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

SeniorPrefer not to say

Jun 2026

Summary

Snowflake software engineer interview with a system design round focused on building an in-memory key-value store from scratch. The scope kept expanding with follow-ups and I wasn't fully prepared for how deep they'd go.

Questions Asked (1)

Design and implement an in-memory key-value store supporting set, get, and delete operations. Be prepared to discuss data structure choices and time complexity.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

Started fine, hashmap for O(1) ops, nothing groundbreaking.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., expected operations, concurrency, persistence) and then propose a hash table as the core data structure, explaining its O(1) average time complexity. Implement the basic operations and discuss trade-offs such as collision handling, resizing, and potential need for thread safety.

Pro tip: Mention that you would consider using a concurrent hash map or adding synchronization for thread safety, and discuss how you might handle collisions (e.g., chaining vs. open addressing) to show depth beyond the basics.

1. Clarify Requirements

Ask about expected scale, concurrency needs, persistence, and any additional operations (e.g., TTL). This shows you think about the problem context before diving into implementation.

2. Choose Data Structure

Propose a hash table (e.g., using an array of buckets with linked lists or open addressing) as the primary structure, justifying O(1) average time for set, get, and delete.

3. Implement Operations

Outline the implementation of set (insert or update), get (retrieve value), and delete (remove key-value pair), including handling of collisions and resizing when load factor exceeds threshold.

4. Analyze Complexity

Discuss time and space complexity: average O(1) for operations, worst-case O(n) with poor hash function or many collisions; space O(n). Mention amortized O(1) for resizing.

5. Address Trade-offs and Extensions

Talk about trade-offs: simplicity vs. performance, thread safety (e.g., using locks or concurrent data structures), and possible extensions like persistence or TTL.

Key Points to Mention

Hash table as the core data structure with O(1) average time complexity for set, get, and delete.
Collision resolution techniques: separate chaining vs. open addressing, and their impact on performance.
Dynamic resizing (rehashing) to maintain load factor and amortized O(1) operations.
Thread safety considerations: using synchronized methods, ConcurrentHashMap, or read-write locks.
Trade-offs between different implementations (e.g., simplicity vs. concurrency, memory overhead).
Potential extensions: TTL, persistence, or additional operations like atomic increment.

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