? 另外一个hadoop的入门demo，求平均数。是对WordCount这个demo的一个小小的修改。输入一堆成绩单（人名，成绩），然后求每个人成绩平均数，比如： //? subject1.txt ? a 90 ? b 80 ? c 70 ?// subject2.txt ? a 100 ? b 90 ? c 80 ? 求a,b,c这三个人的平均

? 另外一个hadoop的入门demo，求平均数。是对WordCount这个demo的一个小小的修改。输入一堆成绩单（人名，成绩），然后求每个人成绩平均数，比如：

//? subject1.txt

? a 90
? b 80
? c 70

?// subject2.txt

? a 100
? b 90
? c 80

? 求a,b,c这三个人的平均分。解决思路很简单，在map阶段key是名字，value是成绩，直接output。reduce阶段得到了map输出的key名字，values是该名字对应的一系列的成绩，那么对其求平均数即可。

? 这里我们实现了两个版本的代码，分别用TextInputFormat和 KeyValueTextInputFormat来作为输入格式。

? TextInputFormat版本：

?

import java.util.*;
import java.io.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class AveScore {
	public static class AveMapper extends Mapper
	{
	@Override
	public void map(Object key, Text value, Context context) throws IOException, InterruptedException
	{
	String line = value.toString();
	String[] strs = line.split(" ");
	String name = strs[0];
	int score = Integer.parseInt(strs[1]);
	context.write(new Text(name), new IntWritable(score));
	}
	}
	public static class AveReducer extends Reducer
	{
	@Override
	public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException
	{
	int sum = 0;
	int count = 0;
	for(IntWritable val : values)
	{
	sum += val.get();
	count++;
	}
	int aveScore = sum / count;
	context.write(key, new IntWritable(aveScore));
	}
	}
	public static void main(String[] args) throws Exception
	{
	Configuration conf = new Configuration();
	Job job = new Job(conf,"AverageScore");
	job.setJarByClass(AveScore.class);
	job.setMapperClass(AveMapper.class);
	job.setReducerClass(AveReducer.class);
	job.setOutputKeyClass(Text.class);
	job.setOutputValueClass(IntWritable.class);
	FileInputFormat.addInputPath(job, new Path(args[0]));
	FileOutputFormat.setOutputPath(job, new Path(args[1]));
	System.exit( job.waitForCompletion(true) ? 0 : 1);
	}
}

KeyValueTextInputFormat版本；

import java.util.*;
import java.io.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class AveScore_KeyValue {
	public static class AveMapper extends Mapper
	{
	@Override
	public void map(Text key, Text value, Context context) throws IOException, InterruptedException
	{
	 int score = Integer.parseInt(value.toString());
	context.write(key, new IntWritable(score) );
	}
	}
	public static class AveReducer extends Reducer
	{
	@Override
	public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException
	{
	int sum = 0;
	int count = 0;
	for(IntWritable val : values)
	{
	sum += val.get();
	count++;
	}
	int aveScore = sum / count;
	context.write(key, new IntWritable(aveScore));
	}
	}
	public static void main(String[] args) throws Exception
	{
	Configuration conf = new Configuration();
	conf.set("mapreduce.input.keyvaluelinerecordreader.key.value.separator", " ");
	Job job = new Job(conf,"AverageScore");
	job.setJarByClass(AveScore_KeyValue.class);
	job.setMapperClass(AveMapper.class);
	job.setReducerClass(AveReducer.class);
	job.setOutputKeyClass(Text.class);
	job.setOutputValueClass(IntWritable.class);
 	job.setInputFormatClass(KeyValueTextInputFormat.class);
	job.setOutputFormatClass(TextOutputFormat.class) ; 
	FileInputFormat.addInputPath(job, new Path(args[0]));
	FileOutputFormat.setOutputPath(job, new Path(args[1]));
	System.exit( job.waitForCompletion(true) ? 0 : 1);
	}
}

输出结果为：

? a 95
? b 85
? c 75

?

作者：qiul12345 发表于2013-8-23 21:51:03 原文链接

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原文地址：Hadoop HelloWord Examples- 求平均数, 感谢原作者分享。