MapReduce（实战）实现统计计数（传递对象Bean）

本套代码实现了对流量的统计

源数据：第2列为手机号，倒数2、3列为上行流量和下行流量，则我们要按照手机号为主键，统计出这个手机号一共的上行和下行流量。（数据是自己造的）

1363154400022	13926251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	240	10	200
1363154400023	13856251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	240	44	200
1363154400024	17856251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	274401	1034	200
1363154400025	13156251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	2404	450	200
1363154400026	13426251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	2406	460	200
1363154400027	13726251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	24046	6450	200
1363154400028	13926251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	24064	6450	200
1363154400029	13926251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	240	0	200
1363154400030	13926251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	240	0	200
1363154400031	13926251106	5C-0E-8B-B1-50:CMCC 120.17.81.234	twat.com	4	0	240	0	200

代码的主要问题是Map和Reduce的键值传递问题，需要同时传递上行和下行的数值。

解决思路是：以手机号为K，V传递一个自定义的类通过这个类的属性来同时传递上行和下行数据，键值对为：<手机号，类>

代码结构树：

1、自定义一个Bean类，需要实现序列化和反序列化，本代码继承的是Writable

package cn.ticast.flowsum;

import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;

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

public class FlowBean implements Writable {

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

    public FlowBean() {
    }

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


    public FlowBean(long upFlow, long downFlow) {
        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;
    }

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

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

    //这是反序列化方法
    //反序列时候  注意序列化的顺序
    //先序列化的先出来
    @Override
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readLong();
        this.downFlow=in.readLong();
        this.sumFlow=in.readLong();

    }

    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;
    }


    //这里就是Bean比较大小的方法
//    @Override
//    public int compareTo(FlowBean o) {
//        //实现按照总流量的倒序排序
//        return this.sumFlow >o.getSumFlow()?-1:1;
//        //正常逻辑  this.sumFlow >o.getSumFlow()?1:-1
//    }
}

2、编写Map阶段

package cn.ticast.flowsum;

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

import java.io.IOException;


/**
 * 在mr程序中，也可以使用我们自定义的类型作为mr的数据类型，前提是需要实现hadoop的序列化机制 writeable
 */
public 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 {
        String line = value.toString();

        String[] fields = line.split("\t");

        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);

    }
}

3、编写Reduce阶段

package cn.ticast.flowsum;

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

import java.io.IOException;

/**
 * <手机号1，bean><手机号2，bean><手机号1，bean><手机号2，bean><手机号，bean>
 *
 *  <手机号1，bean><手机号1，bean>
 *  <手机号2，bean><手机号2，bean>
 */
public class FlowSumReducer extends Reducer<Text,FlowBean,Text,FlowBean>{

    FlowBean v = new FlowBean();
    @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);
    }
}

4、指定Job

package cn.ticast.flowsum;

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;

public class FlowSumDriver {
    public static void main(String[] args) throws Exception{
        //通过Job来封装本次mr的相关信息
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //指定本次mr job jar包运行主类
        job.setJarByClass(FlowSumDriver.class);

        //指定本次mr 所用的mapper reducer类分别是什么
        job.setMapperClass(FlowSumMapper.class);
        job.setReducerClass(FlowSumReducer.class);

        //指定本次mr mapper阶段的输出  k  v类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        //指定本次mr 最终输出的 k v类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

//        job.setNumReduceTasks(3);

        //指定本次mr 输入的数据路径 和最终输出结果存放在什么位置
        FileInputFormat.setInputPaths(job,"D:\\Download\\input");
        FileOutputFormat.setOutputPath(job,new Path("D:\\Download\\outputsum"));

//        job.submit();
        //提交程序  并且监控打印程序执行情况
        boolean b = job.waitForCompletion(true);
        System.exit(b?0:1);
    }
}

使用本地模式运行，将数据放在了D:\\Download\\input下

运行结果：

MapReduce（实战）实现统计计数（传递对象Bean）

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