1 hadoop自己的序列化

因为java中的序列化有太多的冗余信息，所以hadoop采用了自己的序列化机制。

2 hadoop实现

实现hadoop的序列化只需要实现接口org.apache.hadoop.io.Writable，然后重写两个方法。
如：

package com.jiyx.test.mapred.flowStatistics.bo;

import org.apache.hadoop.io.Writable;

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

/**
 * @author jiyx
 * @create 2018-10-15-19:22
 */
public class DataBean implements Writable {

    private long phoneNum;

    private long upFlow;

    private long downFlow;

    private long totalFlow;

    public DataBean() {
    }

    public DataBean(long phoneNum, long upFlow, long downFlow) {
        this.phoneNum = phoneNum;
        this.downFlow = downFlow;
        this.upFlow = upFlow;
        this.totalFlow = upFlow + downFlow;
    }

    /**
     * 序列化
     * @param dataOutput
     * @throws IOException
     */
    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(phoneNum);
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(totalFlow);
    }

    /**
     * 反序列化
     * @param dataInput
     * @throws IOException
     */
    @Override
    public void readFields(DataInput dataInput) throws IOException {
        phoneNum = dataInput.readLong();
        upFlow = dataInput.readLong();
        downFlow = dataInput.readLong();
        totalFlow = dataInput.readLong();
    }

    /**
     * 重写toString主要是为了后面的写入文件
     * @return
     */
    @Override
    public String toString() {
        return this.upFlow + "\t" + this.downFlow + "\t" + this.totalFlow;
    }

    public long getPhoneNum() {
        return phoneNum;
    }

    public void setPhoneNum(long phoneNum) {
        this.phoneNum = phoneNum;
    }

    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 getTotalFlow() {
        return totalFlow;
    }

    public void setTotalFlow(long totalFlow) {
        this.totalFlow = totalFlow;
    }
}

package com.jiyx.test.mapred.flowStatistics;

import com.jiyx.test.mapred.flowStatistics.bo.DataBean;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;

/**
 * Job
 * @author jiyx
 * @create 2018-10-15-19:21
 */
public class FlowStatistics {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance();

        job.setJarByClass(FlowStatistics.class);

        job.setMapperClass(FlowStatisticsMapper.class);
        FileInputFormat.setInputPaths(job, new Path(args[0]));

        job.setReducerClass(FlowStatisticsReducer.class);
        // 这块需要注意的是自己踩了一个坑，就是将key和value整反了
        // 然后就会出现异常java.io.IOException: Initialization of all the collectors failed. Error in last collector was:java.lang.ClassCastException: class com.jiyx.test.mapred.flowStatistics.bo.DataBean
        // 所以这里最好注意下
        job.setOutputKeyClass(LongWritable.class);
        job.setOutputValueClass(DataBean.class);
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);
    }
}

package com.jiyx.test.mapred.flowStatistics;

import com.jiyx.test.mapred.flowStatistics.bo.DataBean;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * Map
 * @author jiyx
 * @create 2018-10-15-19:42
 */
public class FlowStatisticsMapper extends Mapper<LongWritable, Text, LongWritable, DataBean> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] datas = value.toString().split("\t");
        long phoneNum = Long.parseLong(datas[1]);
        long upFlow = Long.parseLong(datas[8]);
        long downFlow = Long.parseLong(datas[9]);
        context.write(new LongWritable(phoneNum), new DataBean(phoneNum, upFlow, downFlow));
    }
}

package com.jiyx.test.mapred.flowStatistics;

import com.jiyx.test.mapred.flowStatistics.bo.DataBean;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * Reduce
 * @author jiyx
 * @create 2018-10-15-19:54
 */
public class FlowStatisticsReducer extends Reducer<LongWritable, DataBean, LongWritable, DataBean> {
    @Override
    protected void reduce(LongWritable key, Iterable<DataBean> values, Context context) throws IOException, InterruptedException {
        long upFlowSum = 0;
        long downFlowSum = 0;
        for (DataBean value : values) {
            upFlowSum += value.getUpFlow();
            downFlowSum += value.getDownFlow();
        }
        context.write(key, new DataBean(key.get(), upFlowSum, downFlowSum));
    }
}