Hadoop MapReduce编程 API入门系列之挖掘气象数据版本3（九）Hadoop MapReduce编程 API入门系列之挖掘气象数据版本1（一）Hadoop MapReduce编程 API入门系列之挖掘气象数据版本2（九）

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我是靠谱客的博主欣慰机器猫，最近开发中收集的这篇文章主要介绍Hadoop MapReduce编程 API入门系列之挖掘气象数据版本3（九）Hadoop MapReduce编程 API入门系列之挖掘气象数据版本1（一）Hadoop MapReduce编程 API入门系列之挖掘气象数据版本2（九），觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

　　不多说，直接上干货！

　　　下面，是版本1。

Hadoop MapReduce编程 API入门系列之挖掘气象数据版本1（一）

　　　　下面是版本2。

Hadoop MapReduce编程 API入门系列之挖掘气象数据版本2（九）

这篇博客，给大家，体会不一样的版本编程。

代码

package zhouls.bigdata.myMapReduce.weather;

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

import org.apache.hadoop.io.WritableComparable;

public class MyKey implements WritableComparable<MyKey>{
    //WritableComparable,实现这个方法，要多很多
    //readFields是读入，write是写出
    private int year;
    private int month;
    private double hot;
    public int getYear() {
    return year;
}

    public void setYear(int year) {
        this.year = year;
    }
    
    public int getMonth() {
        return month;
    }
    
    public void setMonth(int month) {
        this.month = month;
    }
    
    public double getHot() {
        return hot;
    }
    
    public void setHot(double hot) {
        this.hot = hot;
        }//这一大段的get和set，可以右键，source，产生get和set，自动生成。


    public void readFields(DataInput arg0) throws IOException { //反序列化
        this.year=arg0.readInt();
        this.month=arg0.readInt();
        this.hot=arg0.readDouble();
    }
    
    public void write(DataOutput arg0) throws IOException { //序列化
        arg0.writeInt(year);
        arg0.writeInt(month);
        arg0.writeDouble(hot);
    }

    //判断对象是否是同一个对象，当该对象作为输出的key
    public int compareTo(MyKey o) {
        int r1 =Integer.compare(this.year, o.getYear());//比较当前的年和你传过来的年
        if(r1==0){
        int r2 =Integer.compare(this.month, o.getMonth());
        if(r2==0){
            return Double.compare(this.hot, o.getHot());
        }else{
            return r2;
        }
        }else{
            return r1;
        }
    }

}

package zhouls.bigdata.myMapReduce.weather;

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;

public class MyPartitioner extends HashPartitioner<MyKey, DoubleWritable>{//这里就是洗牌

    //执行时间越短越好
    public int getPartition(MyKey key, DoubleWritable value, int numReduceTasks) {
        return (key.getYear()-1949)%numReduceTasks;//对于一个数据集，找到最小，1949
    }
}


//1949-10-01 14:21:02    34c
//1949-10-02 14:01:02    36c
//1950-01-01 11:21:02    32c
//1950-10-01 12:21:02    37c
//1951-12-01 12:21:02    23c
//1950-10-02 12:21:02    41c
//1950-10-03 12:21:02    27c
//1951-07-01 12:21:02    45c
//1951-07-02 12:21:02    46c
//1951-07-03 12:21:03    47c

package zhouls.bigdata.myMapReduce.weather;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

public class MySort extends WritableComparator{

    public MySort(){
        super(MyKey.class,true);//把MyKey传进了
    }

    public int compare(WritableComparable a, WritableComparable b) {//这是排序的精髓
        MyKey k1 =(MyKey) a;
        MyKey k2 =(MyKey) b;
        int r1 =Integer.compare(k1.getYear(), k2.getYear());
        if(r1==0){//年相同
        int r2 =Integer.compare(k1.getMonth(), k2.getMonth());
        if(r2==0){//月相同
            return -Double.compare(k1.getHot(), k2.getHot());//比较气温
        }else{
            return r2;
        }
        }else{
            return r1;
        }

    }
}

package zhouls.bigdata.myMapReduce.weather;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

public class MyGroup extends WritableComparator{

    public MyGroup(){
        super(MyKey.class,true);//把MyKey传进了
}

    public int compare(WritableComparable a, WritableComparable b) {//这是分组的精髓
        MyKey k1 =(MyKey) a;
        MyKey k2 =(MyKey) b;
        int r1 =Integer.compare(k1.getYear(), k2.getYear());
    if(r1==0){
        return Integer.compare(k1.getMonth(), k2.getMonth());
    }else{
        return r1;
    }

    }
}

package zhouls.bigdata.myMapReduce.weather;


import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Calendar;
import java.util.Date;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class RunJob {


//    1949-10-01 14:21:02    34c WeatherMapper
//    1949-10-02 14:01:02    36c
//    1950-01-01 11:21:02    32c    分区在MyPartitioner.java 
//    1950-10-01 12:21:02    37c
//    1951-12-01 12:21:02    23c    排序在MySort.java
//    1950-10-02 12:21:02    41c
//    1950-10-03 12:21:02    27c    分组在MyGroup.java
//    1951-07-01 12:21:02    45c
//    1951-07-02 12:21:02    46c    再，WeatherReducer
//    1951-07-03 12:21:03    47c

//key：每行第一个隔开符（制表符）左边为key，右边为value    自定义类型MyKey，洗牌，    
    static class WeatherMapper extends Mapper<Text, Text, MyKey, DoubleWritable>{
    SimpleDateFormat sdf =new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
    NullWritable v =NullWritable.get();
//    1949-10-01 14:21:02是自定义类型MyKey，即key
//    34c是DoubleWritable，即value

    protected void map(Text key, Text value,Context context) throws IOException, InterruptedException {
    try {
        Date date =sdf.parse(key.toString());
        Calendar c =Calendar.getInstance();
        //Calendar 类是一个抽象类，可以通过调用 getInstance() 静态方法获取一个 Calendar 对象，
        //此对象已由当前日期时间初始化，即默认代表当前时间，如 Calendar c = Calendar.getInstance();    
        c.setTime(date);
        int year =c.get(Calendar.YEAR);
        int month =c.get(Calendar.MONTH);

        double hot =Double.parseDouble(value.toString().substring(0, value.toString().lastIndexOf("c")));
        MyKey k =new MyKey();
        k.setYear(year);
        k.setMonth(month);
        k.setHot(hot);
        context.write(k, new DoubleWritable(hot));
    } catch (Exception e) {
        e.printStackTrace();
    }
    }
}

    static class WeatherReducer extends Reducer<MyKey, DoubleWritable, Text, NullWritable>{
    protected void reduce(MyKey arg0, Iterable<DoubleWritable> arg1,Context arg2)throws IOException, InterruptedException {
        int i=0;
        for(DoubleWritable v :arg1){
        i++;
        String msg =arg0.getYear()+"t"+arg0.getMonth()+"t"+v.get();//"t"是制表符
        arg2.write(new Text(msg), NullWritable.get());
                if(i==3){
                    break;
                }
        }
    }
}

public static void main(String[] args) {
    Configuration config =new Configuration();
//    config.set("fs.defaultFS", "hdfs://HadoopMaster:9000");
//    config.set("yarn.resourcemanager.hostname", "HadoopMaster");
//    config.set("mapred.jar", "C:\Users\Administrator\Desktop\wc.jar");
//    config.set("mapreduce.input.keyvaluelinerecordreader.key.value.separator", ",");//默认分隔符是制表符"t"，这里自定义，如","
    try {
        FileSystem fs =FileSystem.get(config);

        Job job =Job.getInstance(config);
        job.setJarByClass(RunJob.class);

        job.setJobName("weather");

        job.setMapperClass(WeatherMapper.class);
        job.setReducerClass(WeatherReducer.class);
        job.setMapOutputKeyClass(MyKey.class);
        job.setMapOutputValueClass(DoubleWritable.class);

        job.setPartitionerClass(MyPartitioner.class);
        job.setSortComparatorClass(MySort.class);
        job.setGroupingComparatorClass(MyGroup.class);

        job.setNumReduceTasks(3);

        job.setInputFormatClass(KeyValueTextInputFormat.class);

//    FileInputFormat.addInputPath(job, new Path("hdfs://HadoopMaster:9000/weather.txt"));//输入路径，下有weather.txt
//    
//    Path outpath =new Path("hdfs://HadoopMaster:9000/out/weather");

        FileInputFormat.addInputPath(job, new Path("./data/weather.txt"));//输入路径，下有weather.txt

    Path outpath =new Path("./out/weather");

    if(fs.exists(outpath)){
        fs.delete(outpath, true);
    }
    FileOutputFormat.setOutputPath(job, outpath);

        boolean f= job.waitForCompletion(true);
        if(f){
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
    }

}