关注我的微信公众号：pythonislover,领取python,大数据，SQL优化相关视频资料！~
Python大数据与SQL优化笔 QQ群：771686295
take
def take(num: Int): Array[T]
take用于获取RDD中从0到num-1下标的元素，不排序。
scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at :21
scala> rdd1.take(1)
res0: Array[Int] = Array(10)
scala> rdd1.take(2)
res1: Array[Int] = Array(10, 4)
top
def top(num: Int)(implicit ord: Ordering[T]): Array[T]
top函数用于从RDD中，按照默认（降序）或者指定的排序规则，返回前num个元素。
scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at :21
scala> rdd1.top(1)
res2: Array[Int] = Array(12)
scala> rdd1.top(2)
res3: Array[Int] = Array(12, 10)
//指定排序规则
scala> implicit val myOrd = implicitly[Ordering[Int]].reverse
myOrd:scala.math.Ordering[Int]=scala.math.Ordering$$anon$4@767499ef
scala> rdd1.top(1)
res4: Array[Int] = Array(2)
scala> rdd1.top(2)
res5: Array[Int] = Array(2, 3)
takeOrdered
def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T]
takeOrdered和top类似，只不过以和top相反的顺序返回元素。
scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at :21
scala> rdd1.top(1)
res4: Array[Int] = Array(2)
scala> rdd1.top(2)
res5: Array[Int] = Array(2, 3)
scala> rdd1.takeOrdered(1)
res6: Array[Int] = Array(12)
scala> rdd1.takeOrdered(2)
res7: Array[Int] = Array(12, 10)