1、项目名称：

2、程序代码：

package com.averagescorecount;

import java.io.IOException;

import java.util.Iterator;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

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 ScoreCount {

    /*这个map的输入是经过InputFormat分解过的数据集，InputFormat的默认值是TextInputFormat，它针对文件，

    *按行将文本切割成InputSplits，并用LineRecordReader将InputSplit解析成<key,value>对，

    *key是行在文本中的位置，value是文件中的一行。

    */

    public static class Map extends Mapper<LongWritable, Text, Text , IntWritable>{

        public void map(LongWritable key , Text value , Context context ) throws IOException, InterruptedException{

            String line = value.toString();

            System.out.println("line:"+line);

            System.out.println("TokenizerMapper.map...");

            System.out.println("Map key:"+key.toString()+" Map value:"+value.toString());

            //将输入的数据首先按行进行分割

            StringTokenizer tokenizerArticle = new StringTokenizer(line,"\n");

            //分别对每一行进行处理

            while (tokenizerArticle.hasMoreTokens()) {

                //每行按空格划分

                StringTokenizer tokenizerLine = new StringTokenizer(tokenizerArticle.nextToken());

                String strName = tokenizerLine.nextToken();//学生姓名部分

                String  strScore= tokenizerLine.nextToken();//成绩部分

                Text name = new Text(strName);

                int scoreInt = Integer.parseInt(strScore);

                 System.out.println("name:"+name+"  scoreInt:"+scoreInt);

                context.write(name, new IntWritable(scoreInt));

                System.out.println("context_map:"+context.toString());

            }

             System.out.println("context_map_111:"+context.toString());

        }

    }

    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable>{

            public void reduce(Text key , Iterable<IntWritable> values,Context context) throws IOException,InterruptedException{

                int sum = 0;

                int count = 0;

                int score = 0;

                System.out.println("reducer...");

                System.out.println("Reducer key:"+key.toString()+"  Reducer values:"+values.toString());

                //设置迭代器

                Iterator<IntWritable> iterator = values.iterator();

                while (iterator.hasNext()) {

                    score = iterator.next().get();

                    System.out.println("score:"+score);

                    sum += score;

                    count++;

                }

                int average = (int) sum/count;

                System.out.println("key"+key+"   average:"+average);

                context.write(key, new IntWritable(average));

                System.out.println("context_reducer:"+context.toString());

            }

    }

    public static void main(String[] args) throws Exception {

    Configuration conf = new Configuration();

    Job job = new Job(conf, "score count");

    job.setJarByClass(ScoreCount.class);

    job.setMapperClass(Map.class);

    job.setCombinerClass(Reduce.class);

    job.setReducerClass(Reduce.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);

    }

}
 
3、测试数据：
陈洲立 67
陈东伟 90
李宁 87
杨森 86
陈东奇 78
谭果 94
盖盖 83
陈洲立 68
陈东伟 96
李宁 82
杨森 85
陈东奇 72
谭果 97
盖盖 82
陈洲立 46
陈东伟 48
李宁 67
杨森 33
陈东奇 28
谭果 78
盖盖 87
 

4、运行过程：

14/09/20 19:31:16 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/09/20 19:31:16 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
14/09/20 19:31:16 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
14/09/20 19:31:16 INFO input.FileInputFormat: Total input paths to process : 1
14/09/20 19:31:16 WARN snappy.LoadSnappy: Snappy native library not loaded
14/09/20 19:31:16 INFO mapred.JobClient: Running job: job_local_0001
14/09/20 19:31:16 INFO util.ProcessTree: setsid exited with exit code 0
14/09/20 19:31:16 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@4080b02f
14/09/20 19:31:16 INFO mapred.MapTask: io.sort.mb = 100
14/09/20 19:31:16 INFO mapred.MapTask: data buffer = 79691776/99614720
14/09/20 19:31:16 INFO mapred.MapTask: record buffer = 262144/327680
line:陈洲立 67
TokenizerMapper.map...
Map key:0 Map value:陈洲立 67
name:陈洲立  scoreInt:67
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈东伟 90
TokenizerMapper.map...
Map key:13 Map value:陈东伟 90
name:陈东伟  scoreInt:90
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:李宁 87
TokenizerMapper.map...
Map key:26 Map value:李宁 87
name:李宁  scoreInt:87
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:杨森 86
TokenizerMapper.map...
Map key:36 Map value:杨森 86
name:杨森  scoreInt:86
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈东奇 78
TokenizerMapper.map...
Map key:46 Map value:陈东奇 78
name:陈东奇  scoreInt:78
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:谭果 94
TokenizerMapper.map...
Map key:59 Map value:谭果 94
name:谭果  scoreInt:94
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:盖盖 83
TokenizerMapper.map...
Map key:69 Map value:盖盖 83
name:盖盖  scoreInt:83
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈洲立 68
TokenizerMapper.map...
Map key:79 Map value:陈洲立 68
name:陈洲立  scoreInt:68
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈东伟 96
TokenizerMapper.map...
Map key:92 Map value:陈东伟 96
name:陈东伟  scoreInt:96
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:李宁 82
TokenizerMapper.map...
Map key:105 Map value:李宁 82
name:李宁  scoreInt:82
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:杨森 85
TokenizerMapper.map...
Map key:115 Map value:杨森 85
name:杨森  scoreInt:85
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈东奇 72
TokenizerMapper.map...
Map key:125 Map value:陈东奇 72
name:陈东奇  scoreInt:72
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:谭果 97
TokenizerMapper.map...
Map key:138 Map value:谭果 97
name:谭果  scoreInt:97
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:盖盖 82
TokenizerMapper.map...
Map key:148 Map value:盖盖 82
name:盖盖  scoreInt:82
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈洲立 46
TokenizerMapper.map...
Map key:158 Map value:陈洲立 46
name:陈洲立  scoreInt:46
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈东伟 48
TokenizerMapper.map...
Map key:171 Map value:陈东伟 48
name:陈东伟  scoreInt:48
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:李宁 67
TokenizerMapper.map...
Map key:184 Map value:李宁 67
name:李宁  scoreInt:67
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:杨森 33
TokenizerMapper.map...
Map key:194 Map value:杨森 33
name:杨森  scoreInt:33
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:陈东奇 28
TokenizerMapper.map...
Map key:204 Map value:陈东奇 28
name:陈东奇  scoreInt:28
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:谭果 78
TokenizerMapper.map...
Map key:217 Map value:谭果 78
name:谭果  scoreInt:78
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:盖盖 87
TokenizerMapper.map...
Map key:227 Map value:盖盖 87
name:盖盖  scoreInt:87
context_map:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
line:
TokenizerMapper.map...
Map key:237 Map value:
context_map_111:org.apache.hadoop.mapreduce.Mapper$Context@d4cf771
14/09/20 19:31:16 INFO mapred.MapTask: Starting flush of map output
reducer...
Reducer key:李宁  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:82
score:87
score:67
key李宁   average:78
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
reducer...
Reducer key:杨森  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:33
score:86
score:85
key杨森   average:68
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
reducer...
Reducer key:盖盖  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:87
score:83
score:82
key盖盖   average:84
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
reducer...
Reducer key:谭果  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:94
score:97
score:78
key谭果   average:89
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
reducer...
Reducer key:陈东伟  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:48
score:90
score:96
key陈东伟   average:78
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
reducer...
Reducer key:陈东奇  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:72
score:78
score:28
key陈东奇   average:59
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
reducer...
Reducer key:陈洲立  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@63dbbdf2
score:68
score:67
score:46
key陈洲立   average:60
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@3d32487
14/09/20 19:31:16 INFO mapred.MapTask: Finished spill 0
14/09/20 19:31:16 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
14/09/20 19:31:17 INFO mapred.JobClient:  map 0% reduce 0%
14/09/20 19:31:19 INFO mapred.LocalJobRunner:
14/09/20 19:31:19 INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.
14/09/20 19:31:19 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@5fc24d33
14/09/20 19:31:19 INFO mapred.LocalJobRunner:
14/09/20 19:31:19 INFO mapred.Merger: Merging 1 sorted segments
14/09/20 19:31:19 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 102 bytes
14/09/20 19:31:19 INFO mapred.LocalJobRunner:
reducer...
Reducer key:李宁  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:78
key李宁   average:78
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
reducer...
Reducer key:杨森  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:68
key杨森   average:68
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
reducer...
Reducer key:盖盖  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:84
key盖盖   average:84
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
reducer...
Reducer key:谭果  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:89
key谭果   average:89
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
reducer...
Reducer key:陈东伟  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:78
key陈东伟   average:78
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
reducer...
Reducer key:陈东奇  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:59
key陈东奇   average:59
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
reducer...
Reducer key:陈洲立  Reducer values:org.apache.hadoop.mapreduce.ReduceContext$ValueIterable@2407325d
score:60
key陈洲立   average:60
context_reducer:org.apache.hadoop.mapreduce.Reducer$Context@52403ee2
14/09/20 19:31:19 INFO mapred.Task: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
14/09/20 19:31:19 INFO mapred.LocalJobRunner:
14/09/20 19:31:19 INFO mapred.Task: Task attempt_local_0001_r_000000_0 is allowed to commit now
14/09/20 19:31:19 INFO output.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://localhost:9000/user/hadoop/score_output
14/09/20 19:31:20 INFO mapred.JobClient:  map 100% reduce 0%
14/09/20 19:31:22 INFO mapred.LocalJobRunner: reduce > reduce
14/09/20 19:31:22 INFO mapred.Task: Task 'attempt_local_0001_r_000000_0' done.
14/09/20 19:31:23 INFO mapred.JobClient:  map 100% reduce 100%
14/09/20 19:31:23 INFO mapred.JobClient: Job complete: job_local_0001
14/09/20 19:31:23 INFO mapred.JobClient: Counters: 22
14/09/20 19:31:23 INFO mapred.JobClient:   Map-Reduce Framework
14/09/20 19:31:23 INFO mapred.JobClient:     Spilled Records=14
14/09/20 19:31:23 INFO mapred.JobClient:     Map output materialized bytes=106
14/09/20 19:31:23 INFO mapred.JobClient:     Reduce input records=7
14/09/20 19:31:23 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=0
14/09/20 19:31:23 INFO mapred.JobClient:     Map input records=22
14/09/20 19:31:23 INFO mapred.JobClient:     SPLIT_RAW_BYTES=116
14/09/20 19:31:23 INFO mapred.JobClient:     Map output bytes=258
14/09/20 19:31:23 INFO mapred.JobClient:     Reduce shuffle bytes=0
14/09/20 19:31:23 INFO mapred.JobClient:     Physical memory (bytes) snapshot=0
14/09/20 19:31:23 INFO mapred.JobClient:     Reduce input groups=7
14/09/20 19:31:23 INFO mapred.JobClient:     Combine output records=7
14/09/20 19:31:23 INFO mapred.JobClient:     Reduce output records=7
14/09/20 19:31:23 INFO mapred.JobClient:     Map output records=21
14/09/20 19:31:23 INFO mapred.JobClient:     Combine input records=21
14/09/20 19:31:23 INFO mapred.JobClient:     CPU time spent (ms)=0
14/09/20 19:31:23 INFO mapred.JobClient:     Total committed heap usage (bytes)=408944640
14/09/20 19:31:23 INFO mapred.JobClient:   File Input Format Counters
14/09/20 19:31:23 INFO mapred.JobClient:     Bytes Read=238
14/09/20 19:31:23 INFO mapred.JobClient:   FileSystemCounters
14/09/20 19:31:23 INFO mapred.JobClient:     HDFS_BYTES_READ=476
14/09/20 19:31:23 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=81132
14/09/20 19:31:23 INFO mapred.JobClient:     FILE_BYTES_READ=448
14/09/20 19:31:23 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=79
14/09/20 19:31:23 INFO mapred.JobClient:   File Output Format Counters
14/09/20 19:31:23 INFO mapred.JobClient:     Bytes Written=79

5、输出结果：

MapReduce编程系列 — 2：计算平均分的更多相关文章

  1. MapReduce编程系列 — 1：计算单词

    1.代码: package com.mrdemo; import java.io.IOException; import java.util.StringTokenizer; import org.a ...

  2. 【原创】MapReduce编程系列之二元排序

    普通排序实现 普通排序的实现利用了按姓名的排序,调用了默认的对key的HashPartition函数来实现数据的分组.partition操作之后写入磁盘时会对数据进行排序操作(对一个分区内的数据作排序 ...

  3. MapReduce编程系列 — 6：多表关联

    1.项目名称: 2.程序代码: 版本一(详细版): package com.mtjoin; import java.io.IOException; import java.util.Iterator; ...

  4. MapReduce编程系列 — 5：单表关联

    1.项目名称: 2.项目数据: chile    parentTom    LucyTom    JackJone    LucyJone    JackLucy    MaryLucy    Ben ...

  5. MapReduce编程系列 — 4：排序

    1.项目名称: 2.程序代码: package com.sort; import java.io.IOException; import org.apache.hadoop.conf.Configur ...

  6. MapReduce编程系列 — 3：数据去重

    1.项目名称: 2.程序代码: package com.dedup; import java.io.IOException; import org.apache.hadoop.conf.Configu ...

  7. 【原创】MapReduce编程系列之表连接

    问题描述 需要连接的表如下:其中左边是child,右边是parent,我们要做的是找出grandchild和grandparent的对应关系,为此需要进行表的连接. Tom Lucy Tom Jim ...

  8. MapReduce 编程 系列九 Reducer数目

    本篇介绍怎样控制reduce的数目.前面观察结果文件,都会发现通常是以part-r-00000 形式出现多个文件,事实上这个reducer的数目有关系.reducer数目多,结果文件数目就多. 在初始 ...

  9. MapReduce 编程 系列七 MapReduce程序日志查看

    首先,假设须要打印日志,不须要用log4j这些东西,直接用System.out.println就可以,这些输出到stdout的日志信息能够在jobtracker网站终于找到. 其次,假设在main函数 ...

随机推荐

  1. C/C++编译过程理解【转】

    转载自:http://www.cppblog.com/woaidongmao/archive/2008/11/07/66254.aspx 今天,通过自己的努力终于对C/C++的编译过程有了个粗略的了解 ...

  2. httpd 配置用户访问认证

    需求:单用户访问网站的某个目录,需要使用帐号密码来登录才能访问. 一.编辑虚拟主机的配置文件,添加目录级访问限制 <Directory "/var/www/html/demo" ...

  3. C++中执行windows指令

    执行windows指令: BOOL ExecDosCmd(]) { SECURITY_ATTRIBUTES sa; HANDLE hRead,hWrite; sa.nLength = sizeof(S ...

  4. GSM嗅探

    GSM初探 大家应该都听说过HTTP协议,又听说WEB服务,每一个服务的背后都有一个协议在工作着.所谓的没有规矩不成方圆,说的就是这个道理,每一个细小的部分,都已经规定好,只要按照协议执行,就不会出现 ...

  5. java.imageIo给图片添加水印

    最近项目在做一个商城项目, 项目上的图片要添加水印①,添加图片水印;②:添加文字水印; 一下提供下个方法,希望大家可以用得着: package com.blogs.image; import java ...

  6. mysql table readonly

    if the table does not have primary key or unique non-nullable defined, then MySql workbench could no ...

  7. 给LinkLabel文本绘制颜色

    我在form中有一个linkLabel,显示文字是“中秋快乐”.现在我希望其中的“中秋”两个字用红色字体显示,“快乐”用黑色字体.请问如何在一个linkLabel中实现上述效果? 答案: privat ...

  8. CSS 中的 em单位

    em的对应的像素值如何计算,一言以蔽之:em就是倍数当前元素font-size的倍数,其结果就是当前字体的像素值乘以em的值,至于font-size来自何处?有两个来源:继承自父元素和通过CSS直接设 ...

  9. VB.Net 文件处理类

    1.编写日志 2.本地文件的读取和写入 3.Base64与图片的互相转换 Imports System.IO Imports System.Text Public Class Cls_File #Re ...

  10. IEEE 754 浮点数的四种舍入方式

    四种舍入方向: 向最接近的可表示的值:当有两个最接近的可表示的值时首选"偶数"值:向负无穷大(向下):向正无穷大(向上)以及向0(截断). 说明:默认模式是最近舍入(Round t ...