I went with a plain hashmap first, which felt obvious, and it was.
Start by clarifying requirements (e.g., expected operations, concurrency, persistence) and then propose a hash table as the primary data structure, explaining its O(1) average-case time complexity. Walk through the implementation of put, get, and delete, and then discuss an alternative like a balanced binary search tree (e.g., red-black tree) with O(log n) operations, highlighting trade-offs in ordering, memory, and worst-case performance.
Pro tip: Mention that in real-world systems like Lyft, such a store might need to handle concurrent access, so you'd consider thread-safety mechanisms (e.g., locks or concurrent data structures) and discuss how that impacts the design. This shows you think beyond the basic algorithm.
Ask about expected operations, data size, concurrency needs, and persistence requirements to tailor your solution.
Choose a hash table for O(1) average-case put, get, and delete, and explain how you'd handle collisions (e.g., chaining or open addressing).
Walk through the code or pseudocode for put, get, and delete, covering edge cases like updating existing keys and handling missing keys.
Present a balanced BST (e.g., red-black tree) as an alternative, noting O(log n) operations but added benefits like ordered iteration and predictable worst-case performance.
Compare the two approaches in terms of time complexity, memory usage, ordering, concurrency, and suitability for different scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.