hadoop学习笔记2：eclipse开发MapReduce

hadoop学习笔记2：eclipse开发MapReduce

伪分布式搭建见：Hadoop学习笔记1：伪分布式环境搭建

环境：

虚拟机：Ubuntu16.0

jdk1.8.0_111,

eclipse4.6.0,

hadoop-2.7.3,

下载地址见：

http://blog.csdn.net/yhhyhhyhhyhh/article/details/53945810

hadoop-eclipse-plugin-2.7.3,

编译好的jar包下载地址:

http://download.csdn.net/download/yhhyhhyhhyhh/9726479

1.eclipse安装及快捷方式

1)解压并测试启动eclipse

sudo tar -zxvf ~/eclipse-jee-neon-1a-linux-gtk-x86_64.tar.gz -C/usr/local

cd /usr/local/eclipse

./eclipse

2)eclipse快捷方式

创建eclipse.desktop

sudo vim /usr/share/applications/eclipse.desktop

在eclipse.desktop添加以下内容

[Desktop Entry]
Version=1.0
Encoding=UTF-8
Name=Eclipse4.6.0
Exec=eclipse
TryExec=eclipse
Comment=Eclipse4.6.0,EclipseSDK
Exec=/usr/local/eclipse/eclipse
Icon=/usr/local/eclipse/icon.xpm
Terminal=false
Type=Application
Categories=Application;Development;

创建启动器

sudo vim /usr/bin/eclipse

在其中添加如下内容

#!/bin/sh
export MOZILLA_FIVE_HOME="/usr/lib/mozilla/"
export ECLIPSE_HOME="/usr/local/eclipse"
$ECLIPSE_HOME/eclipse $*

添加可执行权限

sudo chmod +x /usr/bin/eclipse

注意：如果提示找到不jdk环境：在eclipse下建立软连接

yhh@ubuntu:/usr/local/eclipse$ sudo ln -s /usr/local/jdk1.8.0_111/jre/ jre

eclipse快捷方式创建成功如下：





2. hadoop-eclipse-plugin-2.7.3的安装

yhh@ubuntu:~$ cd /usr/local/eclipse/
yhh@ubuntu:/usr/local/eclipse$ sudo cp ~/hadoop-eclipse-plugin-2.7.3.jar ./plugins/
#复制到eclipse下的plugins 目录下
yhh@ubuntu:/usr/local/eclipse$ ./eclipse -clean  # 添加插件后需要用这种方式使插件生效

3. hadoop插件在eclipse中的配置

安装好Hadoop-Eclipse-Plugin插件，启动 Eclipse 后就可以在左侧的Project Explorer中看到 DFS Locations（若看到的是 welcome 界面，点击左上角的 x 关闭就可以看到了）。

注意：熟悉Java开发的,对eclipse视图切换应该很熟悉，不熟悉的话，如果DFS Locations或者Map/Reduce Locations面板没出现的话。只要配置了hadoop-eclispe插件，可以打开map/reduce视图，reset，见2）打开Map/Reduce视图，这里可以先配置，不影响。

1）配置hadoop安装目录





2）打开Map/Reduce视图





注意：熟悉Java开发的,对eclipse视图切换应该很熟悉，不熟悉的话，如果DFS Locations或者Map/Reduce Locations面板没出现的话。只要配置了hadoop-eclispe插件，可以打开map/reduce视图，reset,如下图。





3）建立与hadoop集群的连接





注意：hadoop需要在终端中启动，这里才会链接上。

hadoop启动前:



hadoop启动后:



4.eclipse中建立hadoop工程

这里以hadoop自带的wordcount例子测试。

在Hadoop学习笔记1：伪分布式环境搭建中，伪分布式、分布式的Hadoop数据是在hdfs下的目录。所以需要预先将数据上传到hdfs下的目录中。

1）准备数据

在/usr/local/input创建三个txt文件，f1,f2的内容随便输入，f3输入以下：

127.0.0.1,-,-,[08/May/2014:13:42:40 +0800],GET / HTTP/1.1,200,11444

127.0.0.1,-,-,[08/May/2014:13:42:42 +0800],GET /jygl/jaxrs/teachingManage/ClassBatchPlanService/getCurrentClassPlanVO HTTP/1.1,204,-

127.0.0.1,-,-,[08/May/2014:13:42:42 +0800],GET /jygl/jaxrs/teachingManage/ClassBatchPlanService/getCurClassPlanVO HTTP/1.1,204,-

127.0.0.1,-,-,[08/May/2014:13:42:47 +0800],GET /jygl/jaxrs/right/isValidUserByType/1-admin-superadmin HTTP/1.1,200,20

127.0.0.1,-,-,[08/May/2014:13:42:47 +0800],GET /jygl/jaxrs/right/getUserByLoginName/admin HTTP/1.1,200,198

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/right_login2home?loginName=admin&password=superadmin&type=1 HTTP/1.1,200,2525

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/style/style.css HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/js/tree.js HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/js/jquery.js HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/js/frame.js HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/images/logo.png HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/images/leftmenu_bg.gif HTTP/1.1,404,1105

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/menuList.jsp HTTP/1.1,200,47603

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/style/images/header_bg.jpg HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/images/allmenu.gif HTTP/1.1,404,1093

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:47 +0800],GET /jyglFront/mainView/navigate/images/toggle_menu.gif HTTP/1.1,404,1105

127.0.0.1,-,-,[08/May/2014:13:42:48 +0800],GET /jygl/jaxrs/article/getArticleList/10-1 HTTP/1.1,200,20913

127.0.0.1,-,-,[08/May/2014:13:42:48 +0800],GET /jygl/jaxrs/article/getTotalArticleRecords HTTP/1.1,200,22

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:48 +0800],GET /jyglFront/baseInfo_articleList?flag=1 HTTP/1.1,200,8989

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:42:48 +0800],GET /jyglFront/mainView/studentView/style/images/nav_10.png HTTP/1.1,404,1117

127.0.0.1,-,-,[08/May/2014:13:43:21 +0800],GET /jygl/jaxrs/right/isValidUserByType/1-admin-superadmin HTTP/1.1,200,20

127.0.0.1,-,-,[08/May/2014:13:43:21 +0800],GET /jygl/jaxrs/right/getUserByLoginName/admin HTTP/1.1,200,198

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/right_login2home?loginName=admin&password=superadmin&type=1 HTTP/1.1,200,2525

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/js/tree.js HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/js/jquery.js HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/js/frame.js HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/style/style.css HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/menuList.jsp HTTP/1.1,200,47603

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/images/logo.png HTTP/1.1,304,-

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/images/leftmenu_bg.gif HTTP/1.1,404,1105

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/images/toggle_menu.gif HTTP/1.1,404,1105

0:0:0:0:0:0:0:1,-,-,[08/May/2014:13:43:21 +0800],GET /jyglFront/mainView/navigate/style/images/header_bg.jpg HTTP/1.1,304,-

127.0.0.1,-,-,[08/May/2014:13:43:21 +0800],GET /jygl/jaxrs/article/getArticleList/10-1 HTTP/1.1,200,20913

命令：

yhh@ubuntu:/usr/local/hadoop$ cd ..
yhh@ubuntu:/usr/local$ sudo mkdir ./input
yhh@ubuntu:/usr/local$ cd ./input
yhh@ubuntu:/usr/local/input$ sudo vim f1.txt
yhh@ubuntu:/usr/local/input$ sudo vim f2.txt
yhh@ubuntu:/usr/local/input$ sudo vim f3.txt

在hdfs下建立文件夹 /user/yhh/input(**为用户名我的是yhh)

命令：

yhh@ubuntu:/usr/local$ cd ./hadoop/
yhh@ubuntu:/usr/local/hadoop$ ./bin/hdfs dfs -ls /
#可以先查看下hdfs下的文件级文件夹
yhh@ubuntu:/usr/local/hadoop$ ./bin/hdfs dfs -mkdir /user
yhh@ubuntu:/usr/local/hadoop$ ./bin/hdfs dfs -mkdir /user/yhh
yhh@ubuntu:/usr/local/hadoop$ ./bin/hdfs dfs -mkdir /user/yhh/input
yhh@ubuntu:/usr/local/hadoop$ ./bin/hdfs dfs -put /usr/local/input/f*.txt /user/yhh/input
#将数据上传到hdfs下的/user/yhh/input（如果已存在会有提示）

上传数据到hdfs后：



2）Eclipse建立hadoop工程测试（以WordCount为例）

在Eclipse中创建MapReduce项目和创建普通的java项目一样。



创建WordCount 项目后，添加类。

选择 New -> Class需要填写两个地方：

在 Package 处填写 org.apache.hadoop.examples；

在 Name 处填写 WordCount。

在WordCount.java：添加如下代码（可以将自动生成的删除）

package org.apache.hadoop.examples;
import java.io.IOException;
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.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;
import org.apache.hadoop.util.GenericOptionsParser;

@SuppressWarnings("unused")
public class WordCount {

public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
//通过配置工程输入参数
//String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
//代码中直接输入参数,从hdfs输入（具体数据在hdfs://localhost:9000/user/yhh/input）
String[] otherArgs=new String[]{"input/f1.txt","output"}; //此处可以指定hdfs下具体的输入文件
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
//每次运行时自动删除输出目录
Path outputPath = new Path(otherArgs[1]);
outputPath.getFileSystem(conf).delete(outputPath, true);
//1、设置job的基础属性
@SuppressWarnings("deprecation")
Job job = new Job(conf, "WordCount");
job.setJarByClass(WordCount.class);
//2、设置Map与Reudce的类
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
//3、设置map与reduce的输出键值类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//4、设置输入输出路径
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
//5、运行程序
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

注：如果需要通过配置工程输入参数:：

//通过配置工程输入参数
//String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

注意：新建工程运行所需hadoop配置文件：

在终端中通过命令复制到/workspace/WordCount/src下（具体自己的，在命令中修改）

cp /usr/local/hadoop/etc/hadoop/core-site.xml ~/workspace/WordCount/src
cp /usr/local/hadoop/etc/hadoop/hdfs-site.xml ~/workspace/WordCount/src
cp /usr/local/hadoop/etc/hadoop/log4j.properties ~/workspace/WordCount/src

注意：1）数据上传到hdfs

2）终端执行复制配置文件

3）运行成功后，在eclipse查看输出文件

都需要Refresh或者重启eclipse工程，相关位置才会出现对应的文件

4）输入输出已经在代码中指定，并且设置了运行自动删除上次的输出目录。hadoop job的简单属性设置在代码中。

下图Refresh或者重启eclipse工程：





测试成功：



f3.txt的测试：屏蔽掉测试f1.txt的代码，复制如下代码

package org.apache.hadoop.examples;
import java.io.IOException;
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;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCount {
enum Counter{
LINESKIP,
}
public static class CountMap extends Mapper<LongWritable,Text,Text,IntWritable>{
private static final IntWritable one = new IntWritable(1);
public void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException{
String line = value.toString();
try{
String[] lineSplit = line.split(",");
String requestUrl = lineSplit[4];
requestUrl = requestUrl.substring(requestUrl.indexOf(' ')+1, requestUrl.lastIndexOf(' '));
Text out = new Text(requestUrl);
context.write(out,one);
}catch(java.lang.ArrayIndexOutOfBoundsException e){
context.getCounter(Counter.LINESKIP).increment(1);
}
}
}
public static class CountReduce extends Reducer<Text,IntWritable,Text,IntWritable>{
public void reduce(Text key, Iterable<IntWritable> values,Context context)throws IOException{
int count =  0;
for(IntWritable v : values){
count = count + 1;
}
try {
context.write(key, new IntWritable(count));
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
public static void main(String[] args)throws Exception{
Configuration conf =  new Configuration();
//通过配置工程输入参数
//String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
//代码中直接输入参数,从hdfs输入（具体数据在hdfs://localhost:9000/user/yhh/input）
String[] otherArgs=new String[]{"input/f3.txt","output"};
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
//设置每次运行时自动删除输出目录
Path outputPath = new Path(otherArgs[1]);
outputPath.getFileSystem(conf).delete(outputPath, true);
Job job = new Job(conf, "WordCount");
//1、设置job的基础属性
job.setJarByClass(WordCount.class);
//2、设置Map与Reudce的类
job.setMapperClass(CountMap.class);
job.setReducerClass(CountReduce.class);
//3、设置map与reduce的输出键值类型
job.setOutputFormatClass(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//4、设置输入输出路径
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
//5、运行程序
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

测试成功：





参考：

大神的帖子：

http://www.powerxing.com/hadoop-build-project-using-eclipse/

https://my.oschina.net/132722/blog/168022

http://blog.csdn.net/jediael_lu/article/details/43416751

http://blog.csdn.net/liyong199012/article/details/25423221