Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Snapchat software engineer interview that came down to a classic multi-list merge problem. Pretty algorithmic, no fluff, they wanted to see if you knew your heap mechanics cold.

Questions Asked (1)

Given N sorted lists, merge them all into a single sorted output. Walk through your approach and analyze the time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Knew this one but still fumbled the explanation a bit at first.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Use a min-heap to efficiently merge the N sorted lists by repeatedly extracting the smallest element and inserting the next element from the same list. This yields O(M log N) time where M is total elements, and O(N) space for the heap. Alternatively, mention divide-and-conquer merging for O(M log N) time and O(1) extra space if merging in-place, but highlight the heap approach as optimal for streaming or large N.

Pro tip: Discuss trade-offs: the heap approach is better for online/streaming scenarios and when N is large, while divide-and-conquer may be preferred if memory is constrained or if lists are on disk. Also, clarify assumptions about list sizes and whether input can be modified.

1. Clarify the problem

Confirm the number of lists (N), total elements (M), and whether lists are sorted ascending. Ask about constraints like memory limits, input size, and if the output should be a new list or can be in-place.

2. Propose a naive approach

Mention the straightforward approach of concatenating all lists and sorting, which takes O(M log M) time, to establish a baseline and show you can think simply before optimizing.

3. Present the optimal heap-based approach

Explain using a min-heap of size N, where each heap element stores the value and its list index. Repeatedly extract the minimum, append to output, and insert the next element from the same list if available.

4. Analyze time and space complexity

Time: O(M log N) because each of M elements is inserted and extracted from the heap in O(log N). Space: O(N) for the heap plus O(M) for the output (or O(1) extra if output is not counted).

5. Discuss alternatives and trade-offs

Mention divide-and-conquer merging (pairwise merge) which also gives O(M log N) time but O(1) extra space if merging in-place, and compare with the heap approach in terms of memory, streaming capability, and implementation complexity.

Key Points to Mention

Min-heap of size N storing (value, list_index) to efficiently get the next smallest element.
Time complexity O(M log N) where M is total elements and N is number of lists.
Space complexity O(N) for the heap (plus output), which is optimal for this approach.
Handling edge cases: empty lists, N=0, N=1, and lists of varying lengths.
Alternative divide-and-conquer approach with O(M log N) time and O(1) extra space if merging in-place.
Trade-offs: heap is better for streaming/large N, divide-and-conquer for memory-constrained environments.

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