環(huán)境：IntelliJ IDEA
版本：Spark-2.2.1 Scala-2.11.0
利用 Maven 第一次創(chuàng)建 Scala 項(xiàng)目也遇到了許多坑
創(chuàng)建一個(gè) Scala 的 WordCount 程序
第一步：IntelliJ IDEA下安裝 Scala 插件
安裝完 Scala 插件完成
第二步：Maven 下 Scala 下的項(xiàng)目創(chuàng)建
正常創(chuàng)建 Maven 項(xiàng)目（不會(huì)的看另一篇 Maven 配置）
第三步：Scala 版本的下載及配置
通過(guò)Spark官網(wǎng)下載頁(yè)面http://spark.apache.org/downloads.html 可知“Note: Starting version 2.0, Spark is built with Scala 2.11 by default.”，建議下載Spark2.2對(duì)應(yīng)的 Scala 2.11。
登錄Scala官網(wǎng)http://www.scala-lang.org/，單擊download按鈕，然后再“Other Releases”標(biāo)題下找到“下載2.11.0
根據(jù)自己的系統(tǒng)下載相應(yīng)的版本
接下來(lái)就是配置Scala 的環(huán)境變量（跟 jdk 的配置方法一樣）
輸入 Scala -version 查看是否配置成功 會(huì)顯示 Scala code runner version 2.11.0 – Copyright 2002-2013, LAMP/EPFL
選擇自己安裝 Scala 的路徑
第四步：編寫 Scala 程序
將其他的代碼刪除，不然在編輯的時(shí)候會(huì)報(bào)錯(cuò)
配置 pom.xml文件
在里面添加一個(gè) 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>${spark.version}</version>
</dependency>
具體的 pom.xml 內(nèi)容
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>cn.spark</groupId>
<artifactId>Spark</artifactId>
<version>1.0-SNAPSHOT</version>
<inceptionYear>2008</inceptionYear>
<properties>
<scala.version>2.11.0</scala.version>
<spark.version>2.2.1</spark.version>
</properties>
<pluginRepositories>
<pluginRepository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</pluginRepository>
</pluginRepositories>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${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>${scala.version}</scalaVersion>
</configuration>
</plugin>
</plugins>
</reporting>
</project>
編寫 WordCount 文件
package cn.spark
import org.apache.spark.{SparkConf, SparkContext}
/**
* Created by hubo on 2018/1/13
*/
object WordCount {
def main(args: Array[String]) {
var masterUrl = "local"
var inputPath = "/Users/huwenbo/Desktop/a.txt"
var outputPath = "/Users/huwenbo/Desktop/out"
if (args.length == 1) {
masterUrl = args(0)
} else if (args.length == 3) {
masterUrl = args(0)
inputPath = args(1)
outputPath = args(2)
}
println(s"masterUrl:$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代表自己本地運(yùn)行，在 hadoop 上運(yùn)行添加相應(yīng)地址
在配置中遇到的錯(cuò)誤，會(huì)寫在另一篇文章里。
以上就是本文的全部?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持服務(wù)器之家。
