Mapreduce---RandomSampler采样实现全排序

排序是MapReduce的核心技术，排序分为部分排序，全排序和二次排序。

部分排序：调用默认的HashPartitioner，不需要操作，每个reduce聚合的key都是有序的。

全排序：对reduce输出的所有的key实现排序

             方法1：设置一个reducde

             方法2：自定义分区类实现全排序

            方法3 ：使用采样        

下面以统计每年的最高气温为例进行示例：

注意：源文件是一个sequenceFile序列文件

1、MaxTempMapper

package hadoop.mr.sort.total.totalorder;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * MaxTempMapper
 */
public class MaxTempMapper extends Mapper {

	protected void map(IntWritable key, IntWritable value, Context context) throws IOException, InterruptedException {
		context.write(key,value);
	}
}

2、MaxTempReducer

package hadoop.mr.sort.total.totalorder;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 */
public class MaxTempReducer extends Reducer{
	protected void reduce(IntWritable key, Iterable values, Context context) throws IOException, InterruptedException {
		int max = Integer.MIN_VALUE ;
		for(IntWritable iw : values){
			max = max > iw.get() ? max : iw.get() ;
		}
		context.write(key,new IntWritable(max));
	}
}

3、App

package hadoop.mr.sort.total.totalorder;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;

/**
 */
public class App {
	public static void main(String[] args) throws Exception {
		args = new String[]{"d:/java/mr/data/temp.seq", "d:/java/mr/out"};
		Configuration conf = new Configuration();
		FileSystem fs = FileSystem.get(conf);
		if(fs.exists(new Path(args[1]))){
			fs.delete(new Path(args[1]),true);
		}

		Job job = Job.getInstance(conf);

		job.setJobName("maxTemp");
		job.setJarByClass(App.class);

		job.setMapperClass(MaxTempMapper.class);
		job.setReducerClass(MaxTempReducer.class);

		FileInputFormat.addInputPath(job,new Path(args[0]));
		FileOutputFormat.setOutputPath(job,new Path(args[1]));
		//设置combine输入格式
		job.setInputFormatClass(SequenceFileInputFormat.class);
		job.setPartitionerClass(TotalOrderPartitioner.class);

		job.setNumReduceTasks(3);

		job.setMapOutputKeyClass(IntWritable.class);
		job.setMapOutputValueClass(IntWritable.class);

		job.setOutputKeyClass(IntWritable.class);
		job.setOutputValueClass(IntWritable.class);

		TotalOrderPartitioner.setPartitionFile(job.getConfiguration(),new Path("file:///d:/java/mr/par.seq"));
		//随机采样器
		InputSampler.RandomSampler r = new InputSampler.RandomSampler(1f,5,3);
		//创建分区文件
		InputSampler.writePartitionFile(job,r);

		job.waitForCompletion(true);
	}
}