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深入理解 Hadoop 序列化

[日期:2020-03-31] 来源:Linux社区  作者:wnwn [字体: ]

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<LongWritable, Text,Text,FlowwBean> {

    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<Text,FlowwBean,Text,FlowwBean> {

    private FlowwBean sumFlow=new FlowwBean();

    @Override
    protected void reduce(Text key, Iterable<FlowwBean> 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

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