使用MapReduce实现温度排序

温度排序代码，具体说明可以搜索其他博客

KeyPair.java

package temperaturesort;

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

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

public class KeyPair implements WritableComparable<KeyPair> {
    private int hot;
    private int year;

    public int getYear() {
        return year;
    }

    public void setYear(int year) {
        this.year = year;
    }

    public int getHot() {
        return hot;
    }

    public void setHot(int hot) {
        this.hot = hot;
    }

    public int compareTo(KeyPair o) {
        int result = this.year-o.getYear();
        if(result!=0){
            return result<0?-1:1;
        }
        return -( this.hot < o.getHot() ? -1 :(this.hot == o.getHot()?0:1));
    }

    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeInt(year);
        dataOutput.writeInt(hot);
    }

    public void readFields(DataInput dataInput) throws IOException {
        this.year=dataInput.readInt();
        this.hot=dataInput.readInt();
    }

    @Override
    public String toString() {
        return year+"\t"+hot;
    }

    @Override
    public int hashCode() {
        return new Integer(year+hot).hashCode();
    }
}

Sort.java:

package temperaturesort;

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

public class Sort extends WritableComparator {
    public Sort(){
         super(KeyPair.class,true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        KeyPair key1 = (KeyPair)a;
        KeyPair key2 = (KeyPair)b;
        int result = key1.getYear()-key2.getYear();
        if(result!=0){
            return result<0?-1:1;
        }
        return key1.getHot()< key2.getHot() ? 1 :(key1.getHot() == key2.getHot()?0:-1);
    }
}

Partition.java:

package temperaturesort;

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

public class Partition extends Partitioner<KeyPair,Text>{
    @Override
    public int getPartition(KeyPair keyPair, Text text, int num) {
        return keyPair.getYear()*127 % num;
    }
}

Group.java:

package temperaturesort;

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

public class Group extends WritableComparator {
    public Group(){
        super(KeyPair.class,true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        KeyPair key1 = (KeyPair)a;
        KeyPair key2 = (KeyPair)b;
        return key1.getYear() < key2.getYear() ? -1 : (key1.getYear()==key2.getYear()?0:1);
    }
}

RunJob.java:

package temperaturesort;

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

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

public class RunJob {
    public static class TempSortMapper extends Mapper<Object,Text,KeyPair,Text>{
        static SimpleDateFormat simpleDateFormat =new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
        @Override
        protected void map(Object key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] strArr=line.split("\t");
            if(strArr.length==2){
                try {
                    Date date = simpleDateFormat.parse(strArr[0]);
                    Calendar calendar = Calendar.getInstance();
                    calendar.setTime(date);
                    int year = calendar.get(1);
                    int hot = Integer.parseInt(strArr[1].substring(0,strArr[1].indexOf("C")));
                    KeyPair keyPair =new KeyPair();
                    keyPair.setHot(hot);
                    keyPair.setYear(year);
                    /*System.out.println("-------------------------------------------------------------------");
                    System.out.println(keyPair);*/
                    context.write(keyPair,value);
                } catch (ParseException e) {
                    e.printStackTrace();
                }
            }

        }
    }

    public static class TempSortReducer extends Reducer<KeyPair,Text,KeyPair,Text>{
        @Override
        protected void reduce(KeyPair key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            for(Text text:values)
                context.write(key,text);
        }
    }

    public static void main(String[] args) throws Exception {
        //System.setProperty("hadoop.home.dir","E:\\softs\\majorSoft\\hadoop-2.7.5");
        Configuration conf = new Configuration();
        conf.set("mapreduce.app-submission.cross-platform", "true");
        Path fileInput = new Path("hdfs://mycluster/testFile/hot.txt");
        Path fileOutput = new Path("hdfs://mycluster/output/hot");

        Job job =Job.getInstance(conf ,"temperatureSort");
        job.setJar("E:\\bigData\\hadoopDemo\\out\\artifacts\\wordCount_jar\\hadoopDemo.jar");
        job.setJarByClass(RunJob.class);
        job.setMapperClass(TempSortMapper.class);
        job.setReducerClass(TempSortReducer.class);
        job.setMapOutputKeyClass(KeyPair.class);
        job.setMapOutputValueClass(Text.class);

        job.setNumReduceTasks(3);
        job.setSortComparatorClass(Sort.class);
        job.setPartitionerClass(Partition.class);
        job.setGroupingComparatorClass(Group.class);

        FileInputFormat.addInputPath(job,fileInput);
        FileOutputFormat.setOutputPath(job,fileOutput);
        System.exit(job.waitForCompletion(true)?0:1);
    }
}

其中自定义的sort和parititon是在mapTask任务之后使用的，而Group是在reduce任务使用的。