MapReduce:将下面的两排数字先按第一排排序，然后再按第二排排序，要求顺序排序

MapReduce:将下面的两排数字先按第一排排序，然后再按第二排排序，要求顺序排序

文件如下：

这个案例主要考察我们对排序的理解，我们可以这样做：

代码如下（由于水平有限，不保证完全正确，如果发现错误欢迎指正）：

①建一个TestBean

package com;

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

import org.apache.hadoop.io.WritableComparable;

public class TestBean implements WritableComparable<TestBean>{
    private long a;
    private long b;

    public TestBean() {
        super();
    }
    public TestBean(long a, long b) {
        super();
        this.a = a;
        this.b = b;
    }
    public long getA() {
        return a;
    }
    public void setA(long a) {
        this.a = a;
    }
    public long getB() {
        return b;
    }
    public void setB(long b) {
        this.b = b;
    }
    @Override
    public String toString() {
        return  a + "  " + b;
    }
    @Override
    public void readFields(DataInput in) throws IOException {
        this.a=in.readLong();
        this.b=in.readLong();
    }
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeLong(a);
        out.writeLong(b);

    }
    
    //排序主要代码
    @Override
    public int compareTo(TestBean o) {
        long i=this.a-o.getA();
        if(i==){
            long j=this.b-o.getB();
            return (int) j;
        }else{
            return (int)i;
        }
    }
}

②测试代码：

package com;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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.output.FileOutputFormat;

public class TestCount {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration config = new  Configuration();
        config.set("fs.defaultFS", "hdfs://192.168.0.100:9000");
        config.set("yarn.resourcemanager.hostname", "192.168.0.100");

        FileSystem fs = FileSystem.get(config);

        Job job = Job.getInstance(config);

        job.setJarByClass(TestCount.class);

        //设置所用到的map类
        job.setMapperClass(myMapper.class);
        job.setMapOutputKeyClass(TestBean.class);
        job.setMapOutputValueClass(NullWritable.class);

        //设置用到的reducer类
        job.setReducerClass(myReducer.class);
        job.setOutputKeyClass(TestBean.class);
        job.setOutputValueClass(NullWritable.class);

        //设置输出地址
        FileInputFormat.addInputPath(job, new Path("/input/order.txt"));

        Path path = new Path("/output/");

        if(fs.exists(path)){
            fs.delete(path, true);
        }

        //指定文件的输出地址
        FileOutputFormat.setOutputPath(job, path);

        //启动处理任务job
        boolean completion = job.waitForCompletion(true);
        if(completion){
            System.out.println("Job Success!");
        }

    }

    public static class myMapper extends Mapper<LongWritable, Text,TestBean, NullWritable>{

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String values=value.toString();
            String words[]=values.split(" ");
            TestBean bean = new TestBean(Long.parseLong(words[]),Long.parseLong(words[]));
            context.write(bean, NullWritable.get());
        }
    }

    public static class myReducer extends Reducer<TestBean, NullWritable, LongWritable, LongWritable>{

        @Override
        protected void reduce(TestBean key, Iterable<NullWritable> values,Context context)throws IOException, InterruptedException {
            context.write(new LongWritable(key.getA()), new LongWritable(key.getB()));
        }
    }
}

这样就能得到最终结果：

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