<pre class="shiki github-dark shaku"><code>const getParent = idx =&gt; Math.floor((idx - 1) / 2)const getLeftChild = idx =&gt; idx * 2 + 1const getRightChild = idx =&gt; idx * 2 + 2// complete the implementationclass PriorityQueue { #heap = [] /** * @param {(a: any, b: any) =&gt; -1 | 0 | 1} compare - * compare function, similar to parameter of Array.prototype.sort */ constructor(compare) { this.compare = (a, b) =&gt; compare(this.#heap[a], this.#heap[b]) &gt; 0; } /** * return {number} amount of items */ size() { return this.#heap.length } /** * returns the head element */ peek() { return this.#heap[0] } /** * @param {any} element - new element to add */ add(element) { this.#heap.push(element) this.moveUp(this.#heap.length - 1) } /** * remove the head element * @return {any} the head element */ poll() { if (!this.#heap.length) { return undefined } swap(this.#heap, 0, this.#heap.length - 1) const el = this.#heap.pop() this.heapify(0) return el } heapify(idx = 0) { const childIdx = this.getChild(idx) if (idx !== childIdx) { swap(this.#heap, idx, childIdx) this.heapify(childIdx) } } moveUp(idx) { if (!idx) { return } let parentIdx = getParent(idx) if (this.compare(parentIdx, idx)) { swap(this.#heap, idx, parentIdx) this.moveUp(parentIdx) } } getChild(idx) { const leftIdx = getLeftChild(idx) const rightIdx = getRightChild(idx) let childIdx = idx if (leftIdx &lt; this.#heap.length &amp;&amp; this.compare(childIdx, leftIdx)) { childIdx = leftIdx } if (rightIdx &lt; this.#heap.length &amp;&amp; this.compare(childIdx, rightIdx)) { childIdx = rightIdx } return childIdx }}function swap(list, x, y) { [list[x], list[y]] = [list[y], list[x]];}const pq = new PriorityQueue((a, b) =&gt; a - b)pq.add(5)pq.add(3)pq.add(1)pq.add(4)pq.add(2)const result = []while (pq.size() &gt; 0) { result.push(pq.poll())}console.log(result)</code></pre>

This is my solution

Priority Queue is a commonly used data structure in algorithm problem. Especially useful for Top K problem with a huge amount of input data, since it …