简单的java Hadoop MapReduce程序(计算平均成绩)从打包到提交及运行

程序源码

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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class Score {
public static class Map extends
Mapper<LongWritable, Text, Text, IntWritable> {
// 实现map函数
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// 将输入的纯文本文件的数据转化成String
String line = 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.hasMoreElements()) {
// 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);
// 输出姓名和成绩
context.write(name, new IntWritable(scoreInt));
}
}
} public static class Reduce extends
Reducer<Text, IntWritable, Text, IntWritable> {
// 实现reduce函数
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
int count = 0;
Iterator<IntWritable> iterator = values.iterator();
while (iterator.hasNext()) {
sum += iterator.next().get();// 计算总分
count++;// 统计总的科目数
}
int average = (int) sum / count;// 计算平均成绩
context.write(key, new IntWritable(average));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
// "localhost:9000" 需要根据实际情况设置一下
conf.set("mapred.job.tracker", "localhost:9000");
// 一个hdfs文件系统中的 输入目录 及 输出目录
String[] ioArgs = new String[] { "input/score", "output" };
String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: Score Average <in> <out>");
System.exit(2);
} Job job = new Job(conf, "Score Average");
job.setJarByClass(Score.class);
// 设置Map、Combine和Reduce处理类
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
// 设置输出类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 将输入的数据集分割成小数据块splites,提供一个RecordReder的实现
job.setInputFormatClass(TextInputFormat.class);
// 提供一个RecordWriter的实现,负责数据输出
job.setOutputFormatClass(TextOutputFormat.class);
// 设置输入和输出目录
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

编译

命令

javac Score.java

依赖错误

如果出现如下错误:

mint@lenovo ~/Desktop/hadoop $ javac Score.java
Score.java:4: error: package org.apache.hadoop.conf does not exist
import org.apache.hadoop.conf.Configuration;
^
Score.java:5: error: package org.apache.hadoop.fs does not exist
import org.apache.hadoop.fs.Path;
^
Score.java:6: error: package org.apache.hadoop.io does not exist
import org.apache.hadoop.io.IntWritable;
^
Score.java:7: error: package org.apache.hadoop.io does not exist
import org.apache.hadoop.io.LongWritable;
^
Score.java:8: error: package org.apache.hadoop.io does not exist
import org.apache.hadoop.io.Text;

尝试修改环境变量CLASSPATH

sudo vim /etc/profile
# 添加如下内容
export HADOOP_HOME=/usr/local/hadoop # 如果没设置的话, 路径是hadoop安装目录
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH # 如果没设置的话
export CLASSPATH=$($HADOOP_HOME/bin/hadoop classpath):$CLASSPATH

source /etc/profile

然后重复上述编译命令.

打包

编译之后会生成三个class文件:

mint@lenovo ~/Desktop/hadoop $ ls | grep class
Score.class
Score$Map.class
Score$Reduce.class

使用tar程序打包class文件.

tar -cvf Score.jar ./Score*.class

会生成Score.jar文件.

提交运行

样例输入

mint@lenovo ~/Desktop/hadoop $ ls | grep txt
chinese.txt
english.txt
math.txt
mint@lenovo ~/Desktop/hadoop $ cat chinese.txt
Zhao 98
Qian 9
Sun 67
Li 23
mint@lenovo ~/Desktop/hadoop $ cat english.txt
Zhao 93
Qian 42
Sun 87
Li 54
mint@lenovo ~/Desktop/hadoop $ cat math.txt
Zhao 38
Qian 45
Sun 23
Li 43

上传到HDFS

hdfs dfs -put ./*/txt input/score

mint@lenovo ~/Desktop/hadoop $ hdfs dfs -ls input/score
Found 3 items
-rw-r--r-- 1 mint supergroup 28 2017-01-11 23:25 input/score/chinese.txt
-rw-r--r-- 1 mint supergroup 29 2017-01-11 23:25 input/score/english.txt
-rw-r--r-- 1 mint supergroup 29 2017-01-11 23:25 input/score/math.txt

运行

mint@lenovo ~/Desktop/hadoop $ hadoop jar Score.jar Score input/score output
17/01/11 23:26:26 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/01/11 23:26:27 INFO input.FileInputFormat: Total input paths to process : 3
17/01/11 23:26:27 INFO mapreduce.JobSubmitter: number of splits:3
17/01/11 23:26:27 INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address
17/01/11 23:26:27 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1484147224423_0006
17/01/11 23:26:27 INFO impl.YarnClientImpl: Submitted application application_1484147224423_0006
17/01/11 23:26:27 INFO mapreduce.Job: The url to track the job: http://lenovo:8088/proxy/application_1484147224423_0006/
17/01/11 23:26:27 INFO mapreduce.Job: Running job: job_1484147224423_0006
17/01/11 23:26:33 INFO mapreduce.Job: Job job_1484147224423_0006 running in uber mode : false
17/01/11 23:26:33 INFO mapreduce.Job: map 0% reduce 0%
17/01/11 23:26:40 INFO mapreduce.Job: map 67% reduce 0%
17/01/11 23:26:41 INFO mapreduce.Job: map 100% reduce 0%
17/01/11 23:26:46 INFO mapreduce.Job: map 100% reduce 100%
17/01/11 23:26:46 INFO mapreduce.Job: Job job_1484147224423_0006 completed successfully
17/01/11 23:26:47 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=129
FILE: Number of bytes written=471147
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=443
HDFS: Number of bytes written=29
HDFS: Number of read operations=12
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=3
Launched reduce tasks=1
Data-local map tasks=3
Total time spent by all maps in occupied slots (ms)=15538
Total time spent by all reduces in occupied slots (ms)=2551
Total time spent by all map tasks (ms)=15538
Total time spent by all reduce tasks (ms)=2551
Total vcore-milliseconds taken by all map tasks=15538
Total vcore-milliseconds taken by all reduce tasks=2551
Total megabyte-milliseconds taken by all map tasks=15910912
Total megabyte-milliseconds taken by all reduce tasks=2612224
Map-Reduce Framework
Map input records=12
Map output records=12
Map output bytes=99
Map output materialized bytes=141
Input split bytes=357
Combine input records=12
Combine output records=12
Reduce input groups=4
Reduce shuffle bytes=141
Reduce input records=12
Reduce output records=4
Spilled Records=24
Shuffled Maps =3
Failed Shuffles=0
Merged Map outputs=3
GC time elapsed (ms)=462
CPU time spent (ms)=2940
Physical memory (bytes) snapshot=992215040
Virtual memory (bytes) snapshot=7659905024
Total committed heap usage (bytes)=732430336
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=86
File Output Format Counters
Bytes Written=29

输出

mint@lenovo ~/Desktop/hadoop $ hdfs dfs -ls output
Found 2 items
-rw-r--r-- 1 mint supergroup 0 2017-01-11 23:26 output/_SUCCESS
-rw-r--r-- 1 mint supergroup 29 2017-01-11 23:26 output/part-r-00000
mint@lenovo ~/Desktop/hadoop $ hdfs dfs -cat output/part-r-00000
Li 40
Qian 32
Sun 59
Zhao 76

简单的java Hadoop MapReduce程序(计算平均成绩)从打包到提交及运行的更多相关文章

  1. 使用Python实现Hadoop MapReduce程序

    转自:使用Python实现Hadoop MapReduce程序 英文原文:Writing an Hadoop MapReduce Program in Python 根据上面两篇文章,下面是我在自己的 ...

  2. mapreduce实现学生平均成绩

    思路: 首先从文本读入一行数据,按空格对字符串进行切割,切割后包含学生姓名和某一科的成绩,map输出key->学生姓名    value->某一个成绩 然后在reduce里面对成绩进行遍历 ...

  3. 【MFC学习笔记-作业9-基于单击响应的计算平均成绩】【】

    要求..单击出现 一个输入成绩的框,点确定后,计算平均成绩 意义很大~ 完成对话框   再写个鼠标点击的响应部分 鼠标点击的响应部分为难点.... void CWj1401_0302140107_9V ...

  4. [python]使用python实现Hadoop MapReduce程序:计算一组数据的均值和方差

    这是参照<机器学习实战>中第15章“大数据与MapReduce”的内容,因为作者写作时hadoop版本和现在的版本相差很大,所以在Hadoop上运行python写的MapReduce程序时 ...

  5. HDFS基本命令与Hadoop MapReduce程序的执行

    一.HDFS基本命令 1.创建目录:-mkdir [jun@master ~]$ hadoop fs -mkdir /test [jun@master ~]$ hadoop fs -mkdir /te ...

  6. 用Python语言写Hadoop MapReduce程序Writing an Hadoop MapReduce Program in Python

    In this tutorial I will describe how to write a simple MapReduce program for Hadoop in the Python pr ...

  7. MapReduce编程:平均成绩

    问题描述 现在有三个文件分别代表学生的各科成绩,编程求各位同学的平均成绩.                     编程思想 map函数将姓名作为key,成绩作为value输出,reduce根据key ...

  8. Python实现Hadoop MapReduce程序

    1.概述 Hadoop Streaming提供了一个便于进行MapReduce编程的工具包,使用它可以基于一些可执行命令.脚本语言或其他编程语言来实现Mapper和 Reducer,从而充分利用Had ...

  9. Intellij idea开发Hadoop MapReduce程序

    1.首先下载一个Hadoop包,仅Hadoop即可. http://mirrors.hust.edu.cn/apache/hadoop/common/hadoop-2.6.0/hadoop-2.6.0 ...

随机推荐

  1. python 一遍式四则运算

    #!/usr/bin/python #coding: utf-8 INTEGER = 'INTEGER' PLUS = '+' MINUS = '-' MUL = '*' DIV = '/' LC = ...

  2. Firefox 插件 JSview是一套比较实用的JS,CSS文件查看工具,很方便,很快捷地查看页面引用了哪些文件,作为Web前端开发者是一套必备的插件,由于Firefox升级过快,插件很快不兼容了,这里对插件做了一些调整,可以兼容最新Firefox浏览器(目前FireFox 21)

    JSView Firefox Plugins Download  点击下载

  3. CI如何在子目录下可以设置默认控制器

    CI建立大型大型的应用程序,需要创建子文件夹在application/controllers下建立文件夹app1app1目录下有多个控制器,ca.php,cb.php我希望定义app1下的默认控制器, ...

  4. Android之ListView的快速滑动模式:fastScrollEnabled以及滑块的自定义

    http://www.jcodecraeer.com/a/anzhuokaifa/androidkaifa/2014/0917/1690.html http://blog.csdn.net/xyang ...

  5. LVS+Keppalived实现高可用负载均衡

    三.LVS Keppalived的安装 3.1.环境描述 LVS server1 (Master):10.0.0.202 虚拟IP为:10.0.0.210 LVS server2 ( Slave ) ...

  6. python数据类型之 dict(map)

    字典  一.创建字典  方法①:  >>> dict1 = {}  >>> dict2 = {'name': 'earth', 'port': 80}  >& ...

  7. Linux 环境编译安装mysql (源码安装包)

    标注: Linux需要先配置网络yum源,确定yum能在线安装软件包,方便测试过程中安装部分依赖包.配置163网易提示的网络yum源参考博客  http://www.cnblogs.com/zoulo ...

  8. bitmap格式分析(转)

    源:bitmap格式分析 参考:bitmap图像介绍 最近正在着手开发一个图片库,也就是实现对常见图片格式的度写操作.作为总结与积累,我会把这些图片格式以及加载的实现写在我的Blog上. 说到图片,位 ...

  9. HTML编辑模式下制作表格

    前面有朋友问如何做图文并茂的音乐帖子,的确音乐能以表格式做出来,更能让人过目不忘,何况帖子制作过程本身就是创作,包含了制作人对音乐的理解和爱好.以下简单介绍用代码HTML制作表格,希望对大家有所帮助. ...

  10. iOS 主动抛出异常

    http://blog.csdn.net/jymn_chen/article/details/38096749 http://blog.sina.com.cn/s/blog_7270a06c0101b ...