keyvalue对RDD s

scala> val input =sc.textFile("/home/simon/SparkWorkspace/test.txt")
input: org.apache.spark.rdd.RDD[String] = /home/simon/SparkWorkspace/test.txt MapPartitionsRDD[32] at textFile at <console>:24

scala> input.foreach(println)
hello simon!
hello world!
hello gg

scala> val rdds=input.map(line=>(line.split(" ")(0),line))
rdds: org.apache.spark.rdd.RDD[(String, String)] = MapPartitionsRDD[33] at map at <console>:25

scala> rdds.foreach(println)
(hello,hello simon!)
(hello,hello world!)
(hello,hello gg)

scala>

scala> val rdd=sc.parallelize(Array((1,2),(2,3),(3,4),(3,5),(4,6),(2,4)))
rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[34] at parallelize at <console>:24

scala> rdd.foreach(println)
(3,5)
(2,3)
(3,4)
(1,2)
(4,6)
(2,4)

scala> val rdd1=rdd.reduceByKey((x,y)=>x+y)
rdd1: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[35] at reduceByKey at <console>:25

scala> rdd1.foreach(println)
(1,2)
(4,6)
(2,7)
(3,9)

scala> val rdd2=rdd.keys
rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[36] at keys at <console>:25

scala> rdd2.foreach(println)
1
4
2
3
2
3

scala> val rdd3=rdd.values
rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[37] at values at <console>:25

scala> rdd3.foreach(println)
2
6
4
5
3
4

scala> val rdd4=rdd.groupByKey()
rdd4: org.apache.spark.rdd.RDD[(Int, Iterable[Int])] = ShuffledRDD[38] at groupByKey at <console>:25

scala> rdd4.foreach(println)
(3,CompactBuffer(4, 5))
(4,CompactBuffer(6))
(1,CompactBuffer(2))
(2,CompactBuffer(3, 4))

scala> val rdd5=rdd.sortByKey()
rdd5: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[41] at sortByKey at <console>:25

scala> rdd5.foreach(println)
(3,4)
(3,5)
(4,6)
(1,2)
(2,3)
(2,4)

scala> val rdd6=rdd4.sortByKey()
rdd6: org.apache.spark.rdd.RDD[(Int, Iterable[Int])] = ShuffledRDD[44] at sortByKey at <console>:25

scala> rdd6.foreach(println)
(1,CompactBuffer(2))
(4,CompactBuffer(6))
(3,CompactBuffer(4, 5))
(2,CompactBuffer(3, 4))

scala>

val scores=sc.parallelize(Array(("jack",89),("jack",90),("jack",99),("Tom",89),("Tom",95),("Tom",99)))
scores.foreach(println)
val scores2=scores.combineByKey(score=>(1,score),(c1:(Int,Double),newScore)=>(c1._1+1,c1._2+newScore),(c1:(Int,Double),c2:(Int,Double)=>(c1._1+c2._1,c1._2+c2._2))
scores2.foreach(println)
val average =scores2.map{case(name,(num,score))=>(name,score/num)}
average.foreach(println)