Hadoop的序列化和反序列化，和实例演示

目录

什么是序列化和反序列化？

hadoop 中常用数据的序列化类型 

 自定义bean对象实现序列化接口（Writable）

 序列化案例实操    

自定义类：FlowBean

Mapper类

Mapper

Driver

---

什么是序列化和反序列化？

序列化：将内存中的对象装换成字节序列，以便于持久化到硬盘和网络传输

反序列化：将接收到的字节序列或者是磁盘中的持久化数据转换成内存中的对象

在Hadoop中涉及到集群，集群件的需要进行大量的数据传输，所以对于Hadoop集群来说会有一个需求就是怎么样将A 机器内存中的数据传输到B 机器？这可以使用java自带的序列化框架，serializable；但是由于java自带的序列化会有很多额外的信息，不利于网络的传输，所以hadoop有自己的序列化机制 Writable。

hadoop 中常用数据的序列化类型 

表4-1 常用的数据类型对应的Hadoop数据序列化类型

Java类型	Hadoop Writable类型
Boolean	BooleanWritable
Byte	ByteWritable
Int	IntWritable
Float	FloatWritable
Long	LongWritable
Double	DoubleWritable
String	Text
Map	MapWritable
Array	ArrayWritable

 自定义bean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

（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。

 序列化案例实操    

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，数据如下：

1	13736230513	192.196.100.1	www.isea.com	2481	24681	200
2	13846544121	192.196.100.2			264	0	200
3 	13956435636	192.196.100.3			132	1512	200
4 	13966251146	192.168.100.1			240	0	404
5 	18271575951	192.168.100.2	www.isea.com	1527	2106	200
6 	84188413	192.168.100.3	www.isea.com	4116	1432	200
7 	13590439668	192.168.100.4			1116	954	200
8 	15910133277	192.168.100.5	www.hao123.com	3156	2936	200
9 	13729199489	192.168.100.6			240	0	200
10 	13630577991	192.168.100.7	www.shouhu.com	6960	690	200
11 	15043685818	192.168.100.8	www.baidu.com	3659	3538	200
12 	15959002129	192.168.100.9	www.isea.com	1938	180	500
13 	13560439638	192.168.100.10			918	4938	200
14 	13470253144	192.168.100.11			180	180	200
15 	13682846555	192.168.100.12	www.qq.com	1938	2910	200
16 	13992314666	192.168.100.13	www.gaga.com	3008	3720	200
17 	13509468723	192.168.100.14	www.qinghua.com	7335	110349	404
18 	18390173782	192.168.100.15	www.sogou.com	9531	2412	200
19 	13975057813	192.168.100.16	www.baidu.com	11058	48243	200
20 	13768778790	192.168.100.17			120	120	200
21 	13568436656	192.168.100.18	www.alibaba.com	2481	24681	200
22 	13568436656	192.168.100.19			1116	954	200

期望的值是：

13560436666             1116          954                      2070

手机号码               上行流量        下行流量                  总流量

构思过程：

代码实现：

自定义类：FlowBean

package com.isea.flow;

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

//   无参构造方法，反序列化时候需要用到
    public FlowBean() {
    }

    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;
    }

    public void set(Long upFlow,Long downFlow){
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        sumFlow = upFlow + downFlow;
    }

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

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

//    反序列化方法
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readLong();
        this.downFlow = in.readLong();
        this.sumFlow = in.readLong();
    }
}

Mapper类

package com.isea.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 {
    // 键值对的准备
    private Text phone = new Text();
    private FlowBean flowBean = new FlowBean();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//        1,获取一行
        String line = value.toString();

//        2，切割数据 "\t"
        String[] field = line.split("\t");

//        3，封装对象
        phone.set(field[1]);

        long upFlow = Long.parseLong(field[field.length - 3]);
        long downFlow = Long.parseLong(field[field.length - 2]);
        flowBean.setUpFlow(upFlow);
        flowBean.setDownFlow(downFlow);

//        4，写给reduce
        context.write(phone,flowBean);
    }
}

Reducer

package com.isea.flow;

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

public class FlowReducer extends Reducer {
    private FlowBean resultFlowBean = new FlowBean();

    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        long sumUp = 0;
        long sumDown = 0;

//        1，累加求和
        for (FlowBean value : values) {
            sumDown += value.getDownFlow();
            sumUp += value.getUpFlow();
        }
        resultFlowBean.set(sumUp,sumDown);

//        2，输出
        context.write(key,resultFlowBean);
    }
}

Driver

package com.isea.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 FlowSumDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        args = new String[]{"G:/input","G:/output2"};
//    1,获取job对象
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

//    2，设置jar的路径
        job.setJarByClass(FlowSumDriver.class);

//    3，关联Mapper和Reducer
        job.setMapperClass(FlowMapper.class);
        job.setReducerClass(FlowReducer.class);

//    4，设置Mapper输出的key和value类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
//    5，设置最终的输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

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

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

运行之后的结果如下：

13470253144	180	180	360
13509468723	7335	110349	117684
13560439638	918	4938	5856
13568436656	3597	25635	29232
13590439668	1116	954	2070
13630577991	6960	690	7650
13682846555	1938	2910	4848
13729199489	240	0	240
13736230513	2481	24681	27162
13768778790	120	120	240
13846544121	264	0	264
13956435636	132	1512	1644
13966251146	240	0	240
13975057813	11058	48243	59301
13992314666	3008	3720	6728
15043685818	3659	3538	7197
15910133277	3156	2936	6092
15959002129	1938	180	2118
18271575951	1527	2106	3633
18390173782	9531	2412	11943
84188413	4116	1432	5548