承接上文：​​Hadoop基础---流量求和MapReduce程序及自定义数据类型​​ 一：实验数据

对上一篇文章中的数据进行排序处理：



13480253104    180    200    380
13502468823    102    7335    7437
13560439658    5892    400    6292
13600217502    186852    200    187052
13602846565    12    1938    1950
13660577991    9    6960    6969
13719199419    0    200    200
13726230503    2481    24681    27162
13760778710    120    200    320
13823070001    180    200    380
13826544101    0    200    200
13922314466    3008    3720    6728
13925057413    63    11058    11121
13926251106    0    200    200
13926435656    1512    200    1712
15013685858    27    3659    3686
15920133257    20    3156    3176
15989002119    3    1938    1941
18211575961    12    1527    1539
18320173382    18    9531    9549
84138413    4116    1432    5548


二：MapReduce程序编写

Hadoop基础---MapReduce对数据进行排序_apache

（一）自定义数据结构FlowBean编写



package cn.hadoop.mr.wc;

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 String phoneNB;
    private long up_flow;
    private long down_flow;
    private long sum_flow;

    public FlowBean() {}    //无参构造函数，用于反序列化时使用

    public FlowBean(String phoneNB, long up_flow, long down_flow) {
        this.phoneNB = phoneNB;
        this.up_flow = up_flow;
        this.down_flow = down_flow;
        this.sum_flow = up_flow + down_flow;
    }


    public String getPhoneNB() {
        return phoneNB;
    }

    public void setPhoneNB(String phoneNB) {
        this.phoneNB = phoneNB;
    }

    public long getUp_flow() {
        return up_flow;
    }

    public void setUp_flow(long up_flow) {
        this.up_flow = up_flow;
    }

    public long getDown_flow() {
        return down_flow;
    }

    public void setDown_flow(long down_flow) {
        this.down_flow = down_flow;
    }

    public long getSum_flow() {
        return up_flow + down_flow;
    }


    //用于序列化
    @Override
    public void write(DataOutput out) throws IOException {
        // TODO Auto-generated method stub
        out.writeUTF(phoneNB);
        out.writeLong(up_flow);
        out.writeLong(down_flow);
        out.writeLong(up_flow+down_flow);
    }

    //用于反序列化
    @Override
    public void readFields(DataInput in) throws IOException {
        // TODO Auto-generated method stub
        phoneNB = in.readUTF();
        up_flow = in.readLong();
        down_flow = in.readLong();
        sum_flow = in.readLong();
    }

    @Override
    public int compareTo(FlowBean o) {　　//用于排序操作
        return sum_flow > o.sum_flow ? -1 : 1;    //返回值为-1，则排在前面
    }

    @Override
    public String toString() {
        return "" + up_flow + "\t" + down_flow + "\t"+ sum_flow;
    }


}


（二）Map程序编写



package cn.hadoop.rs;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import cn.hadoop.mr.wc.FlowBean;

public class ResSortMapper extends Mapper<LongWritable, Text, FlowBean, NullWritable>{
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, FlowBean, NullWritable>.Context context)
            throws IOException, InterruptedException {
        //获取一行数据
        String line = value.toString();
        //进行文本分割
        String[] fields = StringUtils.split(line, '\t');
        //数据获取
        String phoneNB = fields[0];
        long up_flow = Long.parseLong(fields[1]);
        long down_flow = Long.parseLong(fields[2]);

        context.write(new FlowBean(phoneNB, up_flow, down_flow), NullWritable.get());
    }
}


（三）Reduce程序编写



package cn.hadoop.rs;

import java.io.IOException;

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

import cn.hadoop.mr.wc.FlowBean;

//会在reduce接收数据时，对key进行排序
public class ResSortReducer extends Reducer<FlowBean, NullWritable, Text, FlowBean>{
    @Override
    protected void reduce(FlowBean key, Iterable<NullWritable> values,
            Reducer<FlowBean, NullWritable, Text, FlowBean>.Context context) throws IOException, InterruptedException {
        String phoneNB = key.getPhoneNB();
        context.write(new Text(phoneNB), key);
    }
}


注意：排序比较会在Reduce接收到key时进行排序，所以我们需要对输入的key进行处理

（四）主函数进行调用



package cn.hadoop.rs;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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 cn.hadoop.mr.wc.FlowBean;

public class ResSortRunner {

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

        job.setJarByClass(ResSortRunner.class);

        job.setMapperClass(ResSortMapper.class);
        job.setReducerClass(ResSortReducer.class);

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

        job.setMapOutputKeyClass(FlowBean.class);
        job.setMapOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true)?0:1);
    }
}


（五）结果测试



hadoop jar rs.jar cn.hadoop.rs.ResSortRunner /fs/output1 /fs/output6


Hadoop基础---MapReduce对数据进行排序_hadoop_02

三：实现将两个job在main中一次执行 

Hadoop基础---MapReduce对数据进行排序_mapreduce_03

 

（一）修改main方法，实现连续调用两个job



package cn.hadoop.rs;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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 cn.hadoop.fs.FlowSumMapper;
import cn.hadoop.fs.FlowSumReducer;
import cn.hadoop.fs.FlowSumRunner;
import cn.hadoop.mr.wc.FlowBean;

public class ResSortRunner {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf1 = new Configuration();
        Job job1 = Job.getInstance(conf1);

        job1.setJarByClass(FlowSumRunner.class);

        job1.setMapperClass(FlowSumMapper.class);
        job1.setReducerClass(FlowSumReducer.class);

        job1.setOutputKeyClass(Text.class);
        job1.setOutputValueClass(FlowBean.class);

        job1.setMapOutputKeyClass(Text.class);
        job1.setMapOutputValueClass(FlowBean.class);

        FileInputFormat.setInputPaths(job1, new Path(args[0]));
        FileOutputFormat.setOutputPath(job1, new Path(args[1]));

        if(!job1.waitForCompletion(true)) {
            System.exit(1);
        }

        Configuration conf2 = new Configuration();
        Job job2 = Job.getInstance(conf2);

        job2.setJarByClass(ResSortRunner.class);

        job2.setMapperClass(ResSortMapper.class);
        job2.setReducerClass(ResSortReducer.class);

        job2.setOutputKeyClass(Text.class);
        job2.setOutputValueClass(FlowBean.class);

        job2.setMapOutputKeyClass(FlowBean.class);
        job2.setMapOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job2, new Path(args[1]));
        FileOutputFormat.setOutputPath(job2, new Path(args[2]));

        System.exit(job2.waitForCompletion(true)?0:1);
    }
}


（二）实验测试，结果查看



hadoop jar rs.jar  cn.hadoop.rs.ResSortRunner /fs/input /fs/outdata1 /fs/outdata2


Hadoop基础---MapReduce对数据进行排序_hadoop_04

（三）补充：使用时，不推荐这种方法。中间结果单独输出，使用shell将各个程序串联，灵活性更大，更容易调试