IDEA編寫scala使用maven打包，出現的一些坑，編寫一個wordcount程序

被這個死問題整整折磨了兩個小時，首先我們來看一下如何建立一個使用maven的scala工程（雖然個人覺得sbt更好用）

新建項目後點擊右側scala選項，選擇左側面板的IDea建立好項目輸入項目名稱，建立好項目之後可以右鍵項目名稱（此時選擇Project模式 ） 會出來一個點擊Add Framework support... ,選擇maven之後，新建scala文件夾，右鍵scala文件夾選擇Mark directory as 中選擇source root,一個新的工程就創建好了，下面新建耳熟能詳的wordcount代碼

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf

object WordCount {
  def main(args: Array[String]) {
    val inputFile =  "file:///usr/local/spark/mycode/wordcount/word.txt"
    val conf = new SparkConf().setAppName("WordCount").setMaster("local")
    val sc = new SparkContext(conf)
    val textFile = sc.textFile(inputFile)
    val wordCount = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
    wordCount.foreach(println)
  }
}

 

貼上pom.xml代碼

<?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>groupId</groupId>
    <artifactId>Test</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <spark.version>2.2.0</spark.version>
        <scala.version>2.11</scala.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-streaming_${scala.version}</artifactId>
            <version>${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-mllib_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.0</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.6</version>
        </dependency>

    </dependencies>

    <build>
        <plugins>

            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.19</version>
                <configuration>
                    <skip>true</skip>
                </configuration>
            </plugin>

        </plugins>
    </build>


</project>

 

最好加上hadoop的依賴，否則可能會出問題