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
[java] view plain copy print ? <?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> <?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
[java] view plain copy print ? 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) } 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
