Merge k Sorted Lists

Algorithms
Hard
Amazon
50.1K views

You are given an array of $k$ linked-lists, each sorted in ascending order. Merge all the linked-lists into one sorted linked list. Use a Min-Heap (Priority Queue) for an efficient solution.

Why Interviewers Ask This

Amazon asks this question to evaluate a candidate's mastery of advanced data structures and ability to optimize for scalability. Specifically, they test your capacity to move beyond naive solutions that degrade performance as input size grows. The interviewer looks for proficiency in using Min-Heaps to manage complexity efficiently, demonstrating the 'Invent and Simplify' leadership principle by finding elegant algorithmic shortcuts rather than brute-force methods.

How to Answer This Question

1. Clarify constraints immediately: Ask about the number of lists (k) and average list length to determine if O(N*k) or O(N log k) is required. 2. Propose the naive approach first: Briefly explain merging two lists repeatedly, noting its O(N*k) time complexity to show you understand the baseline problem. 3. Introduce the optimal solution: Pivot to the Min-Heap strategy. Explain how inserting the head of each list into a priority queue allows constant-time access to the smallest element. 4. Detail the algorithm flow: Describe the loop where you extract the minimum node, append it to the result, and push the next node from that specific list back into the heap. 5. Analyze complexity: Explicitly state the Time Complexity as O(N log k) and Space Complexity as O(k), justifying why this meets Amazon's high-performance standards for distributed systems.

Key Points to Cover

  • Demonstrating knowledge of O(N log k) vs O(N*k) time complexity trade-offs
  • Correctly implementing the Min-Heap logic for dynamic element selection
  • Handling null pointers and empty list edge cases gracefully
  • Using a dummy node to simplify linked list construction logic
  • Articulating space complexity as O(k) due to heap storage

Sample Answer

To solve the Merge k Sorted Lists problem efficiently, I would avoid the naive approach of iteratively merging pairs of lists, which results in a quadratic time complexity relative to the total number of nodes. Instead,…

Common Mistakes to Avoid

  • Failing to handle the case where one or more input lists are initially empty
  • Attempting to merge lists sequentially without a heap, leading to TLE on large inputs
  • Forgetting to re-insert the next node from the same list after extracting the current minimum
  • Not explicitly stating the time and space complexity analysis during the solution walkthrough

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