← Bloomberg Interview Insights
Went with a heap plus a hashmap for tracking current prices, and used lazy deletion to handle stale entries in the heap.
Start by clarifying requirements: update frequency, latency needs, and whether K is fixed or varies. Then propose a data structure that supports O(1) or O(log n) updates and efficient top-K retrieval, such as a hash map combined with a balanced BST or a heap with lazy deletion. Discuss trade-offs between different approaches and consider concurrency and scalability for a real-time system.
Pro tip: Mention that you would use a hash map for O(1) price updates and a balanced BST (or skip list) for maintaining sorted order, enabling O(log n) updates and O(K) top-K retrieval. This shows you understand the need for both fast writes and reads, which is crucial for financial systems.
Ask about update rate, number of stocks, typical K, latency requirements, and whether the top-K query is frequent. This ensures the design meets actual needs.
Propose a hash map for O(1) price updates and a balanced BST (e.g., red-black tree) or skip list for maintaining sorted prices. Alternatively, use a max-heap with lazy deletion for top-K.
Detail the time complexity: update O(log n) with BST, top-K O(K) by traversing the tree in reverse order. Compare with heap approach: update O(log n), top-K O(K log n) or O(n log K) if rebuilding.
Discuss locking or lock-free approaches for concurrent updates and queries. Consider sharding by stock symbol if the number of stocks is large.
Compare with alternative designs like using a database with indexes, or a time-series database. Mention how to handle ties, stale data, and dynamic K.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.