Spark- Transformation实战
RDD的算子分为两类,是 Trans formation(Lazy),一类是 Action(触发任务执行
RDD不存在真正要计算的数据,而是记录了RDD的转换关系(调用了什么方法,传入什么函数)
RDD的 Trans formation的特点
1. lazy
2.生成新的RDD
package cn.rzlee.spark.core import org.apache.spark.rdd.RDD import org.apache.spark.{SparkConf, SparkContext} object TransformationOperation { def main(args: Array[String]): Unit = { //map() //filter() //flatMap() // groupByKey() //reduceByKey() //sortByKey() join() } // 将集合中每个元素乘以2 def map(){ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val numbers = Array(1,2,3,4,5) val numberRDD: RDD[Int] = sc.parallelize(numbers,1) numberRDD.foreach(num=>println(num)) } // 过滤出集合中的偶数 def filter(): Unit ={ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val numbers = Array(1,2,3,4,5) val numberRDD: RDD[Int] = sc.parallelize(numbers,1) val evenNumbersRdd = numberRDD.filter(num=>num%2==0) evenNumbersRdd.foreach(num=>println(num)) } // 将行拆分为单词 def flatMap(): Unit ={ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val lineArray = Array("hello you", "just do it", "go go go") val lines = sc.parallelize(lineArray, 1) val words: RDD[String] = lines.flatMap(line=>line.split(" ")) words.foreach(word=>println(word)) } // 将每个班级的成绩进行分组 def groupByKey(): Unit ={ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val scoresList = Array(Tuple2("class1", 50), Tuple2("class1", 95), Tuple2("class2", 60), Tuple2("class2", 88)) val scores: RDD[(String, Int)] = sc.parallelize(scoresList, 1) val groupedScoreds = scores.groupByKey() groupedScoreds.foreach(scored=>{ println(scored._1) scored._2.foreach(singleScore=>println(singleScore)) println("=====================================") }) } // 统计每个班级的总分 def reduceByKey(): Unit ={ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val scoresList = Array(Tuple2("class1", 50), Tuple2("class1", 95), Tuple2("class2", 60), Tuple2("class2", 88)) val scores: RDD[(String, Int)] = sc.parallelize(scoresList, 1) val totalScores: RDD[(String, Int)] = scores.reduceByKey(_+_) totalScores.foreach(totalScore=>println(totalScore._1 +" : " + totalScore._2)) } //将学生分数进行排序 def sortByKey(): Unit ={ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val scoreList = Array(Tuple2(90,"leo"), Tuple2(99, "kent"), Tuple2(80,"Jeo"), Tuple2(91,"Ben"), Tuple2(96,"Sam")) val scores: RDD[( Int,String)] = sc.parallelize(scoreList, 1) val sortedScores = scores.sortByKey(false) sortedScores.foreach(student=>println(student._2 +" : " + student._1)) } // 打印每个学生的成绩 def join(): Unit ={ val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]") val sc = new SparkContext(conf) val studentsList = Array(Tuple2(1,"leo"), Tuple2(2, "Sam"), Tuple2(3, "kevin")) val scoresList = Array(Tuple2(1,60), Tuple2(2,70), Tuple2(3,80)) val students: RDD[(Int, String)] = sc.parallelize(studentsList,1) val scores: RDD[(Int, Int)] = sc.parallelize(scoresList,1) val studentScores: RDD[(Int, (String, Int))] = students.join(scores) studentScores.foreach(studentScore=>{ println("studentid: "+studentScore._1) println("studentNmae:"+studentScore._2._1) println("studentScore: "+ studentScore._2._2) println("###################################################") }) }
// 打印每个学生的成绩
// cogroup相当于full join
def cogroup(): Unit ={
val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[1]")
val sc = new SparkContext(conf)
val studentsList = Array(Tuple2(1,"leo"), Tuple2(2, "Sam"), Tuple2(3, "kevin"))
val scoresList = Array(Tuple2(1,60), Tuple2(2,70), Tuple2(3,80))
val students: RDD[(Int, String)] = sc.parallelize(studentsList,1)
val scores: RDD[(Int, Int)] = sc.parallelize(scoresList,1)
val studentScores: RDD[(Int, (Iterable[String], Iterable[Int]))] = students.cogroup(scores)
studentScores.foreach(studentScore =>{
println("studentid: " + studentScore._1)
println("studentname: "+ studentScore._2._1)
println("studentscore: "+ studentScore._2._2)
})
}
#union求并集,注意类型要一致
val rdd6 = sc.parallelize(List(5,6,4,7)) val rdd7 = sc.parallelize(List(1,2,3,4)) val rdd8 = rdd6.union(rdd7) rdd8.distinct.sortBy(x=>x).collect
#intersection求交集
val rdd9 = rdd6.intersection(rdd7)
#join(连接) 注意按照key相join
val rdd1 = sc.parallelize(List(("tom", 1), ("jerry", 2), ("kitty", 3))) val rdd2 = sc.parallelize(List(("jerry", 9), ("tom", 8), ("shuke", 7), ("tom", 2))) val rdd3 = rdd1.join(rdd2) val rdd3 = rdd1.leftOuterJoin(rdd2) val rdd3 = rdd1.rightOuterJoin(rdd2)
#cogroup 有点像全外连接
// cogroup
val rdd1 = sc.parallelize(List(("tom", 1), ("tom", 2), ("jerry", 3), ("kitty", 2)))
val rdd2 = sc.parallelize(List(("jerry", 2), ("tom", 1), ("shuke", 2)))
val rdd3 = rdd1.cogroup(rdd2)
println(rdd3.collect().toBuffer)
#cartesian笛卡尔积
val rdd1 = sc.parallelize(List("tom", "jerry")) val rdd2 = sc.parallelize(List("tom", "kitty", "shuke")) val rdd3 = rdd1.cartesian(rdd2)