1.序

我的工程是maven工程，通过maven不需要理会包的加载问题，很是方便。如果你还没有使用maven来管理工程的话那强烈建议你使用maven，尽管前期学习有点麻烦（主要是maven的默认下载镜像是国外）

2.搭建详情

下面是我建工程的截图

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3.测试wordcount程序

pom.xml

<?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>cn.pwsoft</groupId>
    <artifactId>SparkStudy</artifactId>
    <version>1.0-SNAPSHOT</version>

    <dependencies>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>3.8.1</version>
            <scope>test</scope>
        </dependency>

        <!-- https://mvnrepository.com/artifact/io.netty/netty-all -->
        <dependency>
            <groupId>io.netty</groupId>
            <artifactId>netty-all</artifactId>
            <version>4.1.4.Final</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>
        
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>


        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.6.4</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-hdfs -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.6.4</version>
        </dependency>

    </dependencies>


</project>

wordcount程序源码

object MyWordCount {
  def main(args: Array[String]) {

    //获取SparkContext
    val spark = SparkSession
      .builder
      .appName("Spark Pi").master("local")
      .getOrCreate()

    var sc = spark.sparkContext

    //读取文件，返回这个文件的行数
    val count = sc.textFile("F:\\vmware\\share\\soft\\spark-1.6.0-bin-hadoop2.6\\README.md").count()
    println(count)

//    val lines = spark.sparkContext.textFile("F:\\vmware\\share\\soft\\spark-1.6.0-bin-hadoop2.6\\README.md")
//    val wordcount = lines.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_+_).collect()
//    wordcount.foreach(pair => println(pair._1 + "  :  " + pair._2))

    /**
      * flatMap产生 MapPartitionsRDD
      * map 产生 MapPartitionsRDD
      * reduceByKey 产生 ShuffledRDD
      * sortByKey 产生 ShuffledRDD
      */
    spark.sparkContext.textFile("F:\\vmware\\share\\soft\\spark-1.6.0-bin-hadoop2.6\\README.md").flatMap(line => line.split(" "))
      .map(word => (word, 1)).reduceByKey(_+_).map(pair => (pair._2, pair._1)).sortByKey(false).collect()
      .map(pair => (pair._2, pair._1)).foreach(pair => println(pair._1 + "  :  " + pair._2))

    //为了可以通过web控制台看到信息，加一个写循环不让程序结束
    while (true) {}
    
    
    spark.stop()

  }
}

成功运行