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Overview

A binary heap is a tree-shaped data structure, usually stored in an array, that keeps elements arranged so the highest-priority item is always at the root.
OperationsComplexityDescription
PeekO(1)The top element is always at the root.
InsertO(log n)The tree needs to be adjusted to keep the heap structure.
Remove TopO(log n)The tree needs to be adjusted to keep the heap structure.

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Structure

Binary heaps are complete binary trees, meaning they stay compact by filling levels from left to right.

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Min-heap

Every parent is smaller than or equal to its children, so the minimum value is at the top. [10, 15, 20, 30, 45, 50, 25, 75, 100] min-heap

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Max-heap

Every parent is greater than or equal to its children, so the maximum value is at the top. [100, 75, 50, 30, 45, 20, 25, 15, 10] max-heap

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Relationships

In an array-based heap, parent and child positions are found using simple index formulas.
Example heap: [100, 75, 50, 30, 45, 20, 25, 15, 10]
Parent index: 
const parentIdx = Math.floor((i - 1) / 2);
The parent index of 45 (at index 4) is Math.floor((4 - 1) / 2) = Math.floor(1.5) = 1, which is 75. Left child index: 
const leftChildIdx = i * 2 + 1;
The left child index of 75 (at index 1) is 1 * 2 + 1 = 3, which is 30. Right child index: 
const rightChildIdx = i * 2 + 2;
The right child index of 75 (at index 1) is 1 * 2 + 2 = 4, which is 45.

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Implementation

Binary Heap (PriorityQueue)
class PriorityQueue {
  constructor(compare) {
    this.compare = compare;
    this.heap = [];
  }

  peek() {
    return this.heap[0];
  }

  insert(value) { ... } 

  remove() { ... } 

  #getParentIdx(i) {
    return Math.floor((i - 1) / 2);
  }

  #getLeftChildIdx(i) {
    return i * 2 + 1;
  }

  #getRightChildIdx(i) {
    return i * 2 + 2;
  }

  #swap(i, j) {
    [this.heap[i], this.heap[j]] = [this.heap[j], this.heap[i]];
  }

  #percolateUp(i) {
    while (i > 0) {
      const parentIdx = this.#getParentIdx(i);

      if (this.compare(this.heap[i], this.heap[parentIdx]) >= 0) {
        break;
      }

      this.#swap(i, parentIdx);
      i = parentIdx;
    }
  }

  #percolateDown(i) {
    const len = this.heap.length;

    while (true) {
      const leftIdx = this.#getLeftChildIdx(i);
      const rightIdx = this.#getRightChildIdx(i);
      let best = i;

      if (leftIdx < len && this.compare(this.heap[leftIdx], this.heap[best]) < 0) {
        best = leftIdx;
      }

      if (rightIdx < len && this.compare(this.heap[rightIdx], this.heap[best]) < 0) {
        best = rightIdx;
      }

      if (best === i) {
        break;
      }

      this.#swap(i, best);
      i = best;
    }
  }
}
Usage:
The initial compare function decides what “higher priority” means, so it determines whether the heap behaves like a min-heap or a max-heap.
// Min-heap (numbers)
const pq1 = new PriorityQueue((a, b) => a - b);
pq1.insert(5);
pq1.insert(2);
pq1.insert(6);
pq1.insert(10);
console.log(pq1.remove()); // 2
console.log(pq1.peek()); // 5

// Max-heap (numbers)
const pq2 = new PriorityQueue((a, b) => b - a);
pq2.insert(5);
pq2.insert(2);
pq2.insert(6);
pq2.insert(10);
console.log(pq2.remove()); // 10
console.log(pq2.peek()); // 6

// Objects by field (min-heap)
const tasks = new PriorityQueue((a, b) => a.priority - b.priority);
tasks.insert({ id: 'a', priority: 10 });
tasks.insert({ id: 'b', priority: 1 });
console.log(tasks.remove()); // { id: "b", priority: 1 }

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Insert

The insert method appends the new value to the end of the array, then percolates up from that last index.
Repeatedly compare the current value with its parent. If the current value has higher priority according to compare (meaning compare(child, parent) < 0), swap them.
insert(value) {
  this.heap.push(value);

  this.#percolateUp(this.heap.length - 1);
}

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Remove

The remove method first handles small cases. If the heap is empty, it returns undefined. If it has one item, it pops and returns it. Otherwise, it stores the top value (at index 0), replaces the root with the last element (using pop()), then percolates down from the root.
Repeatedly compare the current node with its children. Choose the child with higher priority according to compare (the one that should be closer to the top), and swap if that child should come before the current node. When done, return the stored top value.
remove() {
  const len = this.heap.length;

  if (len === 0) {
    return undefined;
  }

  if (len === 1) {
    return this.heap.pop();
  }

  const top = this.heap[0];
  this.heap[0] = this.heap.pop();

  this.#percolateDown(0);

  return top;
}

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Heapify (Optional)

Turn a random array into a Binary Heap. It copies the array into the internal heap, then works bottom-up from the last parent to the root. At each parent index, it percolates down to fix that subtree. Once it reaches index 0, the whole array satisfies the heap structure.
Inserting items one by one takes O(n log n), but heapify builds the heap in O(n).
heapify(arr) {
  this.heap = arr.slice();

  const lastParentIdx = this.#getParentIdx(this.heap.length - 1);
  for (let i = lastParentIdx; i >= 0; i--) {
    this.#percolateDown(i);
  }
}
Usage: 
const myArr = [6, 8, 3, 4, 5, 2, 0, 1, 7, 11, 18, 17, 16, 15, 14, 20, -1, -10, 30];

// Min-heap:
const pq1 = new PriorityQueue((a, b) => a - b);
pq1.heapify(myArr);
console.log(pq1.peek()); // -10

// Max-heap:
const pq2 = new PriorityQueue((a, b) => b - a);
pq2.heapify(myArr);
console.log(pq2.peek()); // 30

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