IntelliJ IDEA下Maven创建Scala项目的方法步骤

环境：IntelliJ IDEA

版本：Spark-2.2.1 Scala-2.11.0

利用 Maven 第一次创建 Scala 项目也遇到了许多坑

创建一个 Scala 的 WordCount 程序

第一步：IntelliJ IDEA下安装 Scala 插件

 

 

安装完 Scala 插件完成

第二步：Maven 下 Scala 下的项目创建

 

正常创建 Maven 项目（不会的看另一篇 Maven 配置）

第三步：Scala 版本的下载及配置

通过Spark官网下载页面http://spark.apache.org/downloads.html 可知“Note: Starting version 2.0, Spark is built with Scala 2.11 by default.”，建议下载Spark2.2对应的 Scala 2.11。

登录Scala官网http://www.scala-lang.org/，单击download按钮，然后再“Other Releases”标题下找到“下载2.11.0

根据自己的系统下载相应的版本
接下来就是配置Scala 的环境变量（跟 jdk 的配置方法一样）

输入 Scala -version 查看是否配置成功 会显示 Scala code runner version 2.11.0 – Copyright 2002-2013, LAMP/EPFL

 

 

 

选择自己安装 Scala 的路径

第四步：编写 Scala 程序

将其他的代码删除，不然在编辑的时候会报错

 

配置 pom.xml文件

在里面添加一个 Spark

 <properties>
  <scala.version>2.11.0</scala.version>
  <spark.version>2.2.1</spark.version>
 </properties>
 <dependency>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.11</artifactId>
   <version>${spark.version}</version>
  </dependency>

具体的 pom.xml 内容

<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/maven-v4_0_0.xsd">
 <modelVersion>4.0.0</modelVersion>
 <groupId>cn.spark</groupId>
 <artifactId>Spark</artifactId>
 <version>1.0-SNAPSHOT</version>
 <inceptionYear>2008</inceptionYear>
 <properties>
  <scala.version>2.11.0</scala.version>
  <spark.version>2.2.1</spark.version>
 </properties>


 <pluginRepositories>
  <pluginRepository>
   <id>scala-tools.org</id>
   <name>Scala-Tools Maven2 Repository</name>
   <url>http://scala-tools.org/repo-releases</url>
  </pluginRepository>
 </pluginRepositories>

 <dependencies>
  <dependency>
   <groupId>org.scala-lang</groupId>
   <artifactId>scala-library</artifactId>
   <version>${scala.version}</version>
  </dependency>
  <dependency>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.11</artifactId>
   <version>${spark.version}</version>
  </dependency>
  <dependency>
   <groupId>junit</groupId>
   <artifactId>junit</artifactId>
   <version>4.4</version>
   <scope>test</scope>
  </dependency>
  <dependency>
   <groupId>org.specs</groupId>
   <artifactId>specs</artifactId>
   <version>1.2.5</version>
   <scope>test</scope>
  </dependency>
 </dependencies>

 <build>
  <sourceDirectory>src/main/scala</sourceDirectory>
  <testSourceDirectory>src/test/scala</testSourceDirectory>
  <plugins>
   <plugin>
    <groupId>org.scala-tools</groupId>
    <artifactId>maven-scala-plugin</artifactId>
    <executions>
     <execution>
      <goals>
       <goal>compile</goal>
       <goal>testCompile</goal>
      </goals>
     </execution>
    </executions>
    <configuration>
     <scalaVersion>${scala.version}</scalaVersion>
     <args>
      <arg>-target:jvm-1.5</arg>
     </args>
    </configuration>
   </plugin>
   <plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-eclipse-plugin</artifactId>
    <configuration>
     <downloadSources>true</downloadSources>
     <buildcommands>
      <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
     </buildcommands>
     <additionalProjectnatures>
      <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
     </additionalProjectnatures>
     <classpathContainers>
      <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
      <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
     </classpathContainers>
    </configuration>
   </plugin>
  </plugins>
 </build>
 <reporting>
  <plugins>
   <plugin>
    <groupId>org.scala-tools</groupId>
    <artifactId>maven-scala-plugin</artifactId>
    <configuration>
     <scalaVersion>${scala.version}</scalaVersion>
    </configuration>
   </plugin>
  </plugins>
 </reporting>
</project>

编写 WordCount 文件

package cn.spark

import org.apache.spark.{SparkConf, SparkContext}

/**
 * Created by hubo on 2018/1/13
 */
object WordCount {
 def main(args: Array[String]) {
  var masterUrl = "local"
  var inputPath = "/Users/huwenbo/Desktop/a.txt"
  var outputPath = "/Users/huwenbo/Desktop/out"

  if (args.length == 1) {
   masterUrl = args(0)
  } else if (args.length == 3) {
   masterUrl = args(0)
   inputPath = args(1)
   outputPath = args(2)
  }

  println(s"masterUrl:$masterUrl, inputPath: $inputPath, outputPath: $outputPath")
  val sparkConf = new SparkConf().setMaster(masterUrl).setAppName("WordCount")
  val sc = new SparkContext(sparkConf)

  val rowRdd = sc.textFile(inputPath)
  val resultRdd = rowRdd.flatMap(line => line.split("\\s+"))
   .map(word => (word, 1)).reduceByKey(_ + _)

  resultRdd.saveAsTextFile(outputPath)
 }
}

var masterUrl = “local”

local代表自己本地运行，在 hadoop 上运行添加相应地址

在配置中遇到的错误，会写在另一篇文章里。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持自由互联。