Heap / Priority Queue Pattern: LeetCode Guide & Problems
Efficiently track the k-th largest/smallest element or merge sorted sequences.
Problems (14)
| Problem | Solution | Link | |
|---|---|---|---|
| Kth Largest Element in an Array
#Array
#Heap
#Sorting | Solution | ||
| Find Median from Data Stream
#Two Heaps
#Design
#Sorting | Solution | ||
| Task Scheduler
#Array
#Heap
#Greedy | Solution | ||
| Merge K Sorted Lists
#Linked List
#Heap
#Divide and Conquer | Solution | ||
| K Closest Points to Origin
#Array
#Math
#Divide and Conquer | Solution | ||
| Reorganize String
#Hash Table
#String
#Greedy | Solution | ||
| Top K Frequent Words
#Hash Table
#String
#Trie | Solution | ||
| Find K Pairs with Smallest Sums
#Array
#Heap | Solution | ||
| Minimum Cost to Connect Sticks ★
#Array
#Greedy
#Heap | Solution | ||
| Sort an Almost Sorted Array
#Heap
#Priority Queue | Solution | ||
| Find K Closest Elements
#Heap
#Priority Queue | Solution | ||
| Connect Ropes to Minimize the Cost
#Heap
#Priority Queue | Solution | ||
| The sum of Elements between k1 smallest and k2 smallest numbers
#Heap
#Priority Queue | Solution | ||
| Kth Largest Element in a Stream
#Heap
#Priority Queue | Solution |
Mastering the Heap / Priority Queue Pattern for Coding Interviews
1. Mental Model & Visual Intuition
The Heap / Priority Queue pattern is designed to eliminate redundant work and reduce search spaces. Rather than evaluating every possible state using a naive brute-force approach, this pattern relies on an invariant that allows you to safely discard subproblems that cannot lead to an optimal answer. When encountering an interview question, look for core triggers such as monotonicity, contiguous constraints, optimal substructure, or overlapping subproblems.
2. Algorithmic Invariants & Step-by-Step Execution
To implement this pattern flawlessly under interview conditions, structure your thought process into three discrete steps:
- State Initialization: Define your boundaries, pointers, or memoization tables clearly before entering loops.
- State Transition: Update tracking variables incrementally as new elements enter or leave your active evaluation window.
- Feasibility Check: When constraints are violated, systematically shrink or adjust your state until the valid invariant is restored.
3. Time and Space Complexity Trade-Offs
In top-tier technical interviews at Google, Meta, and Amazon, arriving at a working solution is only half the battle. You must explicitly justify why your approach achieves optimal Big-O bounds. For Heap / Priority Queue, the target time complexity is typically O(log n) insert/delete, O(1) peek while maintaining an auxiliary space complexity of O(n). Always discuss whether an in-place modification or two-pass traversal can save memory.
4. Common Interview Edge Cases to Test
Before informing your interviewer that you have completed coding, trace your logic against these standard high-failure test cases:
- Empty inputs, single-element collections, or all-identical values.
- Inputs with negative numbers, duplicates, or extreme values that could cause integer overflow.
- Odd versus even length inputs when dealing with pointer convergence or tree midpoints.
Related Guide
Heap & Priority Queue Problems — Every Pattern With Examples [2026] →The heap is the secret weapon for top-K, K closest, and merge-K problems. Here's how it works and th…