1. 配置Maven环境
首先检查Windows是否配置了maven,进入cmd命令行，输入mvn -version命令，如果出现下图所示的 情形则表示满意配置maven。
从浏览器进入maven官网，下载maven压缩包：http://maven.apache.org/download.cgi。下载完后将其解压的一个自定义目录，然后配置环境变量。
进入环境变量配置页面，新建一个MAVEN_HOME变量，变量值为刚才解压的路径（进入能看到bin文件夹的路径）。
然后，在Path变量下添加MAVEN_HOME变量。
注意：老版本Windows直接在变量后面加上分号，然后加上%MAVEN_HOME%\bin。
回到命令行，再输入mvn -version，如果出现下图所示的情形则表明配置成功。
2. 在Eclipse中配置Maven
进入Eclipse，然后Window->Preferences->Maven，首先关联Maven安装路径待eclipse.
然后配置settings.xml文件，下面的本地库保存路径可以自定义（一般默认就好）。
3. 使用Maven管理多个MapReduce项目
首先新建一个maven项目。
*（该图和我最后的名称不同，因为修改过，不过不影响，按照你自己的来即可）
然后新建一个WordCount.java类，代码可以从官网下载：http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Source_Code
此时，WordCount.java类肯定是一片红，有很多报错，这是因为我们目前还没有引入所需要的jar文件。接下来是通过Maven框架引入所依赖的jar文件，这和之前我们直接导入然后Build Path的方法不同。我们现在使用Maven框架来进行管理，我们只需要在pom.xml文件中写入以下内容就可以实现jar文件的自动下载和管理。配置完后保存文件，然后Maven会自动下载好所需要的jar文件，报错也都会给解决掉。
pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
//下面两行改为自己新建项目时的Id
<groupId>com.hadoop</groupId>
<artifactId>maven</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>jar</packaging>
<name>maven</name>
<url>http://maven.apache.org</url>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<hadoop.version>2.6.0</hadoop.version>
</properties>
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>jdk.tools</groupId>
<artifactId>jdk.tools</artifactId>
<version>1.8</version> //改成自己对于的JDK版本号
<scope>system</scope>
<systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.1</version>
<executions>
<!-- Run shade goal on package phase -->
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<!-- add Main-Class to manifest file -->
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.hadoop.mavenPro.MyDriver</mainClass> //根据自己的项目路径修改
</transformer>
</transformers>
<createDependencyReducedPom>false</createDependencyReducedPom> //该句很关键，必须配置为false
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
接下来是调试Maven项目中的MapReduce程序。
在右键WordCount类选择：Run as->Run Configuration。
搜索主类：
注意：如果搜不到对应类，请将Search上面的Project选择为自己所新建的项目。
设置输入输出路径
然后点击运行。
运行结果如下：
输出目录如下
那么，Maven如何管理多个MapReduce程序呢？
我们再新建一个MapReduce程序，于是我又新建了一个2.0版本的WordCount类WordCount2.java。然后配置方法同上，只是输出路径要修改一下。
运行结果如下
根据以上的本地调试证明两个MapReduce程序都没有问题，以下就是多个MapReduce程序的管理。
Maven是通过ProgramDriver类来进行管理的。首先我们先新建一个MyDriver类，代码如下：
MyDriver.java
package com.hadoop.mavenPro;
import org.apache.hadoop.util.ProgramDriver;
/**
* @author Zimo
*
*/
public class MyDriver {
public static void main(String argv[]){
int exitCode = -1;
ProgramDriver pgd = new ProgramDriver();
try {
pgd.addClass("wordcount", WordCount.class, //设置项目别名
"A map/reduce program that counts the words in the input files."); //添加项目描述
pgd.addClass("wordcount2", WordCount2.class,
"A map/reduce program that counts the words in the input files.");
exitCode = pgd.run(argv);
}
catch(Throwable e){
e.printStackTrace();
}
System.exit(exitCode);
}
}
通过cmd命令行打包项目：进入项目路径->clean->package。
然后回到Eclipse，右键项目刷新一下，target目录下也出现了相应的jar包了，可以直接上传到Hadoop集群运行。
然后登陆到Hadoop集群并启动。
[hadoop@centpy ~]$ cd $HADOOP_HOME //进入Hadoop路径
[hadoop@centpy hadoop-2.6.0]$ pwd
/usr/hadoop/hadoop-2.6.0
[hadoop@centpy hadoop-2.6.0]$ sbin/start-all.sh //启动集群
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [centpy]
centpy: starting namenode, logging to /usr/hadoop/hadoop-2.6.0/logs/hadoop-hadoop-namenode-centpy.out
centpy: starting datanode, logging to /usr/hadoop/hadoop-2.6.0/logs/hadoop-hadoop-datanode-centpy.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/hadoop/hadoop-2.6.0/logs/hadoop-hadoop-secondarynamenode-centpy.out
starting yarn daemons
starting resourcemanager, logging to /usr/hadoop/hadoop-2.6.0/logs/yarn-hadoop-resourcemanager-centpy.out
centpy: starting nodemanager, logging to /usr/hadoop/hadoop-2.6.0/logs/yarn-hadoop-nodemanager-centpy.out
[hadoop@centpy hadoop-2.6.0]$ jps
2113 NameNode
2643 NodeManager
2212 DataNode
2794 Jps
2542 ResourceManager
2399 SecondaryNameNode
新建一个文件夹用于该项目文件的存放。
[hadoop@centpy hadoop-2.6.0]$ hadoop fs -mkdir /maven
[hadoop@centpy hadoop-2.6.0]$ hadoop fs -ls /
Found 7 items
drwxr-xr-x - hadoop hadoop 0 2018-04-14 14:20 /hdfsOutput
drwxr-xr-x - hadoop supergroup 0 2018-04-25 09:37 /maven
drwxrwxrwx - hadoop supergroup 0 2018-04-13 22:10 /phone
drwxr-xr-x - hadoop hadoop 0 2018-04-14 14:43 /test
drwx------ - hadoop hadoop 0 2018-04-13 22:10 /tmp
drwxr-xr-x - hadoop hadoop 0 2018-04-14 14:34 /weather
drwxr-xr-x - hadoop hadoop 0 2018-04-14 15:04 /weibo
上传一个输入文件到/maven。
[hadoop@centpy hadoop-2.6.0]$ vi word.txt //新建一个文件作为输入文件
hadoop maven
hadoop maven
hadoop maven
[hadoop@centpy hadoop-2.6.0]$ hadoop fs -put word.txt /maven //将输入文件放到HDFS中
[hadoop@centpy hadoop-2.6.0]$ hadoop fs -ls /maven
Found 1 items
-rw-r--r-- 1 hadoop supergroup 39 2018-04-25 09:43 /maven/word.txt
上传项目jar包
[hadoop@centpy hadoop-2.6.0]$ rz //上传之前打包的jar文件
[hadoop@centpy hadoop-2.6.0]$ ls
bin lib libhadoop.so.1.0.0 LICENSE.txt sbin word.txt
data libexec libhadooputils.a logs share
etc libhadoop.a libhdfs.a maven-1.0-SNAPSHOT.jar Temperature.jar
include libhadooppipes.a libhdfs.so NOTICE.txt WeiboCount.jar
jar libhadoop.so libhdfs.so.0.0.0 README.txt WordCount.jar
运行项目
[hadoop@centpy hadoop-2.6.0]$ hadoop jar maven-1.0-SNAPSHOT.jar wordcount /maven/word.txt /maven/output //运行程序
//由于pom.xml中配置了主类，出现可以直接找到Driver类，所以不用再像以前一样写全包路径，直接写Driver类中的项目别名就行了！
18/04/25 10:35:02 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/04/25 10:35:03 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/04/25 10:35:14 INFO input.FileInputFormat: Total input paths to process : 1
18/04/25 10:35:14 INFO mapreduce.JobSubmitter: number of splits:1
18/04/25 10:35:15 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1524619938432_0001
18/04/25 10:35:15 INFO impl.YarnClientImpl: Submitted application application_1524619938432_0001
18/04/25 10:35:15 INFO mapreduce.Job: The url to track the job: http://centpy:8088/proxy/application_1524619938432_0001/
18/04/25 10:35:15 INFO mapreduce.Job: Running job: job_1524619938432_0001
18/04/25 10:35:41 INFO mapreduce.Job: Job job_1524619938432_0001 running in uber mode : false
18/04/25 10:35:41 INFO mapreduce.Job: map 0% reduce 0%
18/04/25 10:35:52 INFO mapreduce.Job: map 100% reduce 0%
18/04/25 10:36:04 INFO mapreduce.Job: map 100% reduce 100%
18/04/25 10:36:05 INFO mapreduce.Job: Job job_1524619938432_0001 completed successfully
18/04/25 10:36:05 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=31
FILE: Number of bytes written=211407
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=137
HDFS: Number of bytes written=17
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=8939
Total time spent by all reduces in occupied slots (ms)=6521
Total time spent by all map tasks (ms)=8939
Total time spent by all reduce tasks (ms)=6521
Total vcore-seconds taken by all map tasks=8939
Total vcore-seconds taken by all reduce tasks=6521
Total megabyte-seconds taken by all map tasks=9153536
Total megabyte-seconds taken by all reduce tasks=6677504
Map-Reduce Framework
Map input records=3
Map output records=6
Map output bytes=63
Map output materialized bytes=31
Input split bytes=98
Combine input records=6
Combine output records=2
Reduce input groups=2
Reduce shuffle bytes=31
Reduce input records=2
Reduce output records=2
Spilled Records=4
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=283
CPU time spent (ms)=3120
Physical memory (bytes) snapshot=302731264
Virtual memory (bytes) snapshot=4132818944
Total committed heap usage (bytes)=161746944
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=39
File Output Format Counters
Bytes Written=17
输出结果可以从浏览器进入文件系统查看。
同样，运行我们的2.0版本的WordCount程序只需要将运行命令中的wordcount修改为wordcount2即可。
运行后文件系统中也出现了结果目录
到此，通过Maven框架管理多个MapReduce项目的步骤就到此结束了，大家可以多建几个MapReduce项目进行进一步测试。
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