mapreduce自定义排序(map端1.4步)

3　　3

3　　2

3　　1

2　　2

2　　1

1　　1

-----------------期望输出

1　　1

2　　1

2　　2

3　　1

3　　2

3　　3

将以上数据进行排序，排序规则是：按照第一列升序排序，如果第一列数值相同，则按照第二列升序排序。

但是默认情况下结果是：

1　　1

2　　2

2　　1

3　　3

3　　2

3　　1

即默认情况下只有第一列参加排序，第二列并不参加，即原来的v2不能参与排序，想达到目标必须自定义类，该类必须将原来的k2和v2封装到一个类中，作为新的k2必须实现一个接口implements WritableComparable，于mapper  reducer平级，并对其中方法进行实现。这里自定义类NewK2如下：

static class NewK2 implements WritableComparable<NewK2>{
Long first;
Long second;
public NewK2(){}
public NewK2(long first, long second){
this.first = first;
this.second = second;
}
@Override
public void readFields(DataInput in) throws IOException {
this.first = in.readLong();
this.second = in.readLong();
}

@Override
public void write(DataOutput out) throws IOException {
out.writeLong(first);
out.writeLong(second);
}

/**
* 当k2进行排序时，会调用该方法.
* 当第一列不同时，升序；当第一列相同时，第二列升序
*/
@Override
public int compareTo(NewK2 o) {
final long minus = this.first - o.first;
if(minus !=0){
return (int)minus;
}
return (int)(this.second - o.second);
}
@Override
public int hashCode() {
return this.first.hashCode()+this.second.hashCode();
}

public boolean equals(Object obj){

if(!(obj instanceof NewK2))

return false;

NewK2 NK2=(NewK2)obj;

return (this.first==NK2.first&&this.second==NK2.second);

}
}

----------------

static class MyMapper extends Mapper<LongWritable, Text, NewK2, LongWritable>{
protected void map(LongWritable key, Text value, Context context) throws Exception {
final String[] splited = value.toString().split("\t");
final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1]));
final LongWritable v2 = new LongWritable(Long.parseLong(splited[1]));
context.write(k2, v2);
};
}

static class MyReducer extends Reducer<NewK2, LongWritable, LongWritable, LongWritable>{
protected void reduce(NewK2 k2, java.lang.Iterable<LongWritable> v2s, Context context) throws Exception {
context.write(new LongWritable(k2.first), new LongWritable(k2.second));
};
}

---------------------------

package sort;

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

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.Text;
import org.apache.hadoop.io.WritableComparable;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;

public class SortApp {
	static final String INPUT_PATH = "hdfs://mlj:9000/sort";
	static final String OUT_PATH = "hdfs://mlj:9000/sort_out";
	public static void main(String[] args) throws Exception{
		final Configuration configuration = new Configuration();

		final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), configuration);
		if(fileSystem.exists(new Path(OUT_PATH))){
			fileSystem.delete(new Path(OUT_PATH), true);
		}

		final Job job = new Job(configuration, SortApp.class.getSimpleName());

		//1.1 指定输入文件路径
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		//指定哪个类用来格式化输入文件
		job.setInputFormatClass(TextInputFormat.class);

		//1.2指定自定义的Mapper类
		job.setMapperClass(MyMapper.class);
		//指定输出<k2,v2>的类型
		job.setMapOutputKeyClass(NewK2.class);
		job.setMapOutputValueClass(LongWritable.class);

		//1.3 指定分区类
		job.setPartitionerClass(HashPartitioner.class);
		job.setNumReduceTasks(1);

		//1.4 TODO 排序、分区

		//1.5  TODO （可选）合并

		//2.2 指定自定义的reduce类
		job.setReducerClass(MyReducer.class);
		//指定输出<k3,v3>的类型
		job.setOutputKeyClass(LongWritable.class);
		job.setOutputValueClass(LongWritable.class);

		//2.3 指定输出到哪里
		FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
		//设定输出文件的格式化类
		job.setOutputFormatClass(TextOutputFormat.class);

		//把代码提交给JobTracker执行
		job.waitForCompletion(true);
	}

	static class MyMapper extends Mapper<LongWritable, Text, NewK2, LongWritable>{
		protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,NewK2,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {
			final String[] splited = value.toString().split("\t");
			final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1]));
			final LongWritable v2 = new LongWritable(Long.parseLong(splited[1]));
			context.write(k2, v2);
		};
	}

	static class MyReducer extends Reducer<NewK2, LongWritable, LongWritable, LongWritable>{
		protected void reduce(NewK2 k2, java.lang.Iterable<LongWritable> v2s, org.apache.hadoop.mapreduce.Reducer<NewK2,LongWritable,LongWritable,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {
			context.write(new LongWritable(k2.first), new LongWritable(k2.second));
		};
	}

	/**
	 * 问：为什么实现该类？
	 * 答：因为原来的v2不能参与排序，把原来的k2和v2封装到一个类中，作为新的k2
	 *
	 */
	static class  NewK2 implements WritableComparable<NewK2>{
		Long first;
		Long second;

		public NewK2(){}

		public NewK2(long first, long second){
			this.first = first;
			this.second = second;
		}

		@Override
		public void readFields(DataInput in) throws IOException {
			this.first = in.readLong();
			this.second = in.readLong();
		}

		@Override
		public void write(DataOutput out) throws IOException {
			out.writeLong(first);
			out.writeLong(second);
		}

		/**
		 * 当k2进行排序时，会调用该方法.
		 * 当第一列不同时，升序；当第一列相同时，第二列升序
		 */
		@Override
		public int compareTo(NewK2 o) {
			final long minus = this.first - o.first;
			if(minus !=0){
				return (int)minus;
			}
			return (int)(this.second - o.second);
		}

		@Override
		public int hashCode() {
			return this.first.hashCode()+this.second.hashCode();
		}

	}

}