[SPARK-1241] Add sliding to RDD

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

@@ -950,6 +950,22 @@ abstract class RDD[T: ClassTag](
    */
   def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = top(num)(ord.reverse)
 
+  /**
+   * Returns a RDD from grouping items of its parent RDD in fixed size blocks by passing a sliding
+   * window over them. The ordering is first based on the partition index and then the ordering of
+   * items within each partition. This is similar to sliding in Scala collections, except that it
+   * becomes an empty RDD if the window size is greater than the total number of items. It needs to
+   * trigger a Spark job if the parent RDD has more than one partitions and the window size is
+   * greater than 1.
+   */
+  def sliding(windowSize: Int): RDD[Seq[T]] = {
+    if (windowSize == 1) {
+      this.map(Seq(_))
+    } else {
+      new SlidingRDD[T](this, windowSize)
+    }
+  }
+
   /**
    * Save this RDD as a text file, using string representations of elements.
    */

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

@@ -0,0 +1,101 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.collection.mutable
+import scala.reflect.ClassTag
+
+import org.apache.spark.{TaskContext, Partition}
+
+private[spark]
+class SlidingRDDPartition[T](val idx: Int, val prev: Partition, val tail: Seq[T])
+  extends Partition with Serializable {
+  override val index: Int = idx
+}
+
+/**
+ * Represents a RDD from grouping items of its parent RDD in fixed size blocks by passing a sliding
+ * window over them. The ordering is first based on the partition index and then the ordering of
+ * items within each partition. This is similar to sliding in Scala collections, except that it
+ * becomes an empty RDD if the window size is greater than the total number of items. It needs to
+ * trigger a Spark job if the parent RDD has more than one partitions.
+ *
+ * @param parent the parent RDD
+ * @param windowSize the window size, must be greater than 1
+ *
+ * @see [[org.apache.spark.rdd.RDD#sliding]]
+ */
+private[spark]
+class SlidingRDD[T: ClassTag](@transient val parent: RDD[T], val windowSize: Int)
+  extends RDD[Seq[T]](parent) {
+
+  require(windowSize > 1, s"Window size must be greater than 1, but got $windowSize.")
+
+  override def compute(split: Partition, context: TaskContext): Iterator[Seq[T]] = {
+    val part = split.asInstanceOf[SlidingRDDPartition[T]]
+    (firstParent[T].iterator(part.prev, context) ++ part.tail)
+      .sliding(windowSize)
+      .withPartial(false)
+  }
+
+  override def getPreferredLocations(split: Partition): Seq[String] =
+    firstParent[T].preferredLocations(split.asInstanceOf[SlidingRDDPartition[T]].prev)
+
+  override def getPartitions: Array[Partition] = {
+    val parentPartitions = parent.partitions
+    val n = parentPartitions.size
+    if (n == 0) {
+      Array.empty
+    } else if (n == 1) {
+      Array(new SlidingRDDPartition[T](0, parentPartitions(0), Seq.empty))
+    } else {
+      val n1 = n - 1
+      val w1 = windowSize - 1
+      // Get the first w1 items of each partition, starting from the second partition.
+      val nextHeads =
+        parent.context.runJob(parent, (iter: Iterator[T]) => iter.take(w1).toArray, 1 until n, true)
+      val partitions = mutable.ArrayBuffer[SlidingRDDPartition[T]]()
+      var i = 0
+      var partitionIndex = 0
+      while (i < n1) {
+        var j = i
+        val tail = mutable.ListBuffer[T]()
+        // Keep appending to the current tail until appended a head of size w1.
+        while (j < n1 && nextHeads(j).size < w1) {
+          tail ++= nextHeads(j)
+          j += 1
+        }
+        if (j < n1) {
+          tail ++= nextHeads(j)
+          j += 1
+        }
+        partitions += new SlidingRDDPartition[T](partitionIndex, parentPartitions(i), tail)
+        partitionIndex += 1
+        // Skip appended heads.
+        i = j
+      }
+      // If the head of last partition has size w1, we also need to add this partition.
+      if (nextHeads.last.size == w1) {
+        partitions += new SlidingRDDPartition[T](partitionIndex, parentPartitions(n1), Seq.empty)
+      }
+      partitions.toArray
+    }
+  }
+
+  // TODO: Override methods such as aggregate, which only requires one Spark job.
+}

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

@@ -553,4 +553,18 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     val ids = ranked.map(_._1).distinct().collect()
     assert(ids.length === n)
   }
+
+  test("sliding") {
+    val data = 0 until 6
+    for (numPartitions <- 1 to 8) {
+      val rdd = sc.parallelize(data, numPartitions)
+      for (windowSize <- 1 to 6) {
+        val slided = rdd.sliding(windowSize).collect().map(_.toList).toList
+        val expected = data.sliding(windowSize).map(_.toList).toList
+        assert(slided === expected)
+      }
+      assert(rdd.sliding(7).collect().isEmpty,
+        "Should return an empty RDD if the window size is greater than the number of items.")
+    }
+  }
 }