Hadoop,HelloWord,Examples-,平均数
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? 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lua游戏辅助脚本源码,记事本怎样在vscode上查看,ubuntu麒麟桌面,tomcat无法从外部访问,黑蛆爬虫,php面向对象编程书籍,阜宁一站式seo优化lzw
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? 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。