add a new top K method to RDD using a bounded priority queue
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@ -35,6 +35,7 @@ import spark.rdd.ZippedPartitionsRDD2
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import spark.rdd.ZippedPartitionsRDD3
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import spark.rdd.ZippedPartitionsRDD3
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import spark.rdd.ZippedPartitionsRDD4
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import spark.rdd.ZippedPartitionsRDD4
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import spark.storage.StorageLevel
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import spark.storage.StorageLevel
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import spark.util.BoundedPriorityQueue
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import SparkContext._
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import SparkContext._
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@ -722,6 +723,29 @@ abstract class RDD[T: ClassManifest](
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case _ => throw new UnsupportedOperationException("empty collection")
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case _ => throw new UnsupportedOperationException("empty collection")
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}
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}
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/**
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* Returns the top K elements from this RDD as defined by
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* the specified implicit Ordering[T].
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* @param num the number of top elements to return
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* @param ord the implicit ordering for T
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* @return an array of top elements
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*/
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def top(num: Int)(implicit ord: Ordering[T]): Array[T] = {
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val topK = mapPartitions { items =>
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val queue = new BoundedPriorityQueue[T](num)
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queue ++= items
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Iterator(queue)
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}.reduce { (queue1, queue2) =>
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queue1 ++= queue2
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queue1
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}
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val builder = Array.newBuilder[T]
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builder.sizeHint(topK.size)
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builder ++= topK
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builder.result()
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}
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/**
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/**
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* Save this RDD as a text file, using string representations of elements.
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* Save this RDD as a text file, using string representations of elements.
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*/
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*/
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48
core/src/main/scala/spark/util/BoundedPriorityQueue.scala
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48
core/src/main/scala/spark/util/BoundedPriorityQueue.scala
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@ -0,0 +1,48 @@
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package spark.util
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import java.util.{PriorityQueue => JPriorityQueue}
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import scala.collection.generic.Growable
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/**
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* Bounded priority queue. This class modifies the original PriorityQueue's
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* add/offer methods such that only the top K elements are retained. The top
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* K elements are defined by an implicit Ordering[A].
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*/
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class BoundedPriorityQueue[A](maxSize: Int)(implicit ord: Ordering[A], mf: ClassManifest[A])
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extends JPriorityQueue[A](maxSize, ord) with Growable[A] {
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override def offer(a: A): Boolean = {
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if (size < maxSize) super.offer(a)
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else maybeReplaceLowest(a)
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}
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override def add(a: A): Boolean = offer(a)
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override def ++=(xs: TraversableOnce[A]): this.type = {
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xs.foreach(add)
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this
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}
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override def +=(elem: A): this.type = {
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add(elem)
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this
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}
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override def +=(elem1: A, elem2: A, elems: A*): this.type = {
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this += elem1 += elem2 ++= elems
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}
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private def maybeReplaceLowest(a: A): Boolean = {
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val head = peek()
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if (head != null && ord.gt(a, head)) {
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poll()
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super.offer(a)
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} else false
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}
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}
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object BoundedPriorityQueue {
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import scala.collection.JavaConverters._
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implicit def asIterable[A](queue: BoundedPriorityQueue[A]): Iterable[A] = queue.asScala
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}
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@ -317,4 +317,23 @@ class RDDSuite extends FunSuite with LocalSparkContext {
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assert(sample.size === checkSample.size)
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assert(sample.size === checkSample.size)
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for (i <- 0 until sample.size) assert(sample(i) === checkSample(i))
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for (i <- 0 until sample.size) assert(sample(i) === checkSample(i))
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}
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}
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test("top with predefined ordering") {
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sc = new SparkContext("local", "test")
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val nums = Array.range(1, 100000)
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val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2)
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val topK = ints.top(5)
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assert(topK.size === 5)
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assert(topK.sorted === nums.sorted.takeRight(5))
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}
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test("top with custom ordering") {
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sc = new SparkContext("local", "test")
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val words = Vector("a", "b", "c", "d")
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implicit val ord = implicitly[Ordering[String]].reverse
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val rdd = sc.makeRDD(words, 2)
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val topK = rdd.top(2)
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assert(topK.size === 2)
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assert(topK.sorted === Array("b", "a"))
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}
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}
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}
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