This question is massive and I did not fully appreciate that until about 15 minutes in when I realized I'd only talked about one piece.
Start by clarifying requirements and constraints, then propose a distributed architecture that partitions rules by relevant fields (e.g., employee ID, category) to achieve parallelism. Discuss indexing strategies like inverted indexes or predicate indexing, caching of frequently used rules, and a hot-loading mechanism for rule updates. Finally, address latency targets and trade-offs between consistency and availability.
Pro tip: Emphasize that rule evaluation should be stateless and horizontally scalable, and consider pre-filtering rules using a coarse-grained index to reduce the number of rules evaluated per expense. Also, mention the importance of monitoring and fallback strategies for rule updates to avoid latency spikes.
Ask about rule complexity, update frequency, consistency requirements, and latency SLOs. Confirm that rules are independent and can be evaluated in parallel.
Shard rules by employee ID or category to distribute load. Use a consistent hashing scheme to allow scaling and rebalancing. Each shard evaluates rules locally and returns actions.
Build an inverted index on rule predicates (e.g., amount ranges, categories) to quickly retrieve candidate rules. Cache frequently accessed rules or evaluation results in memory (e.g., Redis) to reduce latency.
Use a publish-subscribe mechanism (e.g., Kafka) to propagate rule changes to all nodes. Ensure atomic updates and versioning to avoid inconsistent evaluations during reloads.
Aim for p99 latency under 100ms per expense. Discuss trade-offs: stronger consistency may increase latency; eventual consistency can be acceptable if rules are not safety-critical. Use asynchronous processing where possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.