Skip to main content

Essential Heap & Priority Queue Problems for SDE Interviews

· 5 min read

If you're preparing for placement season or looking to sharpen your Heap and Priority Queue skills, I've compiled a hand-picked list of problems that cover the depth and breadth of this topic.

This isn't a magic list that guarantees you'll ace every interview. But these are the standard problems and patterns I've consistently seen across interview experiences, company hiring tests, and coding assessments.

I've organized them by pattern, because that's how real interviews work. Once you recognize whether a problem needs a "Top K" approach, a "Two Heaps" setup, or a "K-Way Merge," the solution clicks into place.

Connect with me on LinkedIn to stay updated with more coding resources, interview tips, and problem-solving strategies!

Problem List by Pattern​

1. Top K Elements​

  1. Kth Largest Element in an Array
  2. K Closest Points to Origin
  3. Top K Frequent Elements
  4. Top K Frequent Words
  5. Kth Largest Element in a Stream
  6. Find K Pairs with Smallest Sums
  7. Sort Characters By Frequency
  8. Reorganize String

2. Merge K Sorted Structures​

  1. Merge K Sorted Lists
  2. Kth Smallest Element in a Sorted Matrix
  3. Find K-th Smallest Pair Distance
  4. Smallest Range Covering Elements from K Lists

3. Two Heaps Pattern​

  1. Find Median from Data Stream
  2. Sliding Window Median
  3. IPO

4. Heap for Scheduling & Tasks​

  1. Task Scheduler
  2. Maximum CPU Load
  3. Employee Free Time
  4. Meeting Rooms II
  5. Minimum Number of Arrows to Burst Balloons
  6. Car Pooling
  7. Rearrange String k Distance Apart

5. Dijkstra & Heap-Based Graph Traversal​

  1. Network Delay Time
  2. Path with Maximum Probability
  3. Cheapest Flights Within K Stops
  4. Swim in Rising Water
  5. Path With Minimum Effort
  6. Minimum Cost to Reach Destination in Time

6. Heap with Lazy Deletion & Advanced Techniques​

  1. Find Right Interval
  2. Maximum Performance of a Team
  3. The Skyline Problem
  4. Trapping Rain Water II
  5. Minimum Cost to Hire K Workers
  6. Furthest Building You Can Reach
  7. Single-Threaded CPU

7. Heap for Design Problems​

  1. Design Twitter
  2. LFU Cache
  3. Seat Reservation Manager
  4. Stock Price Fluctuation

8. Competitive Programming Classics​

  1. Josephus Problem
  2. Room Allocation
  3. Tasks and Deadlines
  4. Reading Books
  5. Movie Festival II
  6. ABC 141 D - Powerful Discount Tickets
  7. ABC 181 D - Hachi
  8. CF - Codeforces - Heap Operations
  9. CF - Minimize the Difference

Why This List Works​

Pattern-Based Organization: Each section builds on a specific heap technique, so you develop strong pattern recognition rather than memorizing individual solutions.

Progressive Difficulty: Within each pattern, problems range from straightforward to advanced, letting you build confidence before tackling harder variants.

Covers All Scenarios: From classic Top-K and scheduling problems to Dijkstra-based graph traversal and advanced design challenges, this list hits all heap scenarios you'll see in interviews.

Final Thoughts​

The heap is one of those data structures that once you internalize the patterns, you'll spot heap problems instantly. It's not about memorizing — it's about recognizing when you need efficient min/max access and building the intuition for when a min-heap vs max-heap is appropriate.

These problems represent the essential heap patterns you should solve before interviews. Master the two heaps pattern and the top-K pattern first — they cover a huge portion of interview questions.

Let's connect on LinkedIn - I regularly share interview tips, problem-solving strategies, and coding resources that can help accelerate your preparation!

You've got this!