add docs

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

@@ -827,8 +827,15 @@ abstract class RDD[T: ClassTag](
     jobResult.getOrElse(throw new UnsupportedOperationException("empty collection"))
   }
 
-  def treeReduce(f: (T, T) => T, level: Int): T = {
-    require(level >= 1, s"Level must be greater than 1 but got $level.")
+  /**
+   * :: DeveloperApi ::
+   * Reduces the elements of this RDD in a tree pattern.
+   * @param depth suggested depth of the tree
+   * @see [[org.apache.spark.rdd.RDD#reduce]]
+   */
+  @DeveloperApi
+  def treeReduce(f: (T, T) => T, depth: Int): T = {
+    require(depth >= 1, s"Depth must be greater than 1 but got $depth.")
     val cleanF = sc.clean(f)
     val reducePartition: Iterator[T] => Option[T] = iter => {
       if (iter.hasNext) {
@@ -849,7 +856,7 @@ abstract class RDD[T: ClassTag](
         None
       }
     }
-    local.treeAggregate(Option.empty[T])(op, op, level)
+    local.treeAggregate(Option.empty[T])(op, op, depth)
       .getOrElse(throw new UnsupportedOperationException("empty collection"))
   }
 
@@ -888,12 +895,18 @@ abstract class RDD[T: ClassTag](
     jobResult
   }
 
+  /**
+   * :: DeveloperApi ::
+   * Aggregates the elements of this RDD in a tree pattern.
+   * @param depth suggested depth of the tree
+   * @see [[org.apache.spark.rdd.RDD#aggregate]]
+   */
   @DeveloperApi
   def treeAggregate[U: ClassTag](zeroValue: U)(
       seqOp: (U, T) => U,
       combOp: (U, U) => U,
-      level: Int): U = {
-    require(level >= 1, s"Level must be greater than 1 but got $level.")
+      depth: Int): U = {
+    require(depth >= 1, s"Depth must be greater than 1 but got $depth.")
     if (this.partitions.size == 0) {
       return Utils.clone(zeroValue, sc.env.closureSerializer.newInstance())
     }
@@ -902,7 +915,7 @@ abstract class RDD[T: ClassTag](
     val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
     var local = this.mapPartitions(it => Iterator(aggregatePartition(it)))
     var numPartitions = local.partitions.size
-    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / level)).toInt, 2)
+    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
     while (numPartitions > scale + numPartitions / scale) {
       numPartitions /= scale
       local = local.mapPartitionsWithIndex { (i, iter) =>