Hadoop_day05_MapReduce 的 经典案例（流量统计）

需求一：统计求和

       统计每个手机号的上行数据包总和，下行数据包总和，上行总流量之和，下行总流量之和分析：以手机号码作为key值，上行流量，下行流量，上行总流量，下行总流量四个字段作为value值，然后以这个key，和value作为map阶段的输出，reduce阶段的输入

1.1 自定义SumBean

public class SumBean implements Writable{

    private Integer upFlow;
    private Integer downFlow;
    private Integer upCountFlow;
    private Integer downCountFlow;

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

    public Integer getUpFlow() {
        return upFlow;
    }

    public void setUpFlow(Integer upFlow) {
        this.upFlow = upFlow;
    }

    public Integer getDownFlow() {
        return downFlow;
    }

    public void setDownFlow(Integer downFlow) {
        this.downFlow = downFlow;
    }

    public Integer getUpCountFlow() {
        return upCountFlow;
    }

    public void setUpCountFlow(Integer upCountFlow) {
        this.upCountFlow = upCountFlow;
    }

    public Integer getDownCountFlow() {
        return downCountFlow;
    }

    public void setDownCountFlow(Integer downCountFlow) {
        this.downCountFlow = downCountFlow;
    }

    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeInt(upFlow);
        dataOutput.writeInt(downFlow);
        dataOutput.writeInt(upCountFlow);
        dataOutput.writeInt(downCountFlow);
    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.upFlow = dataInput.readInt();
        this.downFlow = dataInput.readInt();
        this.upCountFlow = dataInput.readInt();
        this.downCountFlow = dataInput.readInt();
    }
}

1.2 Mapper

public class SumMapper extends Mapper<LongWritable,Text,Text,SumBean> {
    @Override
    protected void map(LongWritable key, Text value, Con