Started by talking through the approach before writing anything, which I think helped.
Start by clarifying requirements (e.g., rate limit, window size, distributed consistency, persistence needs) and then propose a design using a centralized store like Redis with atomic operations. Implement the core logic with a sliding window or token bucket algorithm, ensuring atomicity via Lua scripts or transactions, and handle resets by storing timestamps or using TTL. Discuss trade-offs between accuracy, latency, and scalability.
Pro tip: Emphasize idempotency and failure modes: what happens if the rate limiter store is unavailable? Propose a fallback strategy (e.g., local rate limiting) to show you think about resilience.
Ask about expected traffic volume, rate limit rules (per user/IP/API key), window size, and consistency requirements. Determine if strict global rate limiting is needed or if eventual consistency is acceptable.
Select a rate limiting algorithm (e.g., sliding window, token bucket) and define the data model in a distributed store like Redis. Consider using sorted sets for sliding window or counters with TTL for fixed window.
Ensure atomicity of check-and-increment operations using Redis Lua scripts or transactions. Discuss partitioning by key to scale horizontally and handle hot keys.
Write pseudocode for request handling: check current count, if under limit increment and allow, else deny. Implement reset by expiring keys or sliding window timestamps. Handle edge cases like clock skew.
Compare centralized vs. distributed rate limiting, latency vs. accuracy, and persistence options (Redis persistence vs. external DB). Mention monitoring, dynamic rule updates, and fallback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
NTP sync plus leaning on Redis as the single source of truth for timestamps.
Start by acknowledging that clock skew is inevitable in distributed systems and can cause rate limiting inaccuracies. Then discuss strategies to mitigate its impact, such as using logical clocks, centralized time services, or designing the rate limiter to be tolerant of small skews. Finally, evaluate trade-offs between accuracy, complexity, and performance.
Pro tip: Emphasize that perfect synchronization is impossible, so the goal is to bound the error and ensure the system degrades gracefully. Mention that you would monitor skew and alert if it exceeds thresholds.
Explain that clock skew can lead to inconsistent rate limiting decisions across servers, potentially allowing more requests than intended or unfairly blocking users.
Discuss options like NTP, PTP, or cloud provider time sync services, and their limitations. Consider using a centralized time service or logical clocks (e.g., Lamport timestamps) for ordering.
Implement algorithms that are robust to small skews, such as sliding window with a grace period, or token bucket with a small buffer. Use a distributed consensus protocol if strict accuracy is needed.
Continuously monitor clock skew across servers and adjust rate limiting parameters dynamically. Set alerts for excessive skew and have fallback mechanisms.
Compare approaches based on accuracy, latency, complexity, and cost. For example, centralized time services add a single point of failure, while logical clocks may not reflect wall-clock time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I was least prepared for.
Start by clarifying the system's requirements and the role of Redis (e.g., cache, session store, rate limiter). Then, describe a fallback strategy that ensures availability, such as using a local in-memory cache with appropriate eviction policies, and discuss trade-offs like consistency and scalability. Finally, mention monitoring and graceful degradation to maintain user experience.
Pro tip: Emphasize that fallback should be automatic and transparent to the user, and that you should design for failure from the start by using patterns like circuit breakers and bulkheads. Also, highlight the importance of testing failure scenarios regularly.
Ask or state what Redis is used for in the system (e.g., caching, session storage, rate limiting) to determine the impact of its failure.
Identify what functionality would be affected and what availability and consistency requirements exist (e.g., can we tolerate stale data?).
Propose using an in-memory cache (e.g., Guava, Caffeine) or local disk-based store as a fallback, with appropriate TTL and eviction policies to prevent memory issues.
Describe how the system detects Redis failure and switches to local storage automatically, using health checks and circuit breakers, and how it recovers when Redis is back.
Acknowledge trade-offs like data inconsistency, increased load on databases, and limited scalability; mention monitoring and alerting to detect and respond to failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.