1、首选创建项目，创建方法请看：https://blog.csdn.net/sunxiaoju/article/details/101229620
2、添加远程同步目录(或者叫远程映射)，首选选择Tools->Deployment->Configuration...，如下图所示：
3、然后选择+号，选择SFTP，如下图所示：
4、输入名称，如下图所示：
5、输入要远程执行spark的host，用户名和密码，如下图所示：
6、输入之后进行测试SFTP是否成功，如果不成功请检查远程主机是否支持SFTP，如下图所示：
7、然后输入远程目录，如下图所示：
8、选择Mappings选项，配置本地目录和远程的二级目录，如下图所示：
9、配置远程目录时，可以选择后边的文件夹图标，可以列出二级目录，如下图所示：
10、如果没有要选择的目录，可以新建一个，然后在选择，如下图所示：
11、选择之后就会自动填到路径中，如下图所示：
12、开始配置编译好之后自动上传至远程服务器上，选择Tools->Deployment->Options...如下图所示：
13、在Upload changed files automatiically to the default server中选择Always，如下图所示：
14、此时在选择Tools->Deployment就会看到Automatic Upload（always）打上了对勾，并且可以选择Browse Remote Host浏览全程目录，如下图所示：
15、在Browse Remote Host就可以看到远程目录了，如下图所示：
16、修改代码，代码如下：
package com.spark
import org.apache.spark.{SparkConf, SparkContext}
object WordCount {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("WorkCount")
.setMaster("spark://master:7077")
val sc = new SparkContext(conf)
//sc.addJar("E:\\sunxj\\idea\\spark\\out\\artifacts\\spark_jar\\spark.jar")
//val line = sc.textFile(args(0))
val file=sc.textFile("hdfs://master:9000/user_data/worldcount.txt")
val rdd = file.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)
rdd.collect()
rdd.foreach(println)
rdd.collectAsMap().foreach(println)
}
}
如下图所示：
17、pom.xml文件内容如下：
<?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>sparktest</groupId>
<artifactId>spark</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.5</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<version>2.15.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>3.1.1</version>
<configuration>
<archive>
<manifest>
<mainClass>com.spark.WordCount</mainClass>
</manifest>
</archive>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
注意：如果没有以下内容会出现无法将scala文件打包到jar的
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<version>2.15.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
在执行时有可能会出现
Listening for transport dt_socket at address: 8888
java.lang.ClassNotFoundException: WordCount
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:175)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:689)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
如下图所示：
18、然后执行mvn package命令进行打包，如下图所示：
19、此时会在项目目录中生成一个target目录，如下图所示：
20、然后查看远程目录中是否有该目录，如下图所示：
21、然后在远程进入到该目录执行：
spark-submit --class com.spark.WordCount --master spark://master:7077 --driver-java-options "-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8888" spark-1.0-SNAPSHOT-jar-with-dependencies.jar
命令解释：
-Xdebug 启用调试特性
-Xrunjdwp 启用JDWP实现，包含若干子选项：
transport=dt_socket JPDA front-end和back-end之间的传输方法。dt_socket表示使用套接字传输。
address=8888 JVM在8888端口上监听请求，这个设定为一个不冲突的端口即可。
server=y y表示启动的JVM是被调试者。如果为n，则表示启动的JVM是调试器。
suspend=y y表示启动的JVM会暂停等待，直到调试器连接上才继续执行。suspend=n，则JVM不会暂停等待。
如下图所示：
22、然后在idea中配置远程调试，首先选择WordCount，然后选择Edit Configurations,如下图所示：：
23、然后点击+号，选择Remote，如下图所示：
24、输入名称，然后输入远程的主机名和端口，刚才spark-submit启动监听的端口是8888，因此这里也要设置8888，也可以换做其他端口，如下图所示：
25、然后启动调试，如下图所示：
26、此时远程就出现了调试内容，如下图所示：
27、此时查看web页面就可以查看结果，如下图所示：
在调试过程中出现的问题：
1、
19/09/24 20:41:28 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:41:43 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:41:58 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:42:13 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:42:28 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:42:43 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:42:58 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:43:13 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
19/09/24 20:43:28 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
^C19/09/24 20:43:30 INFO spark.SparkContext: Invoking stop() from shutdown hook
19/09/24 20:43:30 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/metrics/json,null}
重启spark集群即可。
2、使用maven打包scala的jar出现找不到主类，如：
java.lang.ClassNotFoundException: WordCount
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:175)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:689)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
这是由于没有加入scala插件，如下图所示：
参考了：https://note.youdao.com/ynoteshare1/index.html?id=2b9655588b6a22738081c80ec5cd094e&type=note
转载：https://blog.csdn.net/sunxiaoju/article/details/101312233