This tripped me up a bit because my first instinct was to think about caching layers before I even got to the actual problem.
Start by clarifying the requirements and constraints, then discuss strategies to handle concurrent requests on shared state, such as optimistic vs pessimistic concurrency control, distributed locks, and partitioning. Emphasize trade-offs between consistency, availability, and performance, and relate to real-world systems like databases or distributed caches.
Pro tip: Mention specific technologies (e.g., Redis Redlock, etcd, ZooKeeper) and their trade-offs, and discuss how you'd handle failure scenarios like lock expiration or network partitions. This shows practical experience and depth.
Ask about consistency requirements, latency tolerance, and scale. Determine if the shared state is a database, cache, or in-memory store, and whether the system is read-heavy or write-heavy.
Discuss optimistic concurrency (e.g., versioning, CAS) and pessimistic concurrency (e.g., locks, transactions). Explain when each is appropriate based on contention levels.
For cross-node coordination, mention distributed locks (e.g., Redis Redlock, etcd, ZooKeeper) or consensus protocols (e.g., Raft, Paxos). Highlight challenges like clock skew and network partitions.
Compare approaches in terms of consistency, availability, performance, and complexity. Discuss alternatives like partitioning state to avoid conflicts or using CRDTs for eventual consistency.
Explain how to handle lock timeouts, retries, idempotency, and deadlocks. Mention monitoring and alerting for contention and failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.