（2）编写mapreduce主程序

package com.xyg.mr.flowsum;
import java.io.IOException;
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.output.FileOutputFormat;

public class FlowCount {

    static class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> {

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 1 将一行内容转成string
            String ling = value.toString();

            // 2 切分字段
            String[] fields = ling.split("\t");

            // 3 取出手机号码
            String phoneNum = fields[1];

            // 4 取出上行流量和下行流量
            long upFlow = Long.parseLong(fields[fields.length - 3]);
            long downFlow = Long.parseLong(fields[fields.length - 2]);

            // 5 写出数据
            context.write(new Text(phoneNum), new FlowBean(upFlow, downFlow));
        }
    }

    static class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
        @Override
        protected void reduce(Text key, Iterable<FlowBean> values, Context context)
                throws IOException, InterruptedException {
            long sum_upFlow = 0;
            long sum_downFlow = 0;

            // 1 遍历所用bean，将其中的上行流量，下行流量分别累加
            for (FlowBean bean : values) {
                sum_upFlow += bean.getUpFlow();
                sum_downFlow += bean.getDownFlow();
            }

            // 2 封装对象
            FlowBean resultBean = new FlowBean(sum_upFlow, sum_downFlow);
            context.write(key, resultBean);
        }
    }

    public static void main(String[] args) throws Exception {
        // 1 获取配置信息，或者job对象实例
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        // 6 指定本程序的jar包所在的本地路径
        job.setJarByClass(FlowCount.class);

        // 2 指定本业务job要使用的mapper/Reducer业务类
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);

        // 3 指定mapper输出数据的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        // 4 指定最终输出的数据的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        // 5 指定job的输入原始文件所在目录
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

（3）将程序打成jar包，然后拷贝到hadoop集群中。

（4）启动hadoop集群（3）将程序打成jar包，然后拷贝到hadoop集群中。

（5）执行flowcount程序

[root@node21 ~]$ hadoop jar flowcount.jar com.xyg.mr.flowsum.FlowCount /user/root/flowcount/input/ /user/root/flowcount/output

（6）查看结果

[root@node21 ~]$ hadoop fs -cat /user/root/flowcount/output/part-r-00000

13480253104 FlowBean [upFlow=180, downFlow=180, sumFlow=360]

13502468823 FlowBean [upFlow=7335, downFlow=110349, sumFlow=117684]

13560436666 FlowBean [upFlow=1116, downFlow=954, sumFlow=2070]

13560439658 FlowBean [upFlow=2034, downFlow=5892, sumFlow=7926]

13602846565 FlowBean [upFlow=1938, downFlow=2910, sumFlow=4848]

。。。

7.2.2 需求2：将统计结果按照手机归属地不同省份输出到不同文件中（Partitioner）

0）需求：将统计结果按照手机归属地不同省份输出到不同文件中（分区）

1）数据准备  phone_date.txt

2）分析

（1）Mapreduce中会将map输出的kv对，按照相同key分组，然后分发给不同的reducetask。默认的分发规则为：根据key的hashcode%reducetask数来分发

（2）如果要按照我们自己的需求进行分组，则需要改写数据分发（分组）组件Partitioner

自定义一个CustomPartitioner继承抽象类：Partitioner

（3）在job驱动中，设置自定义partitioner： job.setPartitionerClass(CustomPartitioner.class)

3）在需求1的基础上，增加一个分区类

package com.xyg.mr.partitioner;
import java.util.HashMap;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

/**
 * K2 V2 对应的是map输出kv类型
* @author Administrator
*/
public class ProvincePartitioner extends Partitioner<Text, FlowBean> {
    @Override
    public int getPartition(Text key, FlowBean value, int numPartitions) {
// 1 获取电话号码的前三位
        String preNum = key.toString().substring(0, 3);
        
        int partition = 4;
        
        // 2 判断是哪个省
        if ("136".equals(preNum)) {
            partition = 0;
        }else if ("137".equals(preNum)) {
            partition = 1;
        }else if ("138".equals(preNum)) {
            partition = 2;
        }else if ("139".equals(preNum)) {
            partition = 3;
        }
        return partition;
    }
}

2）在驱动函数中增加自定义数据分区设置和reduce task设置

public static void main(String[] args) throws Exception {
        // 1 获取配置信息，或者job对象实例
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        // 6 指定本程序的jar包所在的本地路径
        job.setJarByClass(FlowCount.class);

        // 8 指定自定义数据分区
        job.setPartitionerClass(ProvincePartitioner.class);
        
        // 9 同时指定相应数量的reduce task
        job.setNumReduceTasks(5); 
        
        // 2 指定本业务job要使用的mapper/Reducer业务类
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);

        // 3 指定mapper输出数据的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        // 4 指定最终输出的数据的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        // 5 指定job的输入原始文件所在目录
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }

3）将程序打成jar包，然后拷贝到hadoop集群中。

4）启动hadoop集群

5）执行flowcountPartitionser程序

[root@node21 ~]$ hadoop jar flowcountPartitionser.jar com.xyg.mr.partitioner.FlowCount /user/root/flowcount/input /user/root/flowcount/output

6）查看结果

[root@node21 ~]]$ hadoop fs -lsr /

/user/root/flowcount/output/part-r-00000

/user/root/flowcount/output/part-r-00001

/user/root/flowcount/output/part-r-00002

/user/root/flowcount/output/part-r-00003

/user/root/flowcount/output/part-r-00004

7.2.3 需求3：将统计结果按照总流量倒序排序（全排序）

0）需求  根据需求1产生的结果再次对总流量进行排序。

1）数据准备      phone_date.txt

2）分析

（1）把程序分两步走，第一步正常统计总流量，第二步再把结果进行排序

（2）context.write(总流量，手机号)

（3）FlowBean实现WritableComparable接口重写compareTo方法

@Override
public int compareTo(FlowBean o) {
// 倒序排列，从大到小
return this.sumFlow > o.getSumFlow() ? -1 : 1;
}

package com.xyg.mr.sort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;

public class FlowBean implements WritableComparable<FlowBean> {

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

    // 反序列化时，需要反射调用空参构造函数，所以必须有
    public FlowBean() {
        super();
    }

    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 getSumFlow() {
        return sumFlow;
    }

    public void setSumFlow(long sumFlow) {
        this.sumFlow = sumFlow;
    }

    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;
    }

    /**
     * 序列化方法
     * @param out
     * @throws IOException
     */
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeLong(upFlow);
        out.writeLong(downFlow);
        out.writeLong(sumFlow);
    }

    /**
     * 反序列化方法 注意反序列化的顺序和序列化的顺序完全一致
     * @param in
     * @throws IOException
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        upFlow = in.readLong();
        downFlow = in.readLong();
        sumFlow = in.readLong();
    }

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

    @Override
    public int compareTo(FlowBean o) {
        // 倒序排列，从大到小
        return this.sumFlow > o.getSumFlow() ? -1 : 1;
    }
}

4）Map方法优化为一个对象，reduce方法则直接输出结果即可，驱动函数根据输入输出重写配置即可。3）FlowBean对象在在需求1基础上增加了比较功能

package com.xyg.mr.sort;
import java.io.IOException;
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.output.FileOutputFormat;

public class FlowCountSort {
    static class FlowCountSortMapper extends Mapper<LongWritable, Text, FlowBean, Text>{
        FlowBean bean = new FlowBean();
        Text v = new Text();
        
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            
            // 1 拿到的是上一个统计程序输出的结果，已经是各手机号的总流量信息
            String line = value.toString();
            
            // 2 截取字符串并获取电话号、上行流量、下行流量
            String[] fields = line.split("\t");
            String phoneNbr = fields[0];
            
            long upFlow = Long.parseLong(fields[1]);
            long downFlow = Long.parseLong(fields[2]);
            
            // 3 封装对象
            bean.set(upFlow, downFlow);
            v.set(phoneNbr);
            
            // 4 输出
            context.write(bean, v);
        }
    }
    
    static class FlowCountSortReducer extends Reducer<FlowBean, Text, Text, FlowBean>{
        
        @Override
        protected void reduce(FlowBean bean, Iterable<Text> values, Context context)
                throws IOException, InterruptedException {
            context.write(values.iterator().next(), bean);
        }
    }
    
    public static void main(String[] args) throws Exception {
        // 1 获取配置信息，或者job对象实例
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        // 6 指定本程序的jar包所在的本地路径
        job.setJarByClass(FlowCountSort.class);

        // 2 指定本业务job要使用的mapper/Reducer业务类
        job.setMapperClass(FlowCountSortMapper.class);
        job.setReducerClass(FlowCountSortReducer.class);

        // 3 指定mapper输出数据的kv类型
        job.setMapOutputKeyClass(FlowBean.class);
        job.setMapOutputValueClass(Text.class);

        // 4 指定最终输出的数据的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        // 5 指定job的输入原始文件所在目录
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        
        Path outPath = new Path(args[1]);
//        FileSystem fs = FileSystem.get(configuration);
//        if (fs.exists(outPath)) {
//            fs.delete(outPath, true);
//        }
        FileOutputFormat.setOutputPath(job, outPath);

        // 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

5）将程序打成jar包，然后拷贝到hadoop集群中。

6）启动hadoop集群5）将程序打成jar包，然后拷贝到hadoop集群中。

7）执行flowcountsort程序

[root@node21 module]$ hadoop jar flowcountsort.jar com.xyg.mr.sort.FlowCountSort /user/root/flowcount/output /user/root/flowcount/output_sort

8）查看结果

[root@node21 module]$ hadoop fs -cat /user/flowcount/output_sort/part-r-00000

13502468823 7335 110349 117684

13925057413 11058 48243 59301

13726238888 2481 24681 27162

13726230503 2481 24681 27162

18320173382 9531 2412 11943

7.2.4 需求4：不同省份输出文件内部排序（部分排序）

1）需求   要求每个省份手机号输出的文件中按照总流量内部排序。

2）分析   基于需求3，增加自定义分区类即可。

3）案例实操

（1）增加自定义分区类

package com.xyg.reduce.flowsort;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

public class FlowSortPartitioner extends Partitioner<FlowBean, Text> {

@Override
public int getPartition(FlowBean key, Text value, int numPartitions) {

int partition = 0;
String preNum = value.toString().substring(0, 3);

if (" ".equals(preNum)) {
partition = 5;
} else {
if ("136".equals(preNum)) {
partition = 1;
} else if ("137".equals(preNum)) {
partition = 2;
} else if ("138".equals(preNum)) {
partition = 3;
} else if ("139".equals(preNum)) {
partition = 4;
}
}
return partition;
 }
}

（2）在驱动类中添加分区类

job.setPartitionerClass(FlowSortPartitioner.class);
job.setNumReduceTasks(5);