Pretty much a hash map question at its core, but they wanted me to actually talk through why, not just write the code.
Start by clarifying requirements (e.g., thread-safety, persistence, key/value types) and then propose a hash table as the core data structure. Explain how it achieves average O(1) for insert, get, and delete, and discuss collision handling (e.g., chaining or open addressing). Finally, analyze time and space complexity, and mention potential optimizations or trade-offs.
Pro tip: Demonstrate awareness of real-world constraints by discussing how you would handle collisions, resizing, and concurrency, and relate it to ML systems where fast key-value lookups are critical (e.g., feature stores, embedding caches).
Ask about expected key/value types, thread-safety, persistence, and performance guarantees. This shows you consider the context before diving into design.
Propose a hash table with separate chaining or open addressing. Explain why it provides average O(1) for insert, get, and delete.
Describe how insert, get, and delete work, including collision resolution and resizing (rehashing) when load factor exceeds a threshold.
State that average time complexity is O(1) per operation, worst-case O(n) due to collisions, and space complexity is O(n). Mention that resizing keeps operations amortized O(1).
Mention alternatives like balanced BSTs (O(log n) worst-case) and trade-offs. Discuss concurrency (e.g., locks, lock-free) and memory overhead.
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