Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/16867#discussion_r104519160
--- 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 --
So what are you proposing here? I'm not convinced that the ability to map
the task length threshold for speculation to a particular task (especially in a
job with thousands of tasks) is useful enough to merit having a `median`
function that returns something that's not technically the median. I don't
doubt that the speculation code path is easy to debug -- but it seems like
there are better ways to improve the logging around that than to have an
incorrect median implementation.
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