基于IntelliJ IDEA开发Spark的Maven项目——Scala语言

基于IntelliJ IDEA开发Spark的Maven项目——Scala语言

1、Maven管理项目在JavaEE普遍使用，开发Spark项目也不例外，而Scala语言开发Spark项目的首选。因此需要构建Maven-Scala项目来开发Spark项目，本文采用的工具是IntelliJ IDEA 2016，IDEA工具越来越被大家认可，开发java, python ,scala 支持都非常好

下载链接 ： https://www.jetbrains.com/idea/download/

安装直接下一步即可

2、安装scala插件，File->Settings->Editor->Plugins,搜索scala即可安装

可能由于网络的原因下载不了，可以采取离线安装的方式，例如：

提示下载失败后，根据提示的地址下载离线安装包 http://plugins.jetbrains.com/files/631/24825/python-145.86.zip

在界面选择离线安装即可：

3、创建Maven工程，File->New Project->Maven

选择相应的JDK版本，直接下一步

设定Maven项目的GroupId及ArifactId

创建项目的工程名称，点击完成即可

创建Maven工程完毕，默认是Java的，没关系后面我们再添加scala与spark的依赖

4、修改Maven项目的pom.xml文件，增加scala与spark的依赖

<?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.ganymede</groupId>
    <artifactId>sparkplatformstudy</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <spark.version>1.6.0</spark.version>
        <scala.version>2.10</scala.version>
        <hadoop.version>2.6.0</hadoop.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.39</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
    </dependencies>

    <!-- maven官方 http://repo1.maven.org/maven2/  或 http://repo2.maven.org/maven2/ （延迟低一些） -->
    <repositories>
        <repository>
            <id>central</id>
            <name>Maven Repository Switchboard</name>
            <layout>default</layout>
            <url>http://repo2.maven.org/maven2</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
        </repository>
    </repositories>

    <build>
        <sourceDirectory>src/main/scala</sourceDirectory>
        <testSourceDirectory>src/test/scala</testSourceDirectory>

        <plugins>
            <plugin>
                <!-- MAVEN 编译使用的JDK版本 -->
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.3</version>
                <configuration>
                    <source>1.7</source>
                    <target>1.7</target>
                    <encoding>UTF-8</encoding>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

5、删除项目的java目录，新建scala并设置源文件夹

添加scala的SDK

添加scala的SDK成功

6、开发Spark实例

测试案例来自spark官网的mllib例子 http://spark.apache.org/docs/latest/mllib-data-types.html

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

/**
  * Created by wuke on 2016/7/5.
  */
object LoadLibSVMFile extends App{
  import org.apache.spark.mllib.regression.LabeledPoint
  import org.apache.spark.mllib.util.MLUtils
  import org.apache.spark.rdd.RDD

  val conf = new SparkConf().setAppName("LogisticRegressionMail").setMaster("local")

  val sc = new SparkContext(conf)
  val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

  println(examples.first)
}

测试通过

7、打包编译，线上发布

注意选择依赖包