I went straight to a hashmap keyed by security ID and accumulated volume as trade events came in.
Clarify the requirements first: are queries point-in-time or real-time, and what are the latency and throughput needs? Then propose a design that separates ingestion from querying, using an efficient data structure like a Fenwick tree or balanced BST for cumulative volume, and discuss trade-offs between in-memory and persistent storage.
Pro tip: Mention that trade events are append-only and often out-of-order, so you need to handle late data—perhaps with a watermark or by allowing queries on a consistent snapshot. Also, consider that volume-check queries might be for a time range, so a prefix-sum structure is ideal.
Ask about query patterns (point-in-time vs. range), latency SLAs, event ordering guarantees, and whether the system must handle multiple securities.
For cumulative volume queries, use a Fenwick tree (BIT) or segment tree for O(log n) updates and queries. If queries are only for the latest volume, a simple running sum suffices.
Process events as they arrive, updating the data structure. Handle out-of-order events by buffering or using a time-based index. Consider partitioning by security for scalability.
Decide whether to keep the data structure in memory or persist to disk. Discuss write-ahead logging or snapshots for fault tolerance.
If needed, shard by security ID, use concurrent data structures, or batch updates. Discuss trade-offs between consistency and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.