Design a High-Throughput Distributed Rate Limiting Service
Problem Context
Design a multi-tenant rate limiting platform intended for deployment at an edge or gateway layer to protect downstream services. The system must enforce both per-user and global rate limits, tolerate bursts, and provide approximately sliding-window semantics across multiple geographical regions. The platform needs to handle a peak throughput of 10 million requests per second (RPS) with low latency.
Requirements
Your design should cover the following aspects:
- Public API and Integration: Define the public API for checking, configuring, and introspecting rate limits, as well as how enforcement integrates into the request path.
- Rate Limiting Algorithms: Discuss suitable algorithms (e.g., Token Bucket, Leaky Bucket, Sliding Window) and justify your choices based on the requirements.
- Data Model: Propose a data model for storing counters, buckets, and rate limiting policies.
- Sharding and Hot-Key Mitigation: Detail strategies for sharding data and mitigating the impact of hot keys (e.g., consistent hashing, key splitting).
- Storage and Replication: Evaluate different storage choices (e.g., in-memory, Redis, custom solutions), discuss replication strategies, and address time coordination across distributed components.
- Failure Handling and Consistency: Describe how the system handles failures and partial outages. Address fairness and the required consistency levels (eventual vs. strong) for different aspects of the rate limiting service.
- Scalability and Operations: Outline a scale-out plan, including capacity planning formulas, an estimate of machines needed for a 2x traffic spike, autoscaling signals, and backpressure/throttling strategies.
AI interview prep
Core dilemma
Balancing extreme scale (10M RPS, multi-region, low latency) with the need for accurate, consistent, and fair rate limiting, especially with approximate sliding windows and burst tolerance. This fundamentally boils down to consistency vs. availability/performance in a distributed, high-throughput environment.
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 High-Throughput Distributed Rate Limiting Service
Problem Context
Design a multi-tenant rate limiting platform intended for deployment at an edge or gateway layer to protect downstream services. The system must enforce both per-user and global rate limits, tolerate bursts, and provide approximately sliding-window semantics across multiple geographical regions. The platform needs to handle a peak throughput of 10 million requests per second (RPS) with low latency.
Requirements
Your design should cover the following aspects:
- Public API and Integration: Define the public API for checking, configuring, and introspecting rate limits, as well as how enforcement integrates into the request path.
- Rate Limiting Algorithms: Discuss suitable algorithms (e.g., Token Bucket, Leaky Bucket, Sliding Window) and justify your choices based on the requirements.
- Data Model: Propose a data model for storing counters, buckets, and rate limiting policies.
- Sharding and Hot-Key Mitigation: Detail strategies for sharding data and mitigating the impact of hot keys (e.g., consistent hashing, key splitting).
- Storage and Replication: Evaluate different storage choices (e.g., in-memory, Redis, custom solutions), discuss replication strategies, and address time coordination across distributed components.
- Failure Handling and Consistency: Describe how the system handles failures and partial outages. Address fairness and the required consistency levels (eventual vs. strong) for different aspects of the rate limiting service.
- Scalability and Operations: Outline a scale-out plan, including capacity planning formulas, an estimate of machines needed for a 2x traffic spike, autoscaling signals, and backpressure/throttling strategies.