A heap is a data structure that supports the operations insert and extract. Heaps typically come in two varieties, min heap (for extracting the minimum value) and max heap. A heap is built using a binary tree where each node is said to dominate the nodes below it. The meaning of dominate depends on the type of heap being implemented, for a min heap the key of each node is less than the keys of both of child nodes.

From here on, without loss of generality, we will talk about a heap of integers (keys) ignoring satellite data that may or may not be associated with each integer key. Node and key will therefore be used interchangeably.

Like a binary search tree a heap can be implemented using a linked data structure. There is however, a nifty method of implementing a heap using an array, thereby reducing the memory requirements since there are no pointers to store. We store the data as an array of keys and use the index of the keys to implicitly satisfy the role of pointers.

The root node is stored at index 1 and all operations are 1-indexed.

For node at index k the left child can be found at index 2k and the right child at index 2k + 1. (Quite clearly, checks must be made prior to access that an index lies within the underlying array).

One additional limitation must be placed on the tree, that it is complete, i.e all levels of the tree are full with the possible exception of the last level, and if the last level is not full all nodes are as far to the left as possible. This limitation results in an array with no holes in it.

A heap as just described may be defined in Go as such;

``````type heap struct {
xs []int
}
``````

We wrap the array inside a struct to abstract the implementation and limit access to the functions that we define. Care must be taken to remember that the heap is 1-indexed.

``````func (h *heap) Len() int {
return len(h.xs) - 1 // heap is 1-indexed
}
``````

Insertion of a key into a heap can be achieved by adding the key to the end of the underlying array. Whilst maintaining the structure of the heap this may violate the heap property of the parent of the newly inserted key, namely the parent of the newly inserted key may not be dominant. The heap property for the parent node can be restored by swapping the newly inserted child node with the parent. This may in turn, result in the parent above violating the heap property. We can continue this bubbling of a node to successively higher nodes until the heap property is restored. When swapping a node with the parent node we need not consider the sibling node since if a node dominates the parent then by definition it dominates the other sibling also.

``````// Insert x into the heap
func (h *heap) Insert(x int) {
(*h).xs = append(h.xs, x)
h.bubbleUp(len(h.xs) - 1)
}
``````

Note that parenthesis are required to dereference the heap pointer in order to save the return value of `append()`.

``````func (h *heap) bubbleUp(k int) {
p, ok := parent(k)
if !ok {
return // k is root node
}
if h.xs[p] > h.xs[k] {
h.xs[k], h.xs[p] = h.xs[p], h.xs[k]
h.bubbleUp(p)
}
}
``````

The function terminates when either the node is in place or it has reached the root position.

We define functions for manipulating indices as described above;

``````// get index of parent of node at index k
func parent(k int) (int, bool) {
if k == 1 {
return 0, false
}
return k / 2, true
}

// get index of left child of node at index k
func left(k int) int {
return 2 * k
}

// get index of right child of node at index k
func right(k int) int {
return 2*k + 1
}
``````

We are now ready to extract the key for which our heap was designed (minimum or maximum). This key is clearly at index 1, extracting this key however, leaves a hole which must be filled. Swapping the last key of the array into the hole restores the heap structure but once again may violate the heap property. In a similar fashion to insertion we can restore the heap property by bubbling this node down the tree until it is either a leaf node or no longer violates the heap property. In doing this we must consider both child nodes and swap any non-dominant parent node with the child node that is most dominant in order for the heap property of these three nodes to be maintained.

``````// ExtractMin: get minimum value of heap
// and remove value from heap
func (h *heap) ExtractMin() (int, bool) {
if h.Len() == 0 {
return 0, false
}
v := h.xs
h.xs = h.xs[h.Len()]
(*h).xs = h.xs[:h.Len()]
h.bubbleDown(1)
return v, true
}

func (h *heap) bubbleDown(k int) {
min := k
c := left(k)

// find index of minimum value (k, k's left child, k's right child)
for i := 0; i < 2; i++ {
if (c + i) <= h.Len() {
if h.xs[min] > h.xs[c+i] {
min = c + i
}
}
}
if min != k {
h.xs[k], h.xs[min] = h.xs[min], h.xs[k]
h.bubbleDown(min)
}
}
``````

Caution is again needed here since the array indexing and loop conditionals are unusual because of the 1-indexing.

See Github for complete source code and tests.

### Final Note

This implementation is based on the text The Algorithm Design Manual [Ski08]. In this, the author Steven S. Skiena makes an interesting observation on the construction of a heap. At first glance one may think to construct a heap by repeated calls to `insert()`. Inserting into a heap (like any balanced tree) takes O(log n)), so heap construction in this manner has worst case running time of O(n log n). We can do better, Skiena notes, if we observe that a full, complete tree of n nodes has n/2 leaf nodes. These leaf nodes may be considered as sub-trees that maintain the heap property (since they have only a single node). If then, we base a heap on any array we need only bubble up n/2 nodes in order to achieve a heap for which the heap property holds. This still gives an upper bound of O(n log n), however Skiena goes on to show that this leads to a not quite geometric series that he then assures us quickly converges to linear. He does however, caution us that this benefit may not be that useful if the algorithm we plan to use our heap for is not governed by the construction (i.e heapsort will still run in O(n log n)).

#### Bibliography:

[Ski08] - The Algorithm Design Manual, Steven S. Skiena