Spent a minute clarifying requirements before touching the keyboard, which I think saved me.
Start by clarifying requirements (e.g., asset class, throughput, latency, order types) and then propose a data structure design that balances performance and simplicity. Walk through the core operations (add, cancel, match) and discuss trade-offs between different implementations (e.g., array vs. tree vs. map). Finally, outline a high-level system architecture that addresses scalability, fault tolerance, and concurrency.
Pro tip: Emphasize the importance of price-time priority and discuss how to handle high-throughput scenarios with lock-free data structures or partitioning, showing awareness of real-world trading systems.
Ask about expected order volume, latency requirements, order types (limit, market, stop), and whether it's for a single asset or multiple assets. This ensures the design meets the actual needs.
Propose using a combination of a hash map for order lookup and two priority queues (or balanced BSTs) for bids and asks to maintain price-time priority. Discuss alternatives like skip lists or arrays for specific scenarios.
Detail algorithms for adding an order, canceling an order, and matching orders. Explain how to efficiently find the best bid/ask and execute trades while maintaining FIFO within price levels.
Discuss partitioning by asset, using multiple threads with careful synchronization, or lock-free approaches. Mention how to handle high throughput and low latency, possibly with in-memory storage and event sourcing.
Explain how to ensure reliability through replication, snapshots, and write-ahead logs. Discuss recovery strategies and consistency guarantees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.