0
点赞
收藏
分享

微信扫一扫

自定义bean对象实现序列化接口(Writable)以及实现案例

吴陆奇 2022-02-24 阅读 36
hadoopapacheide


  2. 自定义bean对象序列化传输必须注意

(1) 实现Writable接口

(2)反序列化时,需要反射调用空构造函数,所以必须有空构造

​​ 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)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(),可以用"\t"分开,以便后续用

(7)如果需要将自定义的bean放key中传输,则还要实现Comparable接口,因为MapReduce框中的shuffle过程一定会对key排序

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

对象

package com.zyd.flowsum;

  import java.io.DataInput;
  import java.io.DataOutput;
  import java.io.IOException;
  import java.util.Set;

  import org.apache.hadoop.io.Writable;
  /**
   * Bean对象要需要实现Writable接口
   * @author Administrator
   *
   */
  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

    //必须要有空参构造,为了后续反射用
    public FlowBean() {
      super();
    }


    public FlowBean(long upFlow, long downFlow) {
      super();
      this.upFlow = upFlow;
      this.downFlow = downFlow;
      this.sumFlow = upFlow+downFlow;//总流量
    }

    //负责累加上行流量和下行流量
      public void set(long upFlow, long downFlow) {
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow+downFlow;//总流量
      }

    //改造toString方法,方便后续处理数据
    @Override
    public String toString() {
      return  upFlow+"\t"+ downFlow+ "\t" + sumFlow;
    }


    //反序列化方法
    @Override
    public void readFields(DataInput in) throws IOException {
      //顺序一致
      this.upFlow = in.readLong();
      this.downFlow = in.readLong();
      this.sumFlow = in.readLong();

    }


    //序列化方法
    @Override
    public void write(DataOutput out) throws IOException {
      //顺序一致
      out.writeLong(upFlow);
      out.writeLong(downFlow);
      out.writeLong(sumFlow);
    }


    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;
    }



    //由于对象是作为value传输,所以不重写compareTo方法 key是手机号

  }

Mapper类

package com.zyd.flowsum;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
//输入:行号 LongWritable 行内容Text 输出 key:手机号 Text value:FlowBean
public class FlowMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
  FlowBean v = new FlowBean();
  Text k = new Text();


  @Override
  protected void map(LongWritable key, Text value,
      Context context)
      throws IOException, InterruptedException {
    //1363157993055 13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15  1116  954  200
    //1 获取一行
    String line = value.toString();

    //2 切割
    String[] fields= line.split("\t");
    //3 封装对象
    //手机号
    String phoneNum = fields[1];
    //上行流量
    long upFlow = Long.parseLong(fields[fields.length-3]);
    //下行流量
    long downFlow = Long.parseLong(fields[fields.length-2]);


    k.set(phoneNum);

    v.set(upFlow, downFlow);
    //4 写出数据
    context.write(k, v);
  }
}

Reducer

package com.zyd.flowsum;

import java.io.IOException;

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

public class FlowReducer extends Reducer<Text, FlowBean, Text, FlowBean> {

  @Override
  protected void reduce(Text key, Iterable<FlowBean> values,
      Context context)
      throws IOException, InterruptedException {
    //1 累加求和 相同的求和操作
    //对整个数据上行流量和下行流量的求和
    long sum_upFlow = 0;
    long sum_downFlow = 0;
    for (FlowBean flowBean : values) {
      sum_upFlow += flowBean.getUpFlow();
      sum_downFlow += flowBean.getDownFlow();
    }
    //2. 输出
    context.write(key, new FlowBean(sum_upFlow,sum_downFlow));
  }
}

Driver

package com.zyd.flowsum;

  import java.io.IOException;

  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;

  public class FlowDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
      //1 获取job对象
      Configuration conf = new Configuration();
      Job job = Job.getInstance(conf);
      //2 设置jar包路径
      job.setJarByClass(FlowDriver.class);
      //3 管理类mapper和Reducer
      job.setMapperClass(FlowMapper.class);
      job.setReducerClass(FlowReducer.class);
      //4 设置mapper输出k,v类型
      job.setMapOutputKeyClass(Text.class);
      job.setMapOutputValueClass(FlowBean.class);
      //5 设置最终输出k,v类型
      job.setOutputKeyClass(Text.class);
      job.setOutputValueClass(FlowBean.class);
      //6 设置输入输出路径
      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);
    }
  }

原数据

1363157993055 13565436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1514  92054 200
1363157993055   13560436866   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1916  914 200
1363157993055   13568436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11616 95  200
1363157993055   13760436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11126 95420 200
1363157993055   13564436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11856 954 200
1363157993055   13561536666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11685 952 200
1363157993055   13560485666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1916  9504  200
1363157993055   13560436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1616  1954  200
1363157993055   13560463666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1216  9354  200
1363157993055   13560488666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1616  9543  200
1363157993055   13560436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1316  95420 200
1363157993055   13560436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1316  95474 200
1363157993055   13560445666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    16616 95489 200
1363157993055   13560436856   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11136 9524  200
1363157993055   13560478666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11162 9554  200
1363157993055   13560445666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    16616 95489 200
1363157993055   13560436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11165 9954  200
1363157993055   13566436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11126 9534  200
1363157993055   13460436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1162  9054  200
1363157993055   13560936666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11201 91254 200
1363157993055   13560445666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    16616 95489 200
1363157993055   13560436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1616  1954  200
1363157993055   13560436866   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    1916  952 200
1363157993055   13568436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11616 95  200
1363157993055   13760436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11126 9542  200
1363157993055   13560445666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    16616 95489 200
1363157993055   13564436666   C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18  15    11856 95452 200



举报

相关推荐

自定义Jackson序列化 @JsonSerialize

@JsonSerializeJacksonjsonxmlide编程语言

自定义bean对象实现日志转换parquet

sparksqlandroid

MapReduce_Writable序列化

arcgisarcgis pro sdkc#gis

【Kotlin】自定义Json反序列化

学习笔记javaweb

kafka 配置自定义序列化方式

Elasticsearch大数据搜索引擎高效搜索数据分析可扩展性电商平台、新闻媒体、企业内部数据库

springboot自定义Json序列化返回，实现自动转换字典值

spring bootjsonjava

SpringBoot整合Redis实现几种自定义数据序列化存储方式

java数据库redis

springsecurity中的自定义反序列化

linqelementuipostman

Go自定义数据的序列化流程

golang开发语言后端

jack反序列化自定义字段绑定

jsonjavaxml编程语言
0 条评论