Start by clarifying requirements (e.g., data volume, latency, consistency) and then propose a layered design: in-memory data structures for fast access, persistence for durability, and scaling strategies for many tickers. Walk through trade-offs for each component, emphasizing how to handle out-of-order inserts and read/write patterns.
Pro tip: Demonstrate awareness of real-world financial data characteristics: timestamps may be out of order, and reads often dominate (e.g., for analytics), so consider write-optimized structures like LSM trees for persistence and read-optimized in-memory indexes.
Ask about expected data volume, read/write ratio, latency requirements, consistency needs, and whether timestamps are unique per ticker. This shapes the entire design.
Propose a per-ticker sorted structure (e.g., balanced BST, skip list, or sorted array with binary search) to support efficient insert and query. Discuss handling out-of-order inserts by maintaining sorted order.
Choose a storage engine (e.g., LSM tree, B-tree) that supports efficient writes and range queries. Consider write-ahead logging for durability and periodic snapshots for recovery.
Shard data by ticker across nodes to distribute load. Use consistent hashing for rebalancing and replication for fault tolerance. Discuss caching hot tickers.
Compare write-optimized (LSM) vs. read-optimized (B-tree) storage. For in-memory, consider read-heavy workloads with caching and write-heavy with buffering. Discuss latency vs. throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.