五、MapReduce普通排序例子--统计手机号流量

1、需求

统计每一个手机号的总流量（上行流量+下行流量）、上行流量、下行流量，并且最后按照总流量进行手机号的排序。****

2、数据输入及输出格式

源数据比较敏感，这里就不展示出来了

输入格式为：

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

分隔符为“\t”

输出格式为：

手机号码        上行流量        下行流量        总流量

并且根据总流量的大小进行排序

3、思路分析

map阶段：
切分字段，以手机号为key，value为一个bean对象，value保存对应手机号的上下行流量、以及总流量；key保存手机号，也就是类似的结构：

<1234567,<上下行流量，总流量>>

reduce阶段：
对于同一个key的（即同一手机号）的上下行流量进行累加，获取总的上下行流量、总流量。
并且最后需要对总流量进行排序，所以reduce输出的key为整个bean，value为空

4、具体程序

FlowBean.java

/*用于保存流量数据的自定义可序列化类*/
package PhoneData;

import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.Setter;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;

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

@Getter
@Setter
@NoArgsConstructor
public class FlowBean implements WritableComparable<FlowBean> {
    /**  该类是一个可序列化类，且可比较，所以要实现 WritableComparable接口
     * 上传、下载、总流量
     */
    private int upFlow;
    private int downFlow;
    private int sumFlow;

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

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

    /**
     * 反序列化
     * @param dataInput
     * @throws IOException
     */
    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.upFlow = dataInput.readInt();
        this.downFlow = dataInput.readInt();
        this.sumFlow = dataInput.readInt();
    }

    /**
     * 打印字符串方法
     * @return
     */
    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append(this.upFlow);
        sb.append(" ");
        sb.append(this.downFlow);
        sb.append(" ");
        sb.append(this.sumFlow);
        return sb.toString();
    }

    /**
     * 对象的比较方法，用于排序比较
     * @param o
     * @return
     */
    @Override
    public int compareTo(FlowBean o) {
        return this.getSumFlow() > o.getSumFlow() ? -1 : 1;
    }
}

mapper：

package PhoneData;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class PhoneMapper 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 {
        String line = value.toString();
        String[] fields = line.split("\t");

        //开始解析切割数据
        k.set(fields[1]);
        int downFlow = Integer.parseInt(fields[fields.length - 2]);
        int upFlow = Integer.parseInt(fields[fields.length - 3]);
        v.setDownFlow(downFlow);
        v.setUpFlow(upFlow);
        v.setSumFlow(upFlow + downFlow);

        context.write(k, v);
    }
}

reducer：

package PhoneData;

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

import java.io.IOException;

public class PhoneReducer extends Reducer<Text, FlowBean, FlowBean, Text> {

    FlowBean v = new FlowBean();

    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
        int upFlow = 0;
        int downFlow = 0;
        int sumFlow = 0;

        //对上传、下载、总流量进行累加
        for (FlowBean f : values) {
            upFlow += f.getUpFlow();
            downFlow += f.getDownFlow();
            sumFlow += f.getSumFlow();
        }

        //将汇总的数据写到新的bean中，然后输出
        v.setUpFlow(upFlow);
        v.setDownFlow(downFlow);
        v.setSumFlow(sumFlow);

        context.write(v, key);
    }
}

driver：

package PhoneData;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.BZip2Codec;
import org.apache.hadoop.io.compress.CompressionCodec;
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;

public class PhoneDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args = new String[]{"G:\\test\\A\\phone_data.txt", "G:\\test\\A\\phonetest5\\"};

        Configuration conf = new Configuration();

        //获取job对象
        Job job = Job.getInstance(conf);

        //配置driver，map，reduce类
        job.setJarByClass(PhoneDriver.class);
        job.setMapperClass(PhoneMapper.class);
        job.setReducerClass(PhoneReducer.class);

        //指定map和reduce的输出类
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
        job.setOutputKeyClass(FlowBean.class);
        job.setOutputValueClass(Text.class);

        //指定输入数据，输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //提交job
        job.waitForCompletion(true);

    }
}