Spark 2.0从入门到精通245讲——操作RDD（transformation案例实战）

package cn.spark.study.core

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

/**
* @author Administrator
*/
object TransformationOperation {

def main(args: Array[String]) {
// map()
// filter()
// flatMap()
// groupByKey()
// reduceByKey()
// sortByKey()
join()
}

def map() {
val conf = new SparkConf()
.setAppName("map")
.setMaster("local")
val sc = new SparkContext(conf)

val numbers = Array(1, 2, 3, 4, 5)
val numberRDD = sc.parallelize(numbers, 1)
val multipleNumberRDD = numberRDD.map { num => num * 2 }

multipleNumberRDD.foreach { num => println(num) }
}

def filter() {
val conf = new SparkConf()
.setAppName("filter")
.setMaster("local")
val sc = new SparkContext(conf)

val numbers = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
val numberRDD = sc.parallelize(numbers, 1)
val evenNumberRDD = numberRDD.filter { num => num % 2 == 0 }

evenNumberRDD.foreach { num => println(num) }
}

def flatMap() {
val conf = new SparkConf()
.setAppName("flatMap")
.setMaster("local")
val sc = new SparkContext(conf)

val lineArray = Array("hello you", "hello me", "hello world")
val lines = sc.parallelize(lineArray, 1)
val words = lines.flatMap { line => line.split(" ") }

words.foreach { word => println(word) }
}

def groupByKey() {
val conf = new SparkConf()
.setAppName("groupByKey")
.setMaster("local")
val sc = new SparkContext(conf)

val scoreList = Array(Tuple2("class1", 80), Tuple2("class2", 75),
Tuple2("class1", 90), Tuple2("class2", 60))
val scores = sc.parallelize(scoreList, 1)
val groupedScores = scores.groupByKey()

groupedScores.foreach(score => {
println(score._1);
score._2.foreach { singleScore => println(singleScore) };
println("=============================")
})
}

def reduceByKey() {
val conf = new SparkConf()
.setAppName("groupByKey")
.setMaster("local")
val sc = new SparkContext(conf)

val scoreList = Array(Tuple2("class1", 80), Tuple2("class2", 75),
Tuple2("class1", 90), Tuple2("class2", 60))
val scores = sc.parallelize(scoreList, 1)
val totalScores = scores.reduceByKey(_ + _)

totalScores.foreach(classScore => println(classScore._1 + ": " + classScore._2))
}

def sortByKey() {
val conf = new SparkConf()
.setAppName("sortByKey")
.setMaster("local")
val sc = new SparkContext(conf)

val scoreList = Array(Tuple2(65, "leo"), Tuple2(50, "tom"),
Tuple2(100, "marry"), Tuple2(85, "jack"))
val scores = sc.parallelize(scoreList, 1)
val sortedScores = scores.sortByKey(false)

sortedScores.foreach(studentScore => println(studentScore._1 + ": " + studentScore._2))
}

def join() {
val conf = new SparkConf()
.setAppName("join")
.setMaster("local")
val sc = new SparkContext(conf)

val studentList = Array(
Tuple2(1, "leo"),
Tuple2(2, "jack"),
Tuple2(3, "tom"));

val scoreList = Array(
Tuple2(1, 100),
Tuple2(2, 90),
Tuple2(3, 60));

val students = sc.parallelize(studentList);
val scores = sc.parallelize(scoreList);

val studentScores = students.join(scores)

studentScores.foreach(studentScore => {
println("student id: " + studentScore._1);
println("student name: " + studentScore._2._1)
println("student socre: " + studentScore._2._2)
println("=======================================")
})
}

def cogroup() {

}

}