准备数据

test.txt

111
sss
ddd
as
zjj
assssd
zjj
aaa
sssds
aaa

创建Maven项目

https://blog.csdn.net/qq_41489540/article/details/109430098

依赖xml

   <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.1.1</version>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <!-- 打包插件, 否则 scala 类不会编译并打包进去 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.4.6</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

编写wordcount

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object WordCount {
  def main(args: Array[String]): Unit = {
    // 指定要分析的文件
    val  filePath ="D:\\Downloads\\test.txt"
    // 1. 创建一个SparkContext  打包的时候, 把master的设置去掉, 在提交的时候使用 --maser 来设置master
    //这样运行只能是local模式,
    val conf: SparkConf = new SparkConf().setMaster("local[2]") .setAppName("WordCount")
    val sc: SparkContext = new SparkContext(conf)
    // 2. 从数据源得到一个RDD
    val lineRDD: RDD[String] = sc.textFile(filePath)
    // 3. 对RDD做各种转换
    val resultRDD: RDD[(String, Int)] = lineRDD
      .flatMap(_.split("\\W"))
      .map((_, 1))
      .reduceByKey(_ + _)
    // 4. 执行一个行动算子   (collect: 把各个节点计算后的数据, 拉取到驱动端)
    val wordCountArr = resultRDD.collect()
    wordCountArr.foreach(println)
    // 5. 关闭SparkContext
    sc.stop()
  }
}

运行

(ddd,1)
(zjj,2)
(as,1)
(sssds,1)
(assssd,1)
(sss,1)
(111,1)
(aaa,2)

码云代码地址

https://gitee.com/crow1/ZJJ_SparkCore

找demo01