Design a Rate Limiter Service
Design a robust rate limiting service to protect an API from abuse and ensure fair usage across its consumers. This service will operate at the API gateway or edge layer.
Requirements
- Limit Enforcement: The service must enforce various types of limits, including:
- Per-user (or per API key) requests per time window (e.g., 100 requests/minute).
- Per-IP limits (optional, but consider its implications).
- Potentially per-endpoint or per-tenant limits.
- Policy Support: Support common rate limiting algorithms. Candidates should be prepared to discuss the pros and cons of:
- Fixed Window (and its edge effects).
- Sliding Window (log or counter).
- Token Bucket.
- Leaky Bucket.
- Distributed Environment: The solution must function correctly and efficiently in a distributed system with multiple stateless API servers.
- Performance: The rate limiting mechanism must introduce low latency overhead to API requests.
Clarifications to Address
During the design discussion, be prepared to address:
- The specific behavior when a request exceeds its limit (e.g., HTTP 429 status code, inclusion of
Retry-Afterheaders). - How the chosen design handles sudden bursts of traffic effectively.
- The required consistency model for the rate limiting counts (e.g., strict accuracy versus near-accurate eventual consistency).
- Strategies to avoid a single point of failure within the rate limiting service.
Deliverables
Your explanation should cover:
- The chosen rate limiting algorithm(s) and the rationale behind your selection.
- The high-level architecture of the service, including the data stores used.
- A detailed explanation of the key data model and the read/write flow for a request.
- How the system scales, handles fault tolerance, and addresses various edge cases.
AI interview prep
Core dilemma
Balancing the need for accurate, real-time rate limiting across a distributed system with the imperative for low latency and high availability, while managing the complexity of different limiting policies and burst handling.
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 Rate Limiter Service
Design a robust rate limiting service to protect an API from abuse and ensure fair usage across its consumers. This service will operate at the API gateway or edge layer.
Requirements
- Limit Enforcement: The service must enforce various types of limits, including:
- Per-user (or per API key) requests per time window (e.g., 100 requests/minute).
- Per-IP limits (optional, but consider its implications).
- Potentially per-endpoint or per-tenant limits.
- Policy Support: Support common rate limiting algorithms. Candidates should be prepared to discuss the pros and cons of:
- Fixed Window (and its edge effects).
- Sliding Window (log or counter).
- Token Bucket.
- Leaky Bucket.
- Distributed Environment: The solution must function correctly and efficiently in a distributed system with multiple stateless API servers.
- Performance: The rate limiting mechanism must introduce low latency overhead to API requests.
Clarifications to Address
During the design discussion, be prepared to address:
- The specific behavior when a request exceeds its limit (e.g., HTTP 429 status code, inclusion of
Retry-Afterheaders). - How the chosen design handles sudden bursts of traffic effectively.
- The required consistency model for the rate limiting counts (e.g., strict accuracy versus near-accurate eventual consistency).
- Strategies to avoid a single point of failure within the rate limiting service.
Deliverables
Your explanation should cover:
- The chosen rate limiting algorithm(s) and the rationale behind your selection.
- The high-level architecture of the service, including the data stores used.
- A detailed explanation of the key data model and the read/write flow for a request.
- How the system scales, handles fault tolerance, and addresses various edge cases.