hadoop序列化實現案例代碼

更新時間：2024年10月11日 11:00:53 作者：chen18677338530

序列化想必大家都很熟悉了,對象在進行網絡傳輸過程中,需要序列化之后才能傳輸到客戶端,或者客戶端的數據序列化之后送達到服務端,本文將為大家介紹Hadoop如何實現序列化,需要的可以參考一下

Hadoop序列化特點

緊湊：高效實用存儲空間
快速：讀寫數據額外開銷小
可擴展：隨著通信協(xié)議的升級而可以升級
互操作：支持多種語言的交互

自定義Bean對象實現序列化

必須實現Writable接口
反序列化時，需要反射調用無參構造函數
如果需要將自定義的bean放在key中傳輸，則還需要實現Comparable接口

案例

package com.chen.phoneproject;

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 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 FlowBean() {
        super();
    }

    public FlowBean(long upFlow, long downFlow) {
        super();
        this.upFlow = upFlow;
        this.downFlow = downFlow;
    }

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

    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);
    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.upFlow = dataInput.readLong();
        this.downFlow = dataInput.readLong();
        this.sumFlow = dataInput.readLong();
    }

    @Override
    public String toString() {
        return "FlowBean{" +
                "upFlow=" + upFlow +
                ", downFlow=" + downFlow +
                ", sumFlow=" + sumFlow +
                '}';
    }
}

package com.chen.phoneproject;

import lombok.extern.slf4j.Slf4j;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

@Slf4j
public class FlowCountMapper extends Mapper<LongWritable, Text,Text,FlowBean> {

    Text k = new Text();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        log.info("---mapper---"+"key:"+key+",value:"+value);
        String line = value.toString();

        String[] fields = line.split("\t");

        String phoneNum = fields[1];
        long upFlow = Long.parseLong(fields[3]);
        long downFlow = Long.parseLong(fields[4]);

        k.set(phoneNum);
        FlowBean bean = new FlowBean(upFlow,downFlow);

        context.write(k,bean);
    }
}

package com.chen.phoneproject;

import lombok.extern.slf4j.Slf4j;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;


@Slf4j
public class FlowCountReducer extends Reducer<Text,FlowBean,Text,FlowBean> {


    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {

       log.info("---reduce---"+"key:"+key+",value:"+values);
        long sum_upFlow = 0;
        long sum_downFlow = 0;

        for (FlowBean flowBean:values){
            sum_upFlow += flowBean.getUpFlow();
            sum_downFlow += flowBean.getDownFlow();
        }

        FlowBean result = new FlowBean(sum_upFlow,sum_downFlow,sum_downFlow + sum_upFlow);

        context.write(key,result);
    }
}

package com.chen.phoneproject;

import com.chen.mapreduce.WordcountDriver;
import com.chen.mapreduce.WordcountMapper;
import com.chen.mapreduce.WordcountReducer;
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;

public class FlowsumDriver {

    public static void main(String[] args) throws Exception {

        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        job.setJarByClass(FlowsumDriver.class);

        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        FileInputFormat.setInputPaths(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));

        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);
    }
}