Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Google SWE interview with a classic hard linked list problem. Not much to say about the setup but the question itself took me a while to get right.

Questions Asked (1)

Given k sorted linked lists, merge them all into a single sorted linked list.

Algorithms & Data Structures

Author's notes

My first instinct was to just repeatedly merge two lists at a time, which works but is not great on complexity.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., k and total nodes) and discussing naive approaches like sequential merging. Then present the optimal heap-based solution, explaining its time and space complexity, and optionally mention the divide-and-conquer alternative. Finally, walk through a small example to demonstrate correctness.

Pro tip: Mention that for small k, a simple sequential merge might be more efficient due to lower constant factors, but for large k, the heap approach is asymptotically better. This shows you consider practical trade-offs, not just theoretical complexity.

1. Clarify constraints and edge cases

Ask about the range of k, total number of nodes, and whether lists can be empty. Discuss edge cases like k=0 or all lists empty.

2. Discuss naive approaches

Explain that merging lists one by one takes O(kN) time, which is inefficient for large k. This sets the stage for optimization.

3. Present heap-based solution

Describe using a min-heap of size k to repeatedly extract the smallest node and insert its next node. Analyze time complexity O(N log k) and space O(k).

4. Mention alternative: divide and conquer

Briefly explain that pairwise merging in a tournament style also achieves O(N log k) time but with O(1) extra space if done iteratively.

5. Walk through an example

Use a small example (e.g., k=3 lists) to illustrate how the heap approach works step by step, ensuring clarity.

Key Points to Mention

Time complexity: O(N log k) where N is total nodes and k is number of lists.
Space complexity: O(k) for the heap (or O(1) for divide-and-conquer).
Use of a min-heap (priority queue) to efficiently select the smallest current node.
Handling of empty lists and null pointers.
Comparison with naive sequential merge (O(kN) time).
Stability of the merge (if required) and how to maintain it.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.