What is a Heap data structure?

A Heap is a specialized tree-based data structure that satisfies the heap property. In a min-heap, for any given node C, if P is a parent node of C, then the key (the value) of P is less than or equal to the key of C. In a max-heap, the key of P is greater than or equal to the key of C.

Why are Heaps useful in interviews?

Heaps are fundamental for implementing priority queues and are crucial for solving problems related to finding the k-th smallest/largest element, median finding in a stream, and in algorithms like Dijkstra's and Prim's.

Priority Queues & Heaps

Heap Interview Questions

Master the heap data structure for your software engineering interviews. Learn to solve problems involving priority queues, k-th elements, and more with our AI-powered guide.

Min-Heap Implementation

Heaps are commonly implemented using arrays. The parent-child relationship is maintained using array indices. For a node at index `i`, its children are at `2*i + 1` and `2*i + 2`, and its parent is at `floor((i-1)/2)`.

                            class MinHeap {
                            constructor() {
                            this.heap = [];
                            }
                            
                            insert(value) { /* ... implementation ... */ }
                            extractMin() { /* ... implementation ... */ }
                            heapifyUp() { /* ... implementation ... */ }
                            heapifyDown() { /* ... implementation ... */ }
                            }
                        

AI Coach Hint: The `heapifyUp` and `heapifyDown` (or `siftUp`/`siftDown`) operations are the core of maintaining the heap property after insertions and deletions. Mastering these is key to solving heap-based interview questions.