Snapchat·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Snapchat SWE interview that went deep into streaming data design. The question sounds straightforward until you're actually in it and realize how many edge cases they want you to think through.

Questions Asked (1)

Design a class that supports a live data stream and can return the top K elements efficiently at any point. Walk through your choice of data structures, the time and space complexity of your add and topK operations, how you handle ties, and what changes if the stream is arbitrarily large.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight to a min-heap of size K backed by a hash map for frequency counts, which is the right skeleton.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: K is fixed, stream is live, and top K must be returned efficiently. Propose a min-heap of size K for add (O(log K)) and topK (O(K log K) if sorted, O(K) if unsorted). Discuss tie-breaking, then address large streams with distributed or approximate methods.

Pro tip: Mention that for Snapchat, where streams are high-velocity and memory-bound, you might combine a heap with a hash map for counts and consider approximate algorithms like Count-Min Sketch for scalability. This shows you think beyond textbook solutions.

1. Clarify requirements and assumptions

Ask if K is fixed, if ties matter, and if exact top K is required. Confirm stream characteristics: velocity, memory limits, and whether elements have counts or are unique.

2. Propose data structure and operations

Choose a min-heap of size K to maintain top K. For add, if heap size < K, insert; else if new element > heap root, replace root and heapify. For topK, return heap elements (sorted if needed).

3. Analyze complexity and tie-breaking

State time: add O(log K), topK O(K) unsorted or O(K log K) sorted. Space: O(K). For ties, define a rule (e.g., earliest timestamp wins) and adjust comparator.

4. Address arbitrarily large streams

Discuss memory limits: use distributed heaps (e.g., merge local top Ks), or approximate algorithms (Count-Min Sketch + heap) for high cardinality. Mention trade-offs: exactness vs. scalability.

5. Summarize and offer extensions

Recap the solution, then suggest extensions like sliding window top K or handling weighted elements, showing depth.

Key Points to Mention

Min-heap of size K for efficient add and topK
Time complexity: add O(log K), topK O(K) or O(K log K)
Space complexity: O(K) for heap, plus O(U) if using hash map for counts
Tie-breaking strategy: define comparator (e.g., by count then timestamp)
Scalability: distributed top K via local heaps and merge, or approximate algorithms like Count-Min Sketch
Trade-offs: exact vs. approximate, memory vs. accuracy, latency vs. throughput

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