Bug in Microsoft's internal PriorityQueue<T>?
The behavior can be reproduced using the initialization vector [0, 1, 2, 4, 5, 3]. The result is:
[0, 1, 2, 4, 3, 5]
(we can see that 3 is incorrectly placed)
The Push algorithm is correct. It builds a min-heap in a straightforward way:
- Start from the bottom right
- If the value is greater than the parent node then insert it and return
- Otherwise, put instead the parent in the bottom right position, then try inserting the value at the parent place (and keep swapping up the tree until the right place has been found)
The resulting tree is:
0
/ \
/ \
1 2
/ \ /
4 5 3
The issue is with the Pop method. It starts by considering the top node as a "gap" to fill (since we popped it):
*
/ \
/ \
1 2
/ \ /
4 5 3
To fill it, it searches for the lowest immediate child (in this case: 1). It then moves the value up to fill the gap (and the child is now the new gap):
1
/ \
/ \
* 2
/ \ /
4 5 3
It then does the exact same thing with the new gap, so the gap moves down again:
1
/ \
/ \
4 2
/ \ /
* 5 3
When the gap has reached the bottom, the algorithm... takes the bottom-rightmost value of the tree and uses it to fill the gap:
1
/ \
/ \
4 2
/ \ /
3 5 *
Now that the gap is at the bottom-rightmost node, it decrements _count to remove the gap from the tree:
1
/ \
/ \
4 2
/ \
3 5
And we end up with... A broken heap.
To be perfectly honest, I don't understand what the author was trying to do, so I can't fix the existing code. At most, I can swap it with a working version (shamelessly copied from Wikipedia):
internal void Pop2()
{
if (_count > 0)
{
_count--;
_heap[0] = _heap[_count];
Heapify(0);
}
}
internal void Heapify(int i)
{
int left = (2 * i) + 1;
int right = left + 1;
int smallest = i;
if (left <= _count && _comparer.Compare(_heap[left], _heap[smallest]) < 0)
{
smallest = left;
}
if (right <= _count && _comparer.Compare(_heap[right], _heap[smallest]) < 0)
{
smallest = right;
}
if (smallest != i)
{
var pivot = _heap[i];
_heap[i] = _heap[smallest];
_heap[smallest] = pivot;
Heapify(smallest);
}
}
Main issue with that code is the recursive implementation, which will break if the number of elements is too large. I strongly recommend using an optimized thirdparty library instead.
Edit: I think I found out what is missing. After taking the bottom-rightmost node, the author just forgot to rebalance the heap:
internal void Pop()
{
Debug.Assert(_count != 0);
if (_count > 1)
{
// Loop invariants:
//
// 1. parent is the index of a gap in the logical tree
// 2. leftChild is
// (a) the index of parent's left child if it has one, or
// (b) a value >= _count if parent is a leaf node
//
int parent = 0;
int leftChild = HeapLeftChild(parent);
while (leftChild < _count)
{
int rightChild = HeapRightFromLeft(leftChild);
int bestChild =
(rightChild < _count && _comparer.Compare(_heap[rightChild], _heap[leftChild]) < 0) ?
rightChild : leftChild;
// Promote bestChild to fill the gap left by parent.
_heap[parent] = _heap[bestChild];
// Restore invariants, i.e., let parent point to the gap.
parent = bestChild;
leftChild = HeapLeftChild(parent);
}
// Fill the last gap by moving the last (i.e., bottom-rightmost) node.
_heap[parent] = _heap[_count - 1];
// FIX: Rebalance the heap
int index = parent;
var value = _heap[parent];
while (index > 0)
{
int parentIndex = HeapParent(index);
if (_comparer.Compare(value, _heap[parentIndex]) < 0)
{
// value is a better match than the parent node so exchange
// places to preserve the "heap" property.
var pivot = _heap[index];
_heap[index] = _heap[parentIndex];
_heap[parentIndex] = pivot;
index = parentIndex;
}
else
{
// Heap is balanced
break;
}
}
}
_count--;
}
Kevin Gosse's answer identifies the problem. Although his re-balancing of the heap will work, it's not necessary if you fix the fundamental problem in the original removal loop.
As he pointed out, the idea is to replace the item at the top of the heap with the lowest, right-most item, and then sift it down to the proper location. It's a simple modification of the original loop:
internal void Pop()
{
Debug.Assert(_count != 0);
if (_count > 0)
{
--_count;
// Logically, we're moving the last item (lowest, right-most)
// to the root and then sifting it down.
int ix = 0;
while (ix < _count/2)
{
// find the smallest child
int smallestChild = HeapLeftChild(ix);
int rightChild = HeapRightFromLeft(smallestChild);
if (rightChild < _count-1 && _comparer.Compare(_heap[rightChild], _heap[smallestChild]) < 0)
{
smallestChild = rightChild;
}
// If the item is less than or equal to the smallest child item,
// then we're done.
if (_comparer.Compare(_heap[_count], _heap[smallestChild]) <= 0)
{
break;
}
// Otherwise, move the child up
_heap[ix] = _heap[smallestChild];
// and adjust the index
ix = smallestChild;
}
// Place the item where it belongs
_heap[ix] = _heap[_count];
// and clear the position it used to occupy
_heap[_count] = default(T);
}
}
Note also that the code as written has a memory leak. This bit of code:
// Fill the last gap by moving the last (i.e., bottom-rightmost) node.
_heap[parent] = _heap[_count - 1];
Does not clear the value from _heap[_count - 1]. If the heap is storing reference types, then the references remain in the heap and cannot be garbage collected until the memory for the heap is garbage collected. I don't know where this heap is used, but if it's large and lives for any significant amount of time, it could cause excess memory consumption. The answer is to clear the item after it's copied:
_heap[_count - 1] = default(T);
My replacement code incorporates that fix.