通常会在IDE中编制程序，然后打成jar包，然后提交到集群，最常用的是创建一个Maven项目，利用Maven来管理jar包的依赖。
一、生成WordCount的jar包
1. 打开IDEA，File→New→Project→Maven→Next→填写Groupld和Artifactld→Next→Finish
2. 配置Maven的pom.xml（配置好pom.xml以后，点击Enable Auto-Import即可）：
<?xml version="1.0" encoding="UTF-8"?>
<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>
<groupId>com.wu</groupId>
<artifactId>sparkWordCount</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.10.6</scala.version>
<scala.compat.version>2.10</scala.compat.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.5.2</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>1.5.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.2</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-make:transitive</arg>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.18.1</version>
<configuration>
<useFile>false</useFile>
<disableXmlReport>true</disableXmlReport>
<includes>
<include>**/*Test.*</include>
<include>**/*Suite.*</include>
</includes>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.wu.WordCount</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>注意：这里需要修改Hadoop版本
3：将src/main/java和src/test/java分别修改成src/main/scala和src/test/scala，与pom.xml中的配置保持一致（）；
操作：java→Refactor→Rename
4：新建一个com.bie包，再新建一个scala class，类型为Object，spark程序如下：
package com.wu
import org.apache.spark.{SparkConf, SparkContext}
object WordCount {
def main(args: Array[String]): Unit = {
//创建SparkConf()并且设置App的名称
val conf = new SparkConf().setAppName("wordCount");
//创建SparkContext,该对象是提交spark app的入口
val sc = new SparkContext(conf);
//使用sc创建rdd,并且执行相应的transformation和action
sc.textFile(args(0)).flatMap(_.split(" ")).map((_ ,1)).reduceByKey(_ + _,1).sortBy(_._2,false).saveAsTextFile(args(1));
//停止sc，结束该任务
sc.stop();
}
}5. 修改pom.xml中的mainClass，使其和自己的类路径对应起来：
6. 使用Maven打包：点击IDEA右侧的Maven Project选项，点击Lifecycle,选择clean和package，然后点击Run Maven Build：
等待编译完成，选择编译成功的jar包，target/sparkWordCount-1.0-SNAPSHOT.jar
二、运行
1. 打开xshell，文件→新建连接
新建好后输入用户名和密码，建立连接。
2. 使用Xftp新建文件传输（Ctrl+Alt+F），将刚刚生成的jar包和WordCount拖拽至 /home/hdfs目录下
3. 使用Xshell将WordCount.txt上传至hdfs系统
切换至hdfs用户：[root@data6 ~]# su hdfs
到spark的bin目录下：[hdfs@data6 root]$ cd /home/hdfs/software/spark/bin
在hdfs系统中新建input文件夹：hadoop fs -mkdir /input
查看是否新建成功：[hdfs@data6 bin]$ cd /home/hdfs/software/hadoop/bin #转到该目录下
[hdfs@data6 bin]$ ./hadoop fs -ls /
将txt文件上传至input文件夹：[hdfs@data6 root]$ cd /home/hdfs/software/spark/bin #转回到该目录
[hdfs@data6 bin]$ hadoop fs -put /home/hdfs/WordCount.txt /input
查看是否上传成功：[hdfs@data6 bin]$ cd /home/hdfs/software/hadoop/bin #转到该目录下
[hdfs@data6 bin]$ ./hadoop fs -ls /input
返回hdfs用户根目录：cd ~
使用spark-submit命令提交Spark应用：[hdfs@data6 ~]$ /home/hdfs/software/spark/bin/spark-submit --class com.bie.WordCount sparkWordCount-1.0-SNAPSHOT.jar hdfs://data2.cshdp.com:9000/input/WordCount.txt hdfs://data2.cshdp.com:9000/output
查看运行结果：[hdfs@data6 bin]$ cd /home/hdfs/software/hadoop/bin #转到该目录下
[hdfs@data6 bin]$ ./hadoop fs -ls /output
[hdfs@data6 bin]$ ./hadoop fs -cat /output/part-00000 #查看文件内容
