Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/1110#discussion_r15508543
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala ---
@@ -44,6 +47,65 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) {
new SlidingRDD[T](self, windowSize)
}
}
+
+ /**
+ * Reduces the elements of this RDD in a tree pattern.
+ * @param depth suggested depth of the tree
+ * @see [[org.apache.spark.rdd.RDD#reduce]]
+ */
+ def treeReduce(f: (T, T) => T, depth: Int): T = {
+ require(depth >= 1, s"Depth must be greater than 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 local = 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(local).treeAggregate(Option.empty[T])(op, op,
depth)
+ .getOrElse(throw new UnsupportedOperationException("empty
collection"))
+ }
+
+ /**
+ * Aggregates the elements of this RDD in a tree pattern.
+ * @param depth suggested depth of the tree
+ * @see [[org.apache.spark.rdd.RDD#aggregate]]
+ */
+ def treeAggregate[U: ClassTag](zeroValue: U)(
+ seqOp: (U, T) => U,
+ combOp: (U, U) => U,
+ depth: Int): U = {
+ require(depth >= 1, s"Depth must be greater than 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 local = self.mapPartitions(it => Iterator(aggregatePartition(it)))
+ var numPartitions = local.partitions.size
+ 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) =>
+ iter.map((i % numPartitions, _))
+ }.reduceByKey(new HashPartitioner(numPartitions), cleanCombOp).values
--- End diff --
This is beyond your PR -- but @mateiz and I talked about adding some native
primitive to shuffle to improve specifically this pattern (basically there is
no need to create numPartitions streams within each map task).
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