Apple's "Distributed Rate Limiter" interview question focuses on designing a scalable system to enforce request limits across distributed services, often tagged with data engineering, distributed systems, backend infrastructure, and system design. It typically tests handling high-throughput scenarios like 1M requests/second for 100M users with low latency (<10ms). No exact full problem statement with coding inputs/outputs was found in public sources, as Apple questions are proprietary, but common formulations emphasize token bucket or sliding window algorithms using Redis for atomic counters.[1]
Design a distributed rate limiter that identifies clients (by user ID, IP, or API key) and enforces configurable rules (e.g., 100 requests/minute per user). On excess, return HTTP 429 with headers for remaining quota and reset time. Support per-user, per-IP, global, and endpoint-specific limits in a multi-node setup.[2][1]
X-RateLimit-Remaining, and X-RateLimit-Reset.No verbatim input/output examples (e.g., LeetCode-style functions) appear publicly, but typical pseudocode interface is:
isRequestAllowed(clientId: str, ruleId: str) -> {passes: bool, remaining: int, resetTime: timestamp}
Example Scenario (token bucket, 3 req/window):
Use Redis for sharded counters with INCR + EXPIRE for atomicity, avoiding race conditions in distributed gateways. Handle hot keys via consistent hashing; scale with Redis Cluster.[1]