深入理解 Hadoop 序列化-演道网
2022 年 1 月 12 日
1.序列化概述
1.1 什么是序列化
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;
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;
1.2 为什么要序列化
一般来说,“活的”对象只生存在内存中,关机断电就没有了;而且“活的”对象只能由本地的进程使用,不能发送到网络上的另外一台计算机;然而序列化可以存储“活的”对象,可以将“活的”对象发送到远程计算机;
1.3 为甚不用Java的序列化
Java的序列化是一个重量级序列化框架(Serializable),一个对象被序列化后,会附带很多额外的信息(各种效验信息,Header,继承体系等),不便于在网络中高效传输,所以,Hadoop自己开发了一套序列化机制(Writable);
1.4 hadoop序列化特点
1.4.1 紧凑:高效使用存储空间;
1.4.2 快速:读写数据的额外开销小;
1.4.3 可扩展:随着通信协议的升级而可升级;
1.4.4 互操作:支持多语言的交互;
2.自定义bean对象实现序列接口(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;
2.1 必须实现Writable接口;
2.2 反序列化,需要反射调用空参构造函数,所以必须有空参构造;
public FlowBean() {
super();
}
2.3 重写序列化方法
/*序列化方法
* dataOutput 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);
}
2.4 重写反序列化方法
/*反序列化方法
* dataInput 框架提供的数据来源
* */
@Override
public void readFields(DataInput dataInput) throws IOException {
upFlow=dataInput.readLong();
downFlow=dataInput.readLong();
sumFlow=dataInput.readLong();
}
3.案例
3.1 编写FlowwBean
package com.wn.flow;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class FlowwBean implements Writable {
private long upFlow;
private long downFlow;
private long sumFlow;
public FlowwBean() {
}
@Override
public String toString() {
return "FlowwBean{" +
"upFlow=" + upFlow +
", downFlow=" + downFlow +
", sumFlow=" + sumFlow +
'}';
}
public void set(long upFlow, long downFlow){
this.upFlow=upFlow;
this.downFlow=downFlow;
this.sumFlow=upFlow+downFlow;
}
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 long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
/*序列化方法
* dataOutput 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);
}
/*顺序要完全一致*/
/*反序列化方法
* dataInput 框架提供的数据来源
* */
@Override
public void readFields(DataInput dataInput) throws IOException {
upFlow=dataInput.readLong();
downFlow=dataInput.readLong();
sumFlow=dataInput.readLong();
}
}
3.2 编写FlowMapper
package com.wn.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 FlowwBean flow=new FlowwBean();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] split = value.toString().split("\t");
phone.set(split[1]);
long upFlow = Long.parseLong(split[split.length - 3]);
long downFlow = Long.parseLong(split[split.length - 2]);
flow.set(upFlow,downFlow);
context.write(phone,flow);
}
}
3.3 编写FlowReducer
package com.wn.flow;
import org.apache.hadoop.mapreduce.Reducer;
import javax.xml.soap.Text;
import java.io.IOException;
public class FlowReducer extends Reducer {
private FlowwBean sumFlow=new FlowwBean();
@Override
protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
long sumUpFlow=0;
long sumDownFlow=0;
for (FlowwBean value:values){
sumUpFlow+=value.getUpFlow();
sumDownFlow+=value.getDownFlow();
}
sumFlow.set(sumUpFlow,sumDownFlow);
context.write(key,sumFlow);
}
}
3.4 编写FlowDriver
package com.wn.flow;
import com.wn.wordcount.WcDriver;
import com.wn.wordcount.WcMapper;
import com.wn.wordcount.WcReducer;
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, ClassNotFoundException, InterruptedException {
//获取一个Job实例
Job job = Job.getInstance(new Configuration());
//设置类路径
job.setJarByClass(WcDriver.class);
//设置mapper和reducer
job.setMapperClass(FlowMapper.class);
job.setReducerClass(FlowReducer.class);
//设置mapper和reducer输出类型
job.setMapOutputKeyClass(org.apache.hadoop.io.Text.class);
job.setMapOutputValueClass(FlowwBean.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowwBean.class);
//设置输入的数据
FileInputFormat.setInputPaths(job,new Path(args[0]));
FileOutputFormat.setOutputPath(job,new Path(args[1]));
//提交job
boolean b = job.waitForCompletion(true);
System.exit(b?0:1);
}
}
更多Hadoop相关信息见Hadoop 专题页面 https://www.linuxidc.com/topicnews.aspx?tid=13
Linux公社的RSS地址:https://www.linuxidc.com/rssFeed.as
转载自演道,想查看更及时的互联网产品技术热点文章请点击http://go2live.cn