使用到的软件安装包: https://pan.baidu.com/s/1fOKsPYBmOUhHupT50_6yqQ 提取码: d473

1、安装Java

点击下一步，

点击下一步：

2、配置环境变量

环境变量设置：右键->我的电脑，选择属性
点击高级系统设置

点击环境变量：

点击新建：

变量名输入：JAVA_HOME
然后点击浏览目录，选择C盘下的C:\Program Files (x86)\Java\jdk1.8.0_301文件夹，点击打开即可

3、配置Hadoop环境

1、将hadoop-common-2.6.0-bin-master 拷贝到C盘目录下
2、将C:\ hadoop-common-2.6.0-bin-master\bin下的hadoop.dll文件和winutils.exe文件拷贝到C:\Windows\System32下，若有重复，不需要复制替换。
3、配置环境变量:

和配置Java的环境变量流程一样，配置Hadoop环境

重启电脑

4、安装Scala插件

选择Plugis

输入scala,点击install

重启IDEA

5、配置maven

输入Spark_Project

选择提供的maven安装包

选择apache-maven-3.6.1-bin\apache-maven-3.6.1\conf下的setting文件，并将Override打钩

点击pom文件，将提供的pom内容全部复制修改

<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>org.example</groupId><artifactId>untitled2</artifactId><version>1.0-SNAPSHOT</version><inceptionYear>2008</inceptionYear><properties><scala.version>2.11.8</scala.version></properties><repositories><repository><id>scala-tools.org</id><name>Scala-Tools Maven2 Repository</name><url>http://scala-tools.org/repo-releases</url></repository></repositories><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>${scala.version}</version></dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>3.8.1</version><scope>test</scope></dependency><dependency><groupId>org.specs</groupId><artifactId>specs</artifactId><version>1.2.5</version><scope>test</scope></dependency><!--    <dependency>--><!--      <groupId>org.apache.spark</groupId>--><!--      <artifactId>spark-core_2.11</artifactId>--><!--      <version>2.1.1</version>--><!--      <scope>provided</scope>--><!--    </dependency>--><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.11</artifactId><version>2.1.1</version><scope>provided</scope></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming_2.11</artifactId><version>2.1.1</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming-kafka-0-10_2.11</artifactId><version>2.1.1</version></dependency></dependencies><build><sourceDirectory>src/main/scala</sourceDirectory><testSourceDirectory>src/test/scala</testSourceDirectory><plugins><plugin><groupId>org.scala-tools</groupId><artifactId>maven-scala-plugin</artifactId><executions><execution><goals><goal>compile</goal><goal>testCompile</goal></goals></execution></executions><configuration><scalaVersion>${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>

至此，环境配置完成，开始Spark编程。

4、Spark编程

1、删除test文件夹

创建Scala文件

选择Object选项，输入HelloWorld类


Scala测试完成！

Spark测试

Spark测试代码：

ackage org.exampleimport org.apache.spark.{SparkConf, SparkContext}object HelloWorld {def main(args: Array[String]) {val logFile = "hdfs://hadoop000:8020/hello.txt"val conf = new SparkConf().setAppName("Simple Application").setMaster("local") //.setMaster("local")val sc = new SparkContext(conf)val rdd = sc.textFile(logFile)val wordcount = rdd.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _).map(x => (x._2, x._1)).sortByKey(false).map(x => (x._2, x._1))println(wordcount)wordcount.saveAsTextFile("hdfs://hadoop000:8020/output")sc.stop()}
}

发现报错，原因是虚拟内存未配置，

输入：-Xms1024m
运行成功！