Design a multi-level API rate limiter
Scenario
You are tasked with designing a backend for an "insight platform" that exposes HTTP APIs. These APIs are consumed by numerous tenants (via API keys) and end-consumers (identified by user/device/client IDs).
Your primary objective is to implement a multi-level API rate-limiting layer that enforces two distinct, concurrent limits:
- API-level limit: A maximum of 100 requests per second per API key (representing a tenant or application).
- Consumer-level limit: A maximum of 10 requests per second per consumer (representing an end user, device, or client ID).
A request must be allowed only if it satisfies both of these limits simultaneously.
Requirements
- Enforcement Point: The rate limiter must operate in front of multiple stateless API servers, acting as a gateway or middleware component.
- Correctness Target: The system should achieve practical accuracy under high concurrency, preventing traffic from exceeding the defined limits by significant margins.
- Latency: The rate-limiting layer should introduce minimal overhead, ideally within single-digit milliseconds for typical operations.
- Scale: The design must accommodate a large number of unique API keys and consumers, and be capable of handling substantial traffic spikes.
- Operability: The system needs to provide robust metrics and logs detailing throttling decisions.
- Behavior: When a request is denied due to exceeding a limit, the system must return an HTTP 429 Too Many Requests status code, accompanied by a helpful response (e.g., including a
Retry-Afterheader).
Deliverables
- Select and justify a suitable rate-limiting algorithm (e.g., Token Bucket, Leaky Bucket, Sliding Window, Fixed Window).
- Propose a distributed design that effectively integrates with multiple API servers, considering single-node versus multi-node approaches.
- Detail how both API-level and consumer-level limits will be enforced atomically, or explain acceptable approximations if strict atomicity is not feasible.
- Discuss the data model, keying strategies, expiration policies, and potential failure modes of your rate-limiting solution.
AI interview prep
Core dilemma
Balancing the strict enforcement of two distinct, concurrent rate limits across a distributed system with high throughput and low latency requirements, while managing state and ensuring operational resilience.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a multi-level API rate limiter
Scenario
You are tasked with designing a backend for an "insight platform" that exposes HTTP APIs. These APIs are consumed by numerous tenants (via API keys) and end-consumers (identified by user/device/client IDs).
Your primary objective is to implement a multi-level API rate-limiting layer that enforces two distinct, concurrent limits:
- API-level limit: A maximum of 100 requests per second per API key (representing a tenant or application).
- Consumer-level limit: A maximum of 10 requests per second per consumer (representing an end user, device, or client ID).
A request must be allowed only if it satisfies both of these limits simultaneously.
Requirements
- Enforcement Point: The rate limiter must operate in front of multiple stateless API servers, acting as a gateway or middleware component.
- Correctness Target: The system should achieve practical accuracy under high concurrency, preventing traffic from exceeding the defined limits by significant margins.
- Latency: The rate-limiting layer should introduce minimal overhead, ideally within single-digit milliseconds for typical operations.
- Scale: The design must accommodate a large number of unique API keys and consumers, and be capable of handling substantial traffic spikes.
- Operability: The system needs to provide robust metrics and logs detailing throttling decisions.
- Behavior: When a request is denied due to exceeding a limit, the system must return an HTTP 429 Too Many Requests status code, accompanied by a helpful response (e.g., including a
Retry-Afterheader).
Deliverables
- Select and justify a suitable rate-limiting algorithm (e.g., Token Bucket, Leaky Bucket, Sliding Window, Fixed Window).
- Propose a distributed design that effectively integrates with multiple API servers, considering single-node versus multi-node approaches.
- Detail how both API-level and consumer-level limits will be enforced atomically, or explain acceptable approximations if strict atomicity is not feasible.
- Discuss the data model, keying strategies, expiration policies, and potential failure modes of your rate-limiting solution.