Snapchat·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Snapchat SWE interview that went pretty deep into co-occurrence counting, which I wasn't fully expecting. The question started simple and then kept expanding into streaming, memory constraints, and complexity analysis. Left feeling like I handled the code okay but fumbled some of the design discussion.

Questions Asked (1)

Given a stream of purchase orders (each order is a list of product IDs bought together), implement a function that returns the k products most frequently bought alongside a given product ID. Ties should be broken by smaller product ID. Then discuss how you'd handle streaming updates, memory limits for large product ID spaces, and the time/space complexity of both building and querying the co-occurrence data.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with the batch case, used a dict of dicts to store co-occurrence counts, and that part went fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define the input stream, output format, and tie-breaking rules. Then propose a solution using a hash map to store co-occurrence counts, and a min-heap or sorting to retrieve the top k. Finally, discuss scalability by addressing streaming updates, memory optimization, and complexity analysis.

Pro tip: Demonstrate awareness of real-world constraints by mentioning approximate algorithms like count-min sketch for memory efficiency, and discuss how to handle dynamic updates without recomputing everything from scratch.

1. Clarify Requirements and Assumptions

Confirm the input format (stream of orders), output (k product IDs), and tie-breaking rule (smaller ID first). Ask about data volume, update frequency, and memory constraints.

2. Design Core Data Structure

Propose a hash map where keys are product IDs and values are another hash map mapping co-occurring product IDs to their counts. This allows efficient updates and queries.

3. Implement Query for Top K

For a given product, retrieve its co-occurrence map, then use a min-heap of size k or sort the entries to get the top k, ensuring ties are broken by smaller product ID.

4. Address Streaming Updates and Memory Limits

Discuss incremental updates: for each new order, update counts for all pairs. For memory, consider pruning low-frequency pairs, using approximate counting (e.g., count-min sketch), or sharding by product ID.

5. Analyze Complexity and Trade-offs

Provide time/space complexity for building (O(total pairs) time, O(unique pairs) space) and querying (O(degree) time to retrieve, O(k log k) to sort). Discuss trade-offs between exact and approximate methods.

Key Points to Mention

Use of hash maps for co-occurrence counting to achieve O(1) average update time per pair.
Efficient top-k retrieval using a min-heap of size k, with tie-breaking by product ID.
Streaming updates: process each order incrementally, updating counts without full recomputation.
Memory optimization: pruning, approximate counting (count-min sketch), or distributed storage for large product spaces.
Time complexity: building O(N*L^2) where N is number of orders and L is average order length; querying O(D + k log k) where D is distinct co-occurring products.
Space complexity: O(P^2) worst-case, but typically sparse; discuss sparsity and compression techniques.

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