This is the kind of question that sounds scoped until you realize how many sub-problems are hiding inside it.
Start by clarifying requirements (e.g., accuracy, latency, scale) and then propose a distributed rate limiter using a token bucket or sliding window algorithm, with per-user quotas stored in a fast, scalable data store like Redis. Walk through the request flow, data model, and scaling strategies, emphasizing trade-offs between consistency, availability, and performance.
Pro tip: Mention that you would use a centralized store like Redis with Lua scripts for atomic operations, but also discuss sharding and local caching to handle high throughput and reduce latency. Highlight the importance of monitoring and dynamic quota updates.
Ask about scale (QPS, number of users), latency requirements, accuracy needs, and whether quotas can be updated dynamically. This shapes your design choices.
Select an algorithm like token bucket or sliding window log/counter, explaining why it fits the use case (e.g., token bucket allows bursts, sliding window is more accurate).
Define how per-user quotas and live counters are stored. Use a fast, distributed store like Redis with appropriate data structures (e.g., hash for token bucket state) and consider sharding by user ID.
Outline the steps: identify user, fetch quota and current counter, atomically check and update counter, allow or deny request, and possibly return rate limit headers.
Discuss horizontal scaling of the rate limiter service, sharding the data store, caching, and trade-offs between consistency (e.g., eventual vs strong) and latency/availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely didn't have a clean answer ready.
Start by clarifying the requirements: strict global quota enforcement with minimal cross-region latency. Propose a hybrid architecture that combines local enforcement with asynchronous global reconciliation, using techniques like regional quota slices, eventual consistency, and a global coordinator for rebalancing. Emphasize trade-offs between consistency, latency, and complexity.
Pro tip: Acknowledge that perfect global consistency with low latency is impossible due to CAP theorem; instead, aim for 'eventual global enforcement' with local fast-path checks and a background reconciliation loop that corrects overshoots. This shows you understand real-world distributed systems constraints.
Ask about the required consistency level (strict vs eventual), acceptable overshoot, and latency targets. Confirm that cross-region latency must be avoided on the hot path.
Each region enforces a local quota slice using a fast in-memory store (e.g., Redis) to avoid cross-region calls. This provides low-latency checks but may allow temporary overshoot.
Use a global quota manager (e.g., a centralized service or a distributed consensus system) to periodically rebalance quota slices based on regional usage. Asynchronously propagate updates to regional caches.
Define a reconciliation strategy: if a region exhausts its slice, it can request more from the global pool (with some latency) or temporarily borrow from other regions. Implement a fallback to deny requests if global quota is truly exhausted.
Compare with strict global locking (high latency) and fully local quotas (no global enforcement). Highlight the chosen approach's balance of latency, consistency, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket handles this naturally with capacity vs refill rate as separate parameters.
Clarify that the core algorithm (e.g., token bucket) remains unchanged, and introduce a burst allowance layer that permits temporary higher rates. Explain how to track burst usage separately, enforce limits, and refill burst credits over time.
Pro tip: Emphasize that burst capacity should be replenished gradually to prevent abuse, and consider using a separate token bucket for burst credits with a higher rate but smaller capacity.
Confirm the sustained rate (100 TPS), burst rate (1000 TPS), and burst duration (e.g., 1 second). Ask about the desired behavior when burst is exhausted.
Propose a secondary token bucket (burst bucket) with capacity equal to the burst allowance (e.g., 900 extra tokens) and a refill rate matching the sustained rate (100 TPS).
Keep the original token bucket for sustained rate. On each request, first check the burst bucket; if tokens available, consume from it; otherwise, fall back to the sustained bucket.
Refill burst bucket at the sustained rate (100 TPS) up to its capacity. Ensure that burst tokens are only used when the sustained bucket is empty or to allow spikes above sustained rate.
Mention that this approach adds minimal overhead and preserves the original algorithm. Alternatively, use a sliding window or leaky bucket with burst parameter, but highlight simplicity of dual bucket.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Walk through the end-to-end flow of a quota upgrade: how the new limit is persisted, propagated to enforcement points, and applied to the user's existing token bucket. Emphasize the need for atomicity, consistency, and minimal disruption to in-flight requests.
Pro tip: Mention that you would use a lazy update strategy: store the new quota and a version/timestamp, and have the rate limiter refresh its state on the next request, avoiding a global push. This reduces complexity and latency.
Describe how the new quota (10,000 TPS) is written to a central configuration store (e.g., database, config service) and how that change is propagated to all rate limiter instances (e.g., via pub/sub, polling, or cache invalidation).
Explain how the existing token bucket (currently at 100 TPS capacity) is updated: either by scaling the bucket capacity and refill rate proportionally, or by resetting the bucket to the new capacity. Discuss trade-offs (e.g., burst allowance vs. fairness).
Detail when the change takes effect: immediately upon propagation, or at the next refill interval. Ensure the update is atomic to avoid race conditions where some requests see the old limit and others see the new limit.
Address what happens to requests currently being processed: they should continue under the old quota until completion, while new requests use the new quota. This avoids mid-request throttling.
Mention the need to monitor the upgrade for errors and have a rollback plan if issues arise (e.g., revert to old quota).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: evaluate cheapest or most selective check first to fail fast.
Start by clarifying the requirements and constraints, then propose a layered rate-limiting architecture where each dimension is evaluated in a deliberate order (e.g., cheapest/most restrictive first). Explain how to combine limits using a composite key or hierarchical checks, and discuss trade-offs like latency, accuracy, and scalability.
Pro tip: Mention that you would evaluate the most restrictive or cheapest limit first to fail fast and reduce load on downstream checks, and use a sliding window or token bucket algorithm with a distributed store like Redis for atomic operations.
Ask about scale, latency tolerance, accuracy needs, and whether limits are hard or soft. This shapes the choice of algorithm and storage.
Propose using a composite key (e.g., user:IP:endpoint) or separate counters per dimension, stored in a fast, distributed data store like Redis with atomic operations.
Evaluate dimensions in order of cost and restrictiveness: typically per-user first (most specific), then per-IP, then per-endpoint, or vice versa based on business rules. Fail fast on the first exceeded limit.
Discuss trade-offs: strict ordering may cause false positives; combining limits can be complex. Address distributed consistency, race conditions, and graceful degradation.
Recap the approach, emphasizing scalability, performance, and alignment with business goals. Mention monitoring and dynamic adjustment of limits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.