mapreduce--流量统计

FlowBean

package com.atguigu.mr.flow;
 
import org.apache.hadoop.io.Writable;
 
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
 
public class FlowBean implements Writable {
//    流量
    private long upFlow;
    private long downFlow;
    private long sumFlow;
 
    public void set(long upFlow,long downFlow){
        this.upFlow=upFlow;
        this.downFlow=downFlow;
        this.sumFlow=upFlow+downFlow;
    }
 
 
    @Override
    public String toString() {
        return upFlow +"\t"+downFlow+"\t"+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;
    }
 
    public long getSumFlow() {
        return sumFlow;
    }
 
    public void setSumFlow(long sumFlow) {
        this.sumFlow = sumFlow;
    }
 
    /**
     *
     * 序列化方法，框架调用该方法将数据序列化到执行缓存
     * @param dataOutput  框架给我们的装数据的箱子。
     * @throws IOException
     */
 
    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);
 
    }
 
    /**
     *反序列化方法，框架调用这个方法将数据从箱子里面取出来
     * @param dataInput 装数据的箱子
     * @throws IOException
     */
 
    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.upFlow=dataInput.readLong();
        this.downFlow=dataInput.readLong();
        this.sumFlow=dataInput.readLong();
 
    }
}

FlowDriver

package com.atguigu.mr.flow;
 
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;
 
import java.io.IOException;
 
public class Flowdriver {
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
        Job job =Job.getInstance(new Configuration());
 
        job.setMapperClass(Flowmapper.class);
        job.setReducerClass(FlowReduce.class);
 
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
 
        FileInputFormat.setInputPaths(job,new Path("F:\\input"));
        FileOutputFormat.setOutputPath(job,new Path("F:\\aa\\output"));
 
        boolean b=job.waitForCompletion(true);
        System.exit(b?0:1);
 
 
    }
}

Flowmapper、

package com.atguigu.mr.flow;
 
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
 
import java.io.IOException;
 
public class Flowmapper extends Mapper {
    private Text phone =new Text();
    private FlowBean flowBean =new FlowBean();
    /**
     * 对数据进行封装
     * @param key
     * @param value
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
 
 
    @Override
    protected void map(LongWritable key, Text value, Mapper.Context context) throws IOException, InterruptedException {
//        super.map(key, value, context);
//            拿到一行数据 按照\t切分
        String[] fields=value.toString().split("\t");
 
//        封装手机号
        phone.set(fields[1]);
        flowBean.set(
                Long.parseLong(fields[fields.length-3]),
                Long.parseLong(fields[fields.length-2])
        );
 
//        将phone和手机号输出
        context.write(phone,flowBean);
 
    }
 
}

Flowreduce

package com.atguigu.mr.flow;
 
import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
 
import java.io.IOException;
 
public class FlowReduce extends Reducer {
    private FlowBean result=new FlowBean();
    /**
     * 按照手机号进行分组，--然后在这里累加
     * @param key 手机号
     * @param values 手机号所有的流量
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void reduce(Text key, Iterable values, Reducer.Context context) throws IOException, InterruptedException {
//        super.reduce(key, values, context);
//        讲一个手机号的所有流量进行累加
        long sumUpFlow =0;
        long sumDownFlow=0;
        for(FlowBean value:values){
            sumUpFlow+=value.getUpFlow();
            sumDownFlow+=value.getDownFlow();
        }
 
        result.set(sumUpFlow,sumDownFlow);
//        将累加的流量输出
        context.write(key,result);
 
    }
}