1.SparkContext哪一端生成的？Driver端2.DAG是在哪一端被构建的？Driver端3.RDD是在哪一端生成的？Driver端4.广播变量是在哪一端调用的方法进行广播的？Driver端5.要广播的数据应该在哪一端先创建好再广播呢？ Driver端6.调用RDD的算子（Transformation和Action）是在哪一端调用的Driver端7.RDD在调用Transformation和Action时需要传入一个函数，函数是在哪一端声明和传入的?Driver端8.RDD在调用Transformation和Action时需要传入函数，请问传入的函数是在哪一端执行了函数的业务逻辑？Executor中的Task执行的9.自定义的分区器这个类是在哪一端实例化的？Driver端10.分区器中的getParitition方法在哪一端调用的呢？Executor中的Task中调用的11.Task是在哪一端生成的呢？ Driver端12.DAG是在哪一端构建好的并被切分成一到多个State的Driver端13.DAG是哪个类完成的切分Stage的功能？DAGScheduler14.DAGScheduler将切分好的Stage以什么样的形式给TaskSchedulerTaskSet

package cn.edu360.spark05// 随意定义一工具类
class MyUtil {def get(msg: String): String ={msg+"aaa"}
}

package cn.edu360.spark05
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDDobject SequenceTest {def main(args: Array[String]): Unit = {val conf: SparkConf = new SparkConf().setAppName("ScalaWordCount").setMaster("local[3]")var sc = new SparkContext(conf)val lines: RDD[String] = sc.textFile("hdfs://hd1:9000/wordcount/input/")val words = lines.flatMap(_.split(" "))// 对类进行实例化val util = new MyUtil// 调用实例的方法val value: RDD[String] = words.map(word => util.get(word))value.collect()sc.stop()}
}

package cn.edu360.spark05// 继承Serializable
class MyUtil extends Serializable {def get(msg: String): String ={msg+"aaa"}
}

package cn.edu360.spark05
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDDobject SequenceTest {def main(args: Array[String]): Unit = {val conf: SparkConf = new SparkConf().setAppName("ScalaWordCount").setMaster("local[3]")var sc = new SparkContext(conf)val lines: RDD[String] = sc.textFile("hdfs://hd1:9000/wordcount/input/")val words = lines.flatMap(_.split(" "))val value: RDD[String] = words.map(word => {// 在这里进行实例化，这里的操作是在Executer中val util = new MyUtilutil.get(word)})val result: Array[String] = value.collect()print(result.toBuffer)sc.stop()}
}

package cn.edu360.spark05// 将class 改为 object的单例模式
object MyUtil {def get(msg: String): String ={msg+"aaa"}
}

package cn.edu360.spark05
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDDobject SequenceTest {def main(args: Array[String]): Unit = {val conf: SparkConf = new SparkConf().setAppName("ScalaWordCount").setMaster("local[3]")var sc = new SparkContext(conf)val lines: RDD[String] = sc.textFile("hdfs://hd1:9000/wordcount/input/")val words = lines.flatMap(_.split(" "))val value: RDD[String] = words.map(word => {// 调用方法
            MyUtil.get(word)})val result: Array[String] = value.collect()print(result.toBuffer)sc.stop()}
}