move treeReduce and treeAggregate to core

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

@@ -900,6 +900,38 @@ abstract class RDD[T: ClassTag](
     jobResult.getOrElse(throw new UnsupportedOperationException("empty collection"))
   }
 
+  /**
+   * Reduces the elements of this RDD in a multi-level tree pattern.
+   *
+   * @param depth suggested depth of the tree (default: 2)
+   * @see [[org.apache.spark.rdd.RDD#reduce]]
+   */
+  def treeReduce(f: (T, T) => T, depth: Int = 2): T = {
+    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
+    val cleanF = context.clean(f)
+    val reducePartition: Iterator[T] => Option[T] = iter => {
+      if (iter.hasNext) {
+        Some(iter.reduceLeft(cleanF))
+      } else {
+        None
+      }
+    }
+    val partiallyReduced = mapPartitions(it => Iterator(reducePartition(it)))
+    val op: (Option[T], Option[T]) => Option[T] = (c, x) => {
+      if (c.isDefined && x.isDefined) {
+        Some(cleanF(c.get, x.get))
+      } else if (c.isDefined) {
+        c
+      } else if (x.isDefined) {
+        x
+      } else {
+        None
+      }
+    }
+    partiallyReduced.treeAggregate(Option.empty[T])(op, op, depth)
+      .getOrElse(throw new UnsupportedOperationException("empty collection"))
+  }
+
   /**
    * Aggregate the elements of each partition, and then the results for all the partitions, using a
    * given associative function and a neutral "zero value". The function op(t1, t2) is allowed to
@@ -935,6 +967,37 @@ abstract class RDD[T: ClassTag](
     jobResult
   }
 
+  /**
+   * Aggregates the elements of this RDD in a multi-level tree pattern.
+   *
+   * @param depth suggested depth of the tree (default: 2)
+   * @see [[org.apache.spark.rdd.RDD#aggregate]]
+   */
+  def treeAggregate[U: ClassTag](zeroValue: U)(
+      seqOp: (U, T) => U,
+      combOp: (U, U) => U,
+      depth: Int = 2): U = {
+    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
+    if (partitions.size == 0) {
+      return Utils.clone(zeroValue, context.env.closureSerializer.newInstance())
+    }
+    val cleanSeqOp = context.clean(seqOp)
+    val cleanCombOp = context.clean(combOp)
+    val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
+    var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it)))
+    var numPartitions = partiallyAggregated.partitions.size
+    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
+    // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation.
+    while (numPartitions > scale + numPartitions / scale) {
+      numPartitions /= scale
+      val curNumPartitions = numPartitions
+      partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) =>
+        iter.map((i % curNumPartitions, _))
+      }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values
+    }
+    partiallyAggregated.reduce(cleanCombOp)
+  }
+
   /**
    * Return the number of elements in the RDD.
    */

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

@@ -157,6 +157,24 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(result.toSet === Set(("a", 6), ("b", 2), ("c", 5)))
   }
 
+  test("treeAggregate") {
+    val rdd = sc.makeRDD(-1000 until 1000, 10)
+    def seqOp = (c: Long, x: Int) => c + x
+    def combOp = (c1: Long, c2: Long) => c1 + c2
+    for (depth <- 1 until 10) {
+      val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth)
+      assert(sum === -1000L)
+    }
+  }
+
+  test("treeReduce") {
+    val rdd = sc.makeRDD(-1000 until 1000, 10)
+    for (depth <- 1 until 10) {
+      val sum = rdd.treeReduce(_ + _, depth)
+      assert(sum === -1000)
+    }
+  }
+
   test("basic caching") {
     val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache()
     assert(rdd.collect().toList === List(1, 2, 3, 4))
@@ -967,4 +985,5 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assertFails { sc.parallelize(1 to 100) }
     assertFails { sc.textFile("/nonexistent-path") }
   }
+
 }

mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala

@@ -22,7 +22,6 @@ import breeze.linalg.{DenseVector => BDV}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd.RDD
 
 /**

mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala

@@ -20,7 +20,6 @@ package org.apache.spark.mllib.feature
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 

mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala

@@ -30,7 +30,6 @@ import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg._
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom

mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala

@@ -25,7 +25,6 @@ import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 
 /**
  * Class used to solve an optimization problem using Gradient Descent.

mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala

@@ -26,7 +26,6 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS.axpy
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd.RDD
 
 /**

mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala

@@ -21,10 +21,7 @@ import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.HashPartitioner
-import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
-import org.apache.spark.util.Utils
 
 /**
  * Machine learning specific RDD functions.
@@ -53,69 +50,31 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) extends Serializable {
    * Reduces the elements of this RDD in a multi-level tree pattern.
    *
    * @param depth suggested depth of the tree (default: 2)
-   * @see [[org.apache.spark.rdd.RDD#reduce]]
+   * @see [[org.apache.spark.rdd.RDD#treeReduce]]
+   * @deprecated Use [[org.apache.spark.rdd.RDD#treeReduce]] instead.
    */
-  def treeReduce(f: (T, T) => T, depth: Int = 2): T = {
-    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
-    val cleanF = self.context.clean(f)
-    val reducePartition: Iterator[T] => Option[T] = iter => {
-      if (iter.hasNext) {
-        Some(iter.reduceLeft(cleanF))
-      } else {
-        None
-      }
-    }
-    val partiallyReduced = self.mapPartitions(it => Iterator(reducePartition(it)))
-    val op: (Option[T], Option[T]) => Option[T] = (c, x) => {
-      if (c.isDefined && x.isDefined) {
-        Some(cleanF(c.get, x.get))
-      } else if (c.isDefined) {
-        c
-      } else if (x.isDefined) {
-        x
-      } else {
-        None
-      }
-    }
-    RDDFunctions.fromRDD(partiallyReduced).treeAggregate(Option.empty[T])(op, op, depth)
-      .getOrElse(throw new UnsupportedOperationException("empty collection"))
-  }
+  @deprecated("Use RDD.treeReduce instead.", "1.3.0")
+  def treeReduce(f: (T, T) => T, depth: Int = 2): T = self.treeReduce(f, depth)
 
   /**
    * Aggregates the elements of this RDD in a multi-level tree pattern.
    *
    * @param depth suggested depth of the tree (default: 2)
-   * @see [[org.apache.spark.rdd.RDD#aggregate]]
+   * @see [[org.apache.spark.rdd.RDD#treeAggregate]]
+   * @deprecated Use [[org.apache.spark.rdd.RDD#treeAggregate]] instead.
    */
+  @deprecated("Use RDD.treeAggregate instead.", "1.3.0")
   def treeAggregate[U: ClassTag](zeroValue: U)(
       seqOp: (U, T) => U,
       combOp: (U, U) => U,
       depth: Int = 2): U = {
-    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
-    if (self.partitions.size == 0) {
-      return Utils.clone(zeroValue, self.context.env.closureSerializer.newInstance())
-    }
-    val cleanSeqOp = self.context.clean(seqOp)
-    val cleanCombOp = self.context.clean(combOp)
-    val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
-    var partiallyAggregated = self.mapPartitions(it => Iterator(aggregatePartition(it)))
-    var numPartitions = partiallyAggregated.partitions.size
-    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
-    // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation.
-    while (numPartitions > scale + numPartitions / scale) {
-      numPartitions /= scale
-      val curNumPartitions = numPartitions
-      partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) =>
-        iter.map((i % curNumPartitions, _))
-      }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values
-    }
-    partiallyAggregated.reduce(cleanCombOp)
+    self.treeAggregate(zeroValue)(seqOp, combOp, depth)
   }
 }
 
 @DeveloperApi
 object RDDFunctions {
 
   /** Implicit conversion from an RDD to RDDFunctions. */
-  implicit def fromRDD[T: ClassTag](rdd: RDD[T]) = new RDDFunctions[T](rdd)
+  implicit def fromRDD[T: ClassTag](rdd: RDD[T]): RDDFunctions[T] = new RDDFunctions[T](rdd)
 }

mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala

@@ -22,7 +22,6 @@ import org.scalatest.FunSuite
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, MultivariateOnlineSummarizer}
 import org.apache.spark.rdd.RDD
 

mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala

@@ -46,22 +46,4 @@ class RDDFunctionsSuite extends FunSuite with MLlibTestSparkContext {
     val expected = data.flatMap(x => x).sliding(3).toSeq.map(_.toSeq)
     assert(sliding === expected)
   }
-
-  test("treeAggregate") {
-    val rdd = sc.makeRDD(-1000 until 1000, 10)
-    def seqOp = (c: Long, x: Int) => c + x
-    def combOp = (c1: Long, c2: Long) => c1 + c2
-    for (depth <- 1 until 10) {
-      val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth)
-      assert(sum === -1000L)
-    }
-  }
-
-  test("treeReduce") {
-    val rdd = sc.makeRDD(-1000 until 1000, 10)
-    for (depth <- 1 until 10) {
-      val sum = rdd.treeReduce(_ + _, depth)
-      assert(sum === -1000)
-    }
-  }
 }