Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates concerns like order management, matching engine, and data storage. Focus on scalability, consistency, and fault tolerance, and discuss trade-offs between different technologies and approaches.
Pro tip: Emphasize the importance of idempotency and exactly-once processing in order execution to prevent duplicate trades, and discuss how to handle market data bursts during high volatility.
Ask questions to understand the scope: user base size, expected throughput, latency requirements, consistency needs, and regulatory constraints. Define core features like order placement, portfolio tracking, and market data.
Sketch the main components: API gateway, user service, order service, matching engine, market data service, and databases. Explain how they interact and the data flow.
Detail the order matching engine, focusing on data structures (e.g., order books) and algorithms. Discuss how to ensure low latency and high throughput, and how to handle concurrency.
Choose appropriate databases for different needs: relational for transactions, time-series for market data, and caching for hot data. Explain schema design and indexing strategies.
Discuss partitioning, replication, and failover strategies. Address consistency vs. availability trade-offs, and how to monitor and scale the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.