Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Snapchat SWE interview with a system design coding problem around a custom key-value store. The core challenge was figuring out the right data structure and then handling updates correctly, which is where things got interesting.

Questions Asked (1)

Design a key-value store that supports put(key, value), get(key), and sumByPrefix(prefix), where sumByPrefix returns the sum of all values whose keys start with the given prefix. How would you implement this efficiently, and how do you handle updates to existing keys?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I jumped to a trie pretty fast which felt right, but then the update case tripped me up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., expected scale, update frequency, prefix query frequency) and then propose a trie-based solution where each node stores the sum of values in its subtree. Explain how put, get, and sumByPrefix work in O(L) time (L = key length), and discuss handling updates by adjusting sums along the path.

Pro tip: Mention that you can optimize memory by storing values only at leaf nodes and sums at internal nodes, and consider using a hash map for O(1) get if memory allows. Also, discuss concurrency and persistence if relevant to Snapchat's scale.

1. Clarify Requirements

Ask about expected data size, read/write ratio, latency requirements, and whether keys are strings. This shows you think before coding.

2. Choose Data Structure

Propose a trie (prefix tree) where each node stores the sum of all values in its subtree. Alternatively, mention a hash map + sorted list for prefix sums, but highlight trie's efficiency for prefix queries.

3. Define Operations

Explain put: traverse/create nodes, update value and adjust sums along the path. get: traverse to node and return value. sumByPrefix: traverse to prefix node and return its stored sum.

4. Handle Updates

When updating an existing key, compute the difference between new and old value, then update the sum at each node along the path by adding the difference.

5. Analyze Complexity & Trade-offs

State time complexity O(L) for all operations, space O(total characters). Discuss trade-offs: trie uses more memory but gives fast prefix sums; hash map is faster for get but slower for prefix queries.

Key Points to Mention

Trie node structure: children map, value (optional), subtree sum.
Time complexity: O(L) for put, get, sumByPrefix where L is key length.
Space complexity: O(N*L) worst case, but can be optimized with compression.
Handling updates: adjust sums by delta (new value - old value).
Alternative approaches: hash map + prefix sum array, or balanced BST, and their trade-offs.
Scalability considerations: sharding, concurrency, persistence for large-scale systems.

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