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1. 什么是序列化
<1>序列化就是把内存中的对象，转换成字节序列（或其他数据传输协议）以便于存储到磁盘（持久化）和网络传输。
<2>反序列化就是将收到字节序列（或其他数据传输协议）或者是磁盘的持久化数据，转换成内存中的对象。
2. 优势
<1>结构紧凑，存储空间少
<2>传输快速
<3>互操作性
3. 自定义bean对象序列化步骤
<1>实现Writable接口
<2>无参构造函数
反序列化时，需要反射调用空参数构造函数，所以必须有无参构造函数
<3>重写序列化方法
<4>重写反序列化方法
<5>反序列化的顺序和序列化的顺序要完全一致
<6>重写toString()
要想把结果显示在文件中，需要重写toString()，可以用”\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框架中的Fhuffle过程要求对key必须能排序
4. 序列化实例
<1>需求分析
- 统计
每个手机号耗费的总上行流量，总下行流量，总流量 - 输入数据
phone_data.txt - 输入数据格式
- 期望输出的数据格式
<2>过程分析
1. Map阶段
<1>读取一行数据，切分字段
<2>抽取手机号，上行流量，下行流量
<3>以手机号为key，bean对象为value输出，即context.write(手机号，bean);
<4> bean对象要想要能够传输，必须实现序列化接口
2. Reduce阶段
<1>累加上行流量和下行流量得到总流量
<3>编写程序
1. FlowBean
package com.demo.mapreduce.writable;import org.apache.hadoop.io.Writable;import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;/*** The type Flow bean.* 1. 定义类实现Writable接口* 2. 重写序列化反序列化接口* 3. 重写无参构造函数* 4. 重写toString方法*/
public class FlowBean implements Writable {private long upFlow;//上行流量private long downFlow;//下行流量private long sumFlow;//总流量//无参构造方法public FlowBean() {}//序列化方法@Overridepublic void write(DataOutput dataOutput) throws IOException {dataOutput.writeLong(upFlow);dataOutput.writeLong(downFlow);dataOutput.writeLong(sumFlow);}//反序列化方法@Overridepublic void readFields(DataInput dataInput) throws IOException {this.upFlow = dataInput.readLong();this.downFlow = dataInput.readLong();this.sumFlow = dataInput.readLong();}//toString方法@Overridepublic String toString() {return upFlow + "\t" + downFlow + "\t" + sumFlow;}//------------get set 方法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 setSumFlow() {this.sumFlow = this.upFlow + this.downFlow;}
}2. FlowMapper
package com.demo.mapreduce.writable;import org.apache.hadoop.io.IntWritable;
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();@Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {//1. 获取一行数据String line = value.toString();//2. 根据\t切分数据String[] paramArray = line.split("\t");//3. 抓取想要的数据 ,手机号，上行流量，下行流量String phoneNumber = paramArray[1];String upFlow = paramArray[paramArray.length - 3];String downFlow = paramArray[paramArray.length - 2];//4.封装outK.set(phoneNumber);outV.setUpFlow(Long.parseLong(upFlow));outV.setDownFlow(Long.parseLong(downFlow));outV.setSumFlow();//5. 写出context.write(outK, outV);}
}3. FlowReducer
package com.demo.mapreduce.writable;import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class FlowReducer extends Reducer<Text, FlowBean, Text, FlowBean> {private FlowBean outV = new FlowBean();@Overrideprotected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context) throws IOException, InterruptedException {long totalUp = 0;long totalDown = 0;long totalSum = 0;//1. 循环遍历集合，累加for (FlowBean value : values) {totalUp += value.getUpFlow();totalDown += value.getDownFlow();totalSum += value.getSumFlow();}//2. 封装outV.setUpFlow(totalUp);outV.setDownFlow(totalDown);outV.setSumFlow(totalSum);//3. 写出context.write(key, outV);}
}4. FlowDriver
package com.demo.mapreduce.writable;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 {//1. 获取jobConfiguration conf = new Configuration();Job job = Job.getInstance(conf);//2. 设置jar包路径job.setJarByClass(FlowDriver.class);//3. 关联mapper和reducerjob.setMapperClass(FlowMapper.class);job.setReducerClass(FlowReducer.class);//4. 设置map的输出的key 和 value 类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(FlowBean.class);// 5. 设置最终输出的key 和 value 的类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(FlowBean.class);// 6. 指定job的输入原始文件所在目录FileInputFormat.setInputPaths(job, new Path("E:\\javaworkspaces\\MapReduceDemo\\phone_data.txt"));//7.指定job的输出结果所在目录（不能提前存在）FileOutputFormat.setOutputPath(job, new Path("E:\\javaworkspaces\\MapReduceDemo\\phone_data_result"));// 8.提交作业boolean result = job.waitForCompletion(true);System.exit(result ? 0 : 1);}
}5. phone_data.txt
数据源
1 13736230513 192.196.100.1 www.atguigu.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.atguigu.com 1527 2106 200
6 84188413 192.168.100.3 www.atguigu.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.atguigu.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<4>运行结果
