SKIPME Streaming iterable

core/src/main/scala/org/apache/spark/rdd/RDD.scala

@@ -45,6 +45,8 @@ import org.apache.spark.util.{Utils, BoundedPriorityQueue, SerializableHyperLogL
 
 import org.apache.spark.SparkContext._
 import org.apache.spark._
+import scala.concurrent.duration.Duration
+import java.util.concurrent.TimeUnit
 
 /**
  * A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable,
@@ -621,6 +623,19 @@ abstract class RDD[T: ClassTag](
     filter(f.isDefinedAt).map(f)
   }
 
+  /**
+   * Return iterator that lazily fetches partitions
+   * @param prefetchPartitions How many partitions to prefetch. Larger value increases parallelism
+   *                           but also increases driver memory requirement.
+   * @param partitionBatchSize How many partitions fetch per job
+   * @param timeOut how long to wait for each partition fetch
+   * @return Iterable of every element in this RDD
+   */
+  def toIterator(prefetchPartitions: Int = 1, partitionBatchSize: Int = 10,
+                 timeOut: Duration = Duration(30, TimeUnit.SECONDS)):Iterator[T] = {
+    new RDDiterator[T](this, prefetchPartitions,partitionBatchSize, timeOut)
+  }
+
   /**
    * Return an RDD with the elements from `this` that are not in `other`.
    *

core/src/main/scala/org/apache/spark/rdd/RDDiterator.scala

@@ -0,0 +1,76 @@
+package org.apache.spark.rdd
+
+import scala.concurrent.{Await, Future}
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.duration.Duration
+import scala.annotation.tailrec
+import scala.collection.mutable
+import org.apache.spark.rdd.RDDiterator._
+import org.apache.spark.FutureAction
+
+/**
+ * Iterable whose iterator iterates over all elements of an RDD without fetching all partitions
+ * to the driver process
+ *
+ * @param rdd RDD to iterate
+ * @param prefetchPartitions    The number of partitions to prefetch.
+ *                              If <1 will not prefetch.
+ *                              partitions prefetched = min(prefetchPartitions, partitionBatchSize)
+ * @param partitionBatchSize    How many partitions to fetch per job
+ * @param timeOut How long to wait for each partition before failing.
+ */
+class RDDiterator[T: ClassManifest](rdd: RDD[T], prefetchPartitions: Int, partitionBatchSize: Int,
+                               timeOut: Duration)
+  extends Iterator[T] {
+
+  val batchSize = math.max(1,partitionBatchSize)
+  var partitionsBatches: Iterator[Seq[Int]] = Range(0, rdd.partitions.size).grouped(batchSize)
+  var pendingFetchesQueue = mutable.Queue.empty[Future[Seq[Seq[T]]]]
+  //add prefetchPartitions prefetch
+  0.until(math.max(0, prefetchPartitions / batchSize)).foreach(x=>enqueueDataFetch())
+
+  var currentIterator: Iterator[T] = Iterator.empty
+  @tailrec
+  final def hasNext = {
+    if (currentIterator.hasNext) {
+      //Still values in the current partition
+      true
+    } else {
+      //Move on to the next partition
+      //Queue new prefetch of a partition
+       enqueueDataFetch()
+      if (pendingFetchesQueue.isEmpty) {
+        //No more partitions
+        currentIterator = Iterator.empty
+        false
+      } else {
+        val future = pendingFetchesQueue.dequeue()
+        currentIterator = Await.result(future, timeOut).flatMap(x => x).iterator
+        //Next partition might be empty so check again.
+        this.hasNext
+      }
+    }
+  }
+  def next() = {
+    hasNext
+    currentIterator.next()
+  }
+
+  def enqueueDataFetch() ={
+    if (partitionsBatches.hasNext) {
+      pendingFetchesQueue.enqueue(fetchData(partitionsBatches.next(), rdd))
+    }
+  }
+}
+
+object RDDiterator {
+  private def fetchData[T: ClassManifest](partitionIndexes: Seq[Int],
+                                     rdd: RDD[T]): FutureAction[Seq[Seq[T]]] = {
+    val results = new ArrayBuffer[Seq[T]]()
+    rdd.context.submitJob[T, Array[T], Seq[Seq[T]]](rdd,
+      x => x.toArray,
+      partitionIndexes,
+      (inx: Int, res: Array[T]) => results.append(res),
+      results.toSeq)
+  }
+}

core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala

@@ -380,6 +380,36 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     for (i <- 0 until sample.size) assert(sample(i) === checkSample(i))
   }
 
+  test("toIterable") {
+    var nums = sc.makeRDD(Range(1, 1000), 100)
+    assert(nums.toIterator(prefetchPartitions = 10).size === 999)
+    assert(nums.toIterator().toArray === (1 to 999).toArray)
+
+    nums = sc.makeRDD(Range(1000, 1, -1), 100)
+    assert(nums.toIterator(prefetchPartitions = 10).size === 999)
+    assert(nums.toIterator(prefetchPartitions = 10).toArray === Range(1000, 1, -1).toArray)
+
+    nums = sc.makeRDD(Range(1, 100), 1000)
+    assert(nums.toIterator(prefetchPartitions = 10).size === 99)
+    assert(nums.toIterator(prefetchPartitions = 10).toArray === Range(1, 100).toArray)
+
+    nums = sc.makeRDD(Range(1, 1000), 100)
+    assert(nums.toIterator(prefetchPartitions = -1).size === 999)
+    assert(nums.toIterator().toArray === (1 to 999).toArray)
+
+    nums = sc.makeRDD(Range(1, 1000), 100)
+    assert(nums.toIterator(prefetchPartitions = 3,partitionBatchSize = 10).size === 999)
+    assert(nums.toIterator().toArray === (1 to 999).toArray)
+
+    nums = sc.makeRDD(Range(1, 1000), 100)
+    assert(nums.toIterator(prefetchPartitions = -1,partitionBatchSize = 0).size === 999)
+    assert(nums.toIterator().toArray === (1 to 999).toArray)
+
+    nums = sc.makeRDD(Range(1, 1000), 100)
+    assert(nums.toIterator(prefetchPartitions = -1).size === 999)
+    assert(nums.toIterator().toArray === (1 to 999).toArray)
+  }
+
   test("take") {
     var nums = sc.makeRDD(Range(1, 1000), 1)
     assert(nums.take(0).size === 0)