I jumped straight into token bucket and felt pretty good about it, but then they started asking about distributed state and I realized I'd been designing for a single node the whole time.
Start by clarifying requirements: what to rate limit (per user, IP, API key), limits, and whether distributed. Then propose a high-level design using a centralized store like Redis with algorithms like token bucket or sliding window, and discuss trade-offs and failure modes.
Pro tip: Mention that rate limiting should be applied at the API gateway level for efficiency, but also consider per-service limits for defense in depth. Also, discuss how to handle rate limit exceeded responses with proper HTTP 429 status and Retry-After headers.
Ask about scale, granularity (per user, IP, endpoint), limits (requests per second/minute), and whether the system is distributed. Determine if rate limiting should be global or per-service.
Select a rate limiting algorithm such as token bucket, leaky bucket, fixed window, or sliding window. Discuss pros and cons, e.g., token bucket allows bursts, sliding window is more accurate but complex.
Propose using a centralized data store (e.g., Redis) for distributed rate limiting. Consider using an API gateway for enforcement, and discuss how to handle synchronization and latency.
Discuss trade-offs: accuracy vs performance, centralized vs decentralized, and handling failures (e.g., Redis down). Mention race conditions and how to mitigate (Lua scripts, atomic operations).
Specify response for rate-limited requests (HTTP 429, Retry-After header). Include monitoring and alerting for rate limit breaches and system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.