This is the kind of question where you think you know it and then the follow-ups make you realize you only knew half of it.
Start by clarifying requirements and constraints (e.g., single-node vs distributed, memory limits, precision of sliding window). Then propose a sliding window log or counter approach, analyze time/space complexity, and discuss trade-offs and scalability.
Pro tip: Mention that a true sliding window can be approximated with a fixed window counter or a sliding window counter to save memory, and discuss how to handle distributed rate limiting using a centralized store like Redis with atomic operations.
Ask about scale (number of users, QPS), memory constraints, whether the system is distributed, and the required accuracy of the rate limiting.
Propose a sliding window log using a deque or a sorted set per user, or a sliding window counter using two fixed windows. Explain how to enforce the limit.
Compare time and space complexity of different approaches (e.g., log vs counter), and discuss precision vs memory usage.
Discuss using a centralized store (e.g., Redis) with atomic operations, sharding, and handling race conditions.
Cover timestamp precision, clock skew, cleanup of old entries, and potential optimizations like approximate counting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: multiple users with different limits and per-endpoint rules. Then propose a design that separates identity (user) and resource (endpoint) dimensions, using a composite key and a rules engine to resolve the applicable limit. Discuss trade-offs between simplicity and flexibility, and how to handle distributed rate limiting at scale.
Pro tip: At Uber, rate limiting is often about protecting shared resources while allowing critical traffic; mention how you'd handle priority users or endpoints (e.g., driver app vs. rider app) and the need for real-time configuration updates without redeploying.
Ask about the expected scale (number of users, endpoints, QPS), whether limits are static or dynamic, and if there are different tiers of users (e.g., free vs. premium). Also confirm if the rate limiter is distributed or single-node.
Propose a composite key that combines user ID and endpoint (e.g., 'user:123:endpoint:/api/rides'). This allows independent tracking of each user-endpoint pair. Discuss how to handle global user limits and global endpoint limits as separate keys.
Describe a configuration system that maps (user, endpoint) to a limit. This could be a hierarchical rule set: default global limit, overridden by endpoint-specific limits, overridden by user-specific limits. Mention using a fast lookup store like Redis or an in-memory cache with pub/sub for updates.
Select an algorithm that supports multiple limits efficiently, such as sliding window or token bucket. Explain how to apply multiple limits (e.g., per-user and per-endpoint) by checking all applicable buckets and taking the most restrictive.
Discuss how to synchronize counters across nodes (e.g., using Redis with Lua scripts for atomicity). Trade-offs: accuracy vs. latency, centralized vs. decentralized, and how to handle failures (e.g., fail-open vs. fail-closed).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that a single-instance rate limiter (e.g., in-memory) fails in a distributed setup due to inconsistent state. Then propose a centralized store like Redis with atomic operations, and discuss trade-offs between accuracy, latency, and availability. Finally, mention alternatives like sticky sessions or distributed counters with eventual consistency, and how to handle failures.
Pro tip: Emphasize that the choice depends on the required strictness: for strict global limits, use Redis with Lua scripts; for approximate limits, consider a decentralized approach with gossip or local limits plus periodic sync. Also, mention monitoring and dynamic adjustment of limits.
Explain why a single-instance rate limiter doesn't work: each instance has its own counter, leading to inconsistent enforcement and potential overload. The load balancer distributes requests, so a global view is needed.
Propose using a fast, shared data store like Redis or Memcached. Redis is preferred for its atomic operations (INCR, EXPIRE) and Lua scripting for complex logic.
Use Redis Lua scripts to atomically check and increment counters, ensuring race conditions are avoided. For sliding window, use sorted sets with timestamps.
Discuss what happens if Redis is down: fallback to local rate limiting (fail-open or fail-closed), or use a distributed cache with replication. Also, consider latency added by network calls.
Mention sticky sessions (if acceptable), or a hybrid approach: local rate limiting with periodic sync to a central store for approximate global limits. Also, discuss sharding the rate limiter by user ID to scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.