By Ignatius Teo

Data Structures for PHP Devs: Heaps

By Ignatius Teo

Data Structures for PHP Devs

In past couple articles I’ve introduced you to three basic data structures: stack, queue, and tree. In this article I’ll introduce you to another abstract data type that is closely related: heap. Heaps are specialized tree-like data structures which satisfy the heap property – the node value (key) of any parent is always ordered with respect to its child node values across the entire tree. Let’s take a closer look!


There are several heap variants. For instance, if parent keys are ordered such that they are of equal or greater value than their children, and the highest key is at the root, the heap is said to be a maxheap. If parent keys are ordered such that they are of equal or lower value than their children, with the lowest key at the root, the heap is called a minheap. SPL (PHP>=5.3.0) provides a basic heap, minheap, maxheap, and a specialized data type called a Priority Queue.

So here’s an example of what a heap looks like:

The figure above depicts a complete binary maxheap. It’s binary because each node has exactly two children, and a maxheap because the highest value key is at the root node and all parent nodes have values greater than their children.

While heaps are usually implemented as complete binary trees, unlike binary trees there is no implied ordering of siblings and cousins, nor is there any implied sequence for an in-order traversal.

Heaps are a variant of the table data type so they also have the same basic operations:

  • create – create an empty heap.
  • isEmpty – determine if the heap is empty.
  • insert – add an item to the heap.
  • extract – remove the topmost item (root) item from the heap.

Unlike a table, the retrieve and delete operations for a heap are combined into a single extract operation, so let’s focus on how the extract operation works first.

The condition for removing an item from the heap is that we can only remove the root node. Let’s say we removed the root node (100) from the above example. We would be left with two disjoint heaps. We need a method of transforming the remaining nodes back into a single heap after the root is removed. Joining them is easily accomplished by moving the last node to the root, but we’re left with a resulting structure that fails the heap property. Such a structure is called a semiheap.

We now need a way to transform the semiheap into a heap. One strategy is to trickle the root item down the tree until it reaches a node where it will not be out of place. We iteratively compare the value of the root node to its children and swap places with the larger child until it arrives at a node where no child has a greater (or equal) value than itself.

Implementing a Heap as an Array

We can naively implement a binary maxheap as an array. A binary bode has at most two children, therefore for any n number of nodes a binary heap will have at most 2n + 1 nodes.

Here’s what an implementation looks like:

class BinaryHeap
    protected $heap;

    public function __construct() {
        $this->heap  = array();

    public function isEmpty() {
        return empty($this->heap);

    public function count() {
        // returns the heapsize
        return count($this->heap) - 1;

    public function extract() {
        if ($this->isEmpty()) {
            throw new RunTimeException('Heap is empty');

        // extract the root item
        $root = array_shift($this->heap);

        if (!$this->isEmpty()) {
            // move last item into the root so the heap is
            // no longer disjointed
            $last = array_pop($this->heap);
            array_unshift($this->heap, $last);

            // transform semiheap to heap

        return $root;

    public function compare($item1, $item2) {
        if ($item1 === $item2) {
            return 0;
        // reverse the comparison to change to a MinHeap!
        return ($item1 > $item2 ? 1 : -1);

    protected function isLeaf($node) {
        // there will always be 2n + 1 nodes in the
        // sub-heap
        return ((2 * $node) + 1) > $this->count();

    protected function adjust($root) {
        // we've gone as far as we can down the tree if
        // root is a leaf
        if (!$this->isLeaf($root)) {
            $left  = (2 * $root) + 1; // left child
            $right = (2 * $root) + 2; // right child

            // if root is less than either of its children
            $h = $this->heap;
            if (
              (isset($h[$left]) &&
                $this->compare($h[$root], $h[$left]) < 0)
              || (isset($h[$right]) &&
                $this->compare($h[$root], $h[$right]) < 0)
            ) {
                // find the larger child
                if (isset($h[$left]) && isset($h[$right])) {
                  $j = ($this->compare($h[$left], $h[$right]) >= 0)
                      ? $left : $right;
                else if (isset($h[$left])) {
                  $j = $left; // left child only
                else {
                  $j = $right; // right child only

                // swap places with root
                list($this->heap[$root], $this->heap[$j]) = 
                  array($this->heap[$j], $this->heap[$root]);

                // recursively adjust semiheap rooted at new
                // node j

The insertion strategy is the exact opposite of extraction: we insert the item at the bottom of the heap and trickle it up to its correct location. Since we know that a complete binary tree with a full last level contains n/2 + 1 nodes, we can traverse the heap using a simple binary search.

public function insert($item) {
    // insert new items at the bottom of the heap
    $this->heap[] = $item;

    // trickle up to the correct location
    $place = $this->count();
    $parent = floor($place / 2);
    // while not at root and greater than parent
    while (
      $place > 0 && $this->compare(
        $this->heap[$place], $this->heap[$parent]) >= 0
    ) {
        // swap places
        list($this->heap[$place], $this->heap[$parent]) =
            array($this->heap[$parent], $this->heap[$place]);
        $place = $parent;
        $parent = floor($place / 2);

So let’s see what happens when we insert a few items into the structure:

$heap = new BinaryHeap();

If you dump the heap structure you’ll notice that the items aren’t in any particular order. In fact, it’s not even in the order we would expect:

    [0] => 100
    [1] => 67
    [2] => 54
    [3] => 36
    [4] => 19
    [5] => 7
    [6] => 25
    [7] => 1
    [8] => 17
    [9] => 2
    [10] => 3

However, if you extract the items, this is what you would get:

while (!$heap->isEmpty()) {
    echo $heap->extract() . "n";

SplMaxHeap and SplMinHeap

Fortunately for us, SplHeap, SplMaxHeap and SplMinHeap abstracts all of this for us. All we have to do is extend the base class and override the comparison method like so:

class MyHeap extends SplMaxHeap
    public function compare($item1, $item2) {
        return (int) $item1 - $item2;

The compare method can perform any arbitrary comparison, as long as it returns – in the case of a SplMaxHeap – a positive integer if $item1 is greater than $item2, 0 if they are equal, or a negative integer otherwise. If extending SplMinHeap, it should instead return a positive integer if $item1 is less than $item2.

It’s not recommended to have multiple elements with the same value in a heap as they may end up in an arbitrary relative position.


The Priority Queue is a specialized abstract data type that behaves like a queue, but is usually implemented as a heap – in the case of SplPriorityQueue, as a maxheap. Prioritized queues have many real-world applications, such as service desks/ticket escalation. They are also essential in improving the performance of certain graph applications.

Like the SplHeap, you only have to override the base class and comparator method:

class PriQueue extends SplPriorityQueue
    public function compare($p1, $p2) {
        if ($p1 === $p2) return 0;
        // in ascending order of priority, a lower value
        // means higher priority
        return ($p1 < $p2) ? 1 : -1;

The main difference in a SplPriorityQueue is that the insert operation expects a priority value – which can be a mixed data type. The insert operation uses the priority to sift the element up the heap based on the returned result of your comparator.

For illustration purposes, let’s use integer priorities:

$pq = new PriQueue();
$pq->insert('A', 4);
$pq->insert('B', 3);
$pq->insert('C', 5);
$pq->insert('D', 8);
$pq->insert('E', 2);
$pq->insert('F', 7);
$pq->insert('G', 1);
$pq->insert('H', 6);
$pq->insert('I', 0);
while ($pq->valid()) {
    echo "n";

Notice that our items are displayed in order of priority – highest to lowest (the lower the value, the higher the priority). You can reverse the priority order by changing the comparator to return a positive integer if $p1 is greater than $p2.

By default, only the element’s data is extracted. If you wanted to extract the priority values only, or both the data and priority, you can set the extract flag like this:

// extract just the priority

// extract both data and priority (returns an associative
// array for each element)


I’ve introduced you to the heap abstract data type and showed you how items are inserted and extracted from a heap using a simple array implementation. We’ve also seen how the comparator is used in min and maxheaps, and how the Priority Queue operates. Stay tuned; in the next article I’ll discuss graphs!

Image via Fotolia

  • Mikey

    Whoa! I just learned about these data structures in one of my Computer Science course, however we used Python. It’s very interesting to see how it is implemented in PHP. What are the benefits of using heaps versus trees, stacks, and queues? And what would you use heaps for in a project?

  • Aleksey

    Thank you for the article. Really interesting and very understandable!

  • thanks for the update!

  • > We can naively implement a binary maxheap as an array. A binary bode has at most two children, therefore for any n number of nodes a binary heap will have at most 2n + 1 nodes.

    I read this maybe ten times before I understood what you meant.
    For anyone else who can’t grasp it, I’ll reword it and make some explanations:

    Let’s implement the heap as array counting items from left to right, top to bottom. That is for the second picture we start at the top (let’s call it level 1):
    $a[0] = 7; // 1st level, root
    $a[1] = 19; // 2nd level, left child of root
    $a[2] = 36; // 2nd level, right child of root
    $a[3] = 17; // 3rd level, left child of $a[1]
    $a[4] = 3; // 3rd level, right child of $a[1]

    $a[7] = 2; // 4th level, left child of $a[3]

    The claim is that a COMPLETE binary tree has a good property that allows us to quickly detect if the node is a leaf. The node $a[$n] is a leaf if the complete binary tree contains not more than 2*$n+1 items.
    quick check: count($a) = 8; $a[3] shouldn’t be a leaf (8 > 2*3+1) – correct; $a[4] should be a leaf (8 <= 2*4+1) – correct;

    • Ignatius Teo

      That’s correct. A “complete” (and “full”) binary tree is one in which all (interior) nodes have either 2 children, or 0 children (i.e. it becomes a leaf node). The root node is the only one that is not a child.

      The mathematical proof lies in the following relationship:
      The total number of nodes ($n) in a tree comprises leaves ($l) and interior nodes ($i), i.e. $n = $l + $i. Therefore, if you have ($i) interior nodes, and each interior node is a parent of 2 children, that gives you 2*$i children. Plus the root node is (2*$i)+1.

      So in the 1st diagram, you have 4 interior nodes – the root node counts as an interior node since it has 2 children – and 5 leaves for a total of 9 nodes. i.e. n = l + i => 9 = 4 + 5, and 2i + 1 => 2(4) + 1 = 9.

      As we’re using an array to represent our binary heap, we can infer that the 2nd half (i.e. floor($n/2)) of the array will contain the leaf nodes – since a complete binary heap with a full last level will have $n/2 + 1 nodes in it. So, when you consider a node rooted at $i, if 2*$i + 1 > the last item in the tree (i.e. $n), the node must be a leaf.

      Thus, as you rightly inferred, if $n is the total number of nodes in binary tree, the node rooted at position $i is a leaf node, if $n <= 2*$i + 1.

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