I started with token bucket because it felt safer, explained the refill logic, talked about using a deque for sliding window.
Start by clarifying requirements: QPS limit, burst allowance, distributed vs single-node, and consistency needs. Then compare token bucket and sliding window on data structures, complexity, and concurrency, and recommend one based on trade-offs. Finally, discuss implementation details like atomic operations and synchronization.
Pro tip: Emphasize that token bucket naturally supports bursts while sliding window provides precise rate limiting; mention that in distributed systems, you'd likely use Redis with Lua scripts for atomicity, and consider the trade-off between accuracy and performance.
Ask about scale (single node vs distributed), burst allowance specifics, and consistency requirements. This shows you understand the problem context before diving into solutions.
Explain the token bucket algorithm: a bucket with capacity B, refilled at rate R tokens per second. Each request consumes a token; if none available, request is denied. This allows bursts up to B.
Explain sliding window: maintain a window of the last N seconds and count requests. Use a circular buffer or timestamp queue to expire old requests. This provides precise rate limiting but no burst allowance unless combined with other techniques.
Token bucket: O(1) time and space per bucket (just tokens and last refill time). Sliding window: O(1) time per request but O(N) space for timestamps, where N is max requests in window. Discuss trade-offs.
Discuss atomic operations (e.g., compare-and-swap, locks, or Redis Lua scripts) to ensure correctness under concurrent access. Mention that token bucket can be implemented with atomic updates to token count and timestamp.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that a distributed rate limiter requires a shared, low-latency store like Redis or a dedicated service. Then systematically address each concern: use atomic operations (Lua scripts or Redis transactions) for coordination and atomicity, logical clocks or server-side timestamps for clock skew, idempotency keys for safe retries, and design for failure with replication, quorum, and graceful degradation. Finally, discuss trade-offs between consistency, availability, and performance.
Pro tip: Emphasize that the rate limiter should fail open or closed based on business impact, and that using a centralized store introduces a single point of failure—so consider a hybrid approach with local fallback and eventual consistency.
Ask about scale (QPS, number of instances), consistency needs (strict vs eventual), and tolerance for latency and failures. This shapes the design choices.
Decide between a centralized store (e.g., Redis, DynamoDB) with atomic operations, or a distributed consensus system (e.g., etcd). Discuss trade-offs of each.
Use server-side timestamps or logical clocks to avoid skew issues. Ensure atomicity via Lua scripts, transactions, or compare-and-swap operations.
Implement idempotency keys to deduplicate requests. Design for node loss with replication and quorum, and handle partial updates with retries and compensating actions.
Weigh consistency vs availability (CAP), latency vs accuracy, and propose mitigations like local caching, sliding windows, and graceful degradation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a cooldown question after the hard distributed stuff, but I rambled.
Structure your answer around three pillars: the control plane (APIs and configuration), the data plane (runtime behavior), and observability (monitoring/alerting). Emphasize how these work together to detect and mitigate saturation, and tie your choices to Google-scale reliability principles like SLOs and graceful degradation.
Pro tip: Frame saturation detection in terms of SLOs and error budgets, and mention that you'd expose both real-time and historical metrics to support capacity planning and incident response. This shows you think like a Google engineer who balances reliability with velocity.
Outline the core APIs for managing rate limits: CRUD for policies, real-time quota checks, and usage reporting. Include both synchronous (e.g., CheckRateLimit) and asynchronous (e.g., batch updates) endpoints.
Describe configurable parameters such as rate limits (per client, per endpoint), burst allowances, window sizes, and override rules. Mention dynamic configuration via a control plane with versioning and audit logs.
List key metrics: request rate, allowed/denied counts, latency percentiles, queue depth, and saturation indicators like CPU/memory of limiter instances. Emphasize per-client and per-tenant breakdowns.
Propose alerts based on thresholds and anomalies: e.g., deny rate spike, latency SLO burn, or resource saturation. Include multi-window burn-rate alerts and integration with incident management.
Explain how to detect saturation (e.g., high deny rate, queue buildup, resource exhaustion) and the automated responses (e.g., adaptive throttling, circuit breakers, scaling). Tie back to SLOs and error budgets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.