Cloudflare·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Cloudflare coding round, one question, pretty standard merge k sorted lists problem. Nothing fancy but they clearly wanted you to know your heap basics.

Questions Asked (1)

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

Algorithms & Data Structures

Author's notes

I knew this one but still fumbled the heap setup a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then discuss multiple approaches such as sequential merging, divide-and-conquer, and heap-based merging. Compare their time and space complexities, and implement the most efficient one (heap-based or divide-and-conquer) with clean code and thorough testing.

Pro tip: Mention that a heap-based approach is optimal for large k, but divide-and-conquer is often preferred in practice for its simplicity and lower constant factors. Also, discuss how to handle edge cases like empty lists and duplicate values.

1. Clarify requirements and constraints

Ask about the number of lists (k), average length, whether lists can be empty, and if the merged list should be a new list or modify existing nodes. Confirm the expected time and space complexity.

2. Discuss possible approaches

Outline at least two approaches: (1) sequentially merging lists one by one, (2) using a min-heap to efficiently select the smallest node among all lists, and (3) divide-and-conquer by merging pairs of lists. Compare their complexities.

3. Choose and justify an approach

Select the most efficient approach based on constraints. For example, if k is large, heap-based O(N log k) is optimal; if k is small, divide-and-conquer may be simpler. Explain your reasoning.

4. Implement the solution

Write clean code for the chosen approach. Use a dummy node to simplify list construction, and handle edge cases like empty input. For heap-based, store (value, list_index, node) tuples.

5. Test and analyze

Walk through test cases: empty lists, single list, lists with different lengths, and duplicate values. Analyze time and space complexity, and discuss potential optimizations or trade-offs.

Key Points to Mention

Time complexity: O(N log k) for heap-based, where N is total nodes and k is number of lists; O(N log k) for divide-and-conquer; O(Nk) for sequential merging.
Space complexity: O(k) for heap, O(1) extra space for divide-and-conquer if merging in-place, O(log k) recursion stack for divide-and-conquer.
Use of a min-heap (priority queue) to efficiently extract the smallest node among k lists.
Divide-and-conquer approach: merge lists in pairs iteratively to reduce the number of lists.
Edge cases: empty input, lists with different lengths, duplicate values, and null nodes.
Stability: if stability is required, ensure that nodes from earlier lists are chosen when values are equal.

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