The multi-key part tripped me up more than the sliding window logic.
Start by clarifying requirements: single-node vs distributed, exact vs approximate counts, and read/write patterns. Then propose a design using a sliding window with per-key timestamp buckets (e.g., per-second granularity) and discuss trade-offs between memory, accuracy, and concurrency. Finally, extend to distributed systems using sharding and aggregation.
Pro tip: Mention that for high-throughput systems, approximate counting with probabilistic data structures (like count-min sketch) or sampling can be acceptable, but always confirm with the interviewer. Also, highlight the importance of handling clock skew and idempotency in distributed settings.
Ask about scale (QPS, number of keys), accuracy needs, latency requirements, and whether the system is distributed. Confirm if the 5-minute window is sliding or fixed.
Propose a per-key sliding window using a circular buffer of timestamps or a map of timestamp buckets (e.g., per-second counts). Discuss memory vs accuracy trade-offs.
Address thread safety with locks or lock-free structures, and consider read/write contention. For distributed, discuss sharding by key and using a shared store like Redis with TTL.
Discuss eviction of old buckets, memory management, and potential use of approximate algorithms (e.g., count-min sketch) if exact counts aren't required.
Recap the design, highlight trade-offs (memory, accuracy, complexity), and mention monitoring and failure handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part felt more natural once the counter was done.
Start by clarifying the requirements: what exactly is being rate limited (e.g., requests per user/IP), the desired limit, and the time window. Then, leverage the existing hit counter to track counts per key, and implement a rate limiting algorithm such as fixed window or sliding window, discussing trade-offs. Finally, outline how to enforce the limit and handle edge cases like distributed environments.
Pro tip: Demonstrate awareness of distributed systems challenges: even a simple rate limiter needs to handle multiple servers, so mention using a centralized store like Redis and atomic operations to avoid race conditions.
Ask questions to understand the scope: what is the rate limit (e.g., 100 requests per minute), what key is used (user ID, IP), and whether it's per endpoint or global. Also confirm if the system is distributed.
Select a rate limiting algorithm (e.g., fixed window, sliding window, token bucket) based on trade-offs between accuracy, memory, and complexity. Explain why you chose it.
Use the existing hit counter to increment counts per key and time window. If the counter is in-memory, discuss how to adapt it for distributed use (e.g., Redis with TTL).
Describe how to check the count against the limit and reject or allow requests. Include how to handle the response (e.g., HTTP 429) and possibly include headers like Retry-After.
Discuss race conditions, atomicity, clock skew, and scalability. Mention how to handle bursts and whether to use a sliding window for smoother limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.