第二章 MapReduce序列化案例

2.1 自定义FloBean对象实现序列化接口（`Writable`）

在企业开发中往往常用的基本序列化类型不能满足所有需求，比如在Hadoop框架内部传递一个Bean对象，那么该对象就需要实现序列化接口。

具体实现Bean对象序列化步骤如下7步。

(1) 必须实现Writable接口

public class FlowBean implements Writable {
	...
}

(2) 反序列化时，需要反射调用空参构造函数，所以必须有空参构造

public FlowBean() {
	super();
}

(3) 重写序列化方法

@Override
public void write(DataOutput out) throws IOException {
	out.writeLong(upFlow);
	out.writeLong(downFlow);
	out.writeLong(sumFlow);
}

(4) 重写反序列化方法

@Override
public void readFields(DataInput in) throws IOException {
	upFlow = in.readLong();
	downFlow = in.readLong();
	sumFlow = in.readLong();
}

(5) 注意反序列化的顺序和序列化的顺序完全一致
(6) 要想把结果显示在文件中，需要重写toString()，可用”\t”分开，方便后续用。
(7) 如果需要将自定义的Bean放在key中传输，则还需要实现Comparable接口，因为MapReduce框中的Shuffle过程要求对key必须能排序。详见后面排序案例。

@Override
public int compareTo(FlowBean o) {
	// 倒序排列，从大到小
	return this.sumFlow > o.getSumFlow() ? -1 : 1;
}

2.2 序列化案例实操

2.3.1 需求

统计每一个手机号耗费的总上行流量、下行流量、总流量。

(1) 输入数据
从phone_data.txt文件中输入数据。
(2) 输入数据格式：
(3) 期望输出数据格式

2.3.2 需求分析

在这里插入图片描述

2.3.3 编写MapReduce程序

(1) 编写流量统计的Bean对象

import org.apache.hadoop.io.Writable;
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() {

    }

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

    public void setFlow(long upFlow, long downFlow) {
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow + downFlow;
    }

    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
    * @Param out 框架给我们提供的数据出口
    * */
    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);
    }

    /*
     * 反序列化方法
     * @Param in 框架给我们提供的数据l来源
     * */
    @Override
    public void readFields(DataInput dataInput) throws IOException {
        upFlow = dataInput.readLong();
        downFlow = dataInput.readLong();
        sumFlow = dataInput.readLong();
    }
}

(2) 编写Mapper类

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<LongWritable, Text, Text, FlowBean> {

    private Text outK = new Text();
    private FlowBean outV = new FlowBean();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split("\t");
        String phone = fields[1];
        long upFlow = Long.parseLong(fields[fields.length - 3]);
        long downFlow = Long.parseLong(fields[fields.length - 2]);

        outK.set(phone);
        outV.setFlow(upFlow, downFlow);
        context.write(outK, outV);
    }
}

(3) 编写Reducer类

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;

public class WcReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

    int sum;
    private IntWritable total = new IntWritable();

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {

        // 1 累加求和
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }

        // 2 包装结果并输出
        total.set(sum);
        context.write(key, total);
    }
}

(4) 编写Driver驱动类

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 {

        // 1 获取配置信息以及封装任务
        Job job = Job.getInstance(new Configuration());

        // 2 设置jar加载路径
        job.setJarByClass(FlowDriver.class);

        // 3 设置map和reduce类
        job.setMapperClass(FlowMapper.class);
        job.setReducerClass(FlowReducer.class);

        // 4 设置map输出
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        // 5 设置最终输出kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        // 6 设置输入和输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 7 提交
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}