Github user mridulm commented on a diff in the pull request: https://github.com/apache/spark/pull/16867#discussion_r104517384 --- Diff: core/src/main/scala/org/apache/spark/util/collection/MedianHeap.scala --- @@ -0,0 +1,94 @@ +/* + * 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.util.collection + +import scala.collection.mutable + +/** + * MedianHeap stores numbers and returns the median by O(1) time complexity. + * The basic idea is to maintain two heaps: a maxHeap and a minHeap. The maxHeap stores + * the smaller half of all numbers while the minHeap stores the larger half. The sizes + * of two heaps need to be balanced each time when a new number is inserted so that their + * sizes will not be different by more than 1. Therefore each time when findMedian() is + * called we check if two heaps have the same size. If they do, we should return the + * average of the two top values of heaps. Otherwise we return the top of the heap which + * has one more element. + */ + +private[spark] +class MedianHeap(implicit val ord: Ordering[Double]) { + + // Stores all the numbers less than the current median in a maxHeap, + // i.e median is the maximum, at the root + val maxHeap = mutable.PriorityQueue.empty[Double](implicitly[Ordering[Double]]) + + // Stores all the numbers greater than the current median in a minHeap, + // i.e median is the minimum, at the root + val minHeap = mutable.PriorityQueue.empty[Double](implicitly[Ordering[Double]].reverse) + + // Returns if there is no element in MedianHeap. + def isEmpty(): Boolean = { + maxHeap.isEmpty && minHeap.isEmpty + } + + // Size of MedianHeap. + def size(): Int = { + maxHeap.size + minHeap.size + } + + // Insert a new number into MedianHeap. + def insert(x: Double): Unit = { + // If both heaps are empty, we arbitrarily insert it into a heap, let's say, the minHeap. + if (isEmpty) { + minHeap.enqueue(x) + } else { + // If the number is larger than current median, it should be inserted into minHeap, + // otherwise maxHeap. + if (x > findMedian) { + minHeap.enqueue(x) + } else { + maxHeap.enqueue(x) + } + } + rebalance() + } + + // Re-balance the heaps. + private[this] def rebalance(): Unit = { + if (minHeap.size - maxHeap.size > 1) { + maxHeap.enqueue(minHeap.dequeue()) + } + if (maxHeap.size - minHeap.size > 1) { + minHeap.enqueue(maxHeap.dequeue) + } + } + + // Returns the median of the numbers. + def findMedian(): Double = { + if (isEmpty) { + throw new NoSuchElementException("MedianHeap is empty.") + } + if (minHeap.size == maxHeap.size) { + (minHeap.head + maxHeap.head) / 2.0 --- End diff -- Wont that not be very verbose for large number of tasks ? I have rarely had to debug spec exec - but when I had to, it was not easy !
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