API Rate Limiter Design

Problem and Scope

Design a rate-limiting service that protects APIs shared by many users and client applications. Product teams should be able to define limits for different identities and endpoints, and callers should receive a clear decision for every request. The limiter sits on a latency-sensitive path, must handle very high traffic, and should behave predictably when instances or regions disagree or become unavailable. Clarify the policy semantics and failure behavior before walking through your design.

Define the identities, endpoints, and time semantics that policies can target. State how precise enforcement must be and what callers should observe when the limiter cannot obtain a fully up-to-date view of usage.

Requirements to Clarify

A strong answer should establish scope before choosing components.

Functional requirements:

• The rate limiter should be able to restrict requests based on various identifiers (IP address, user ID, API key, etc.)
• It should block requests that exceed the defined limits
• It should return appropriate response codes (429 Too Many Requests) when a request is blocked
• It should support different rate limiting algorithms (token bucket, fixed window, sliding window, etc.)
• It should be configurable with different limits for different users/services

Non-functional requirements:

• Low latency: The rate limiter should add minimal overhead to the request processing time
• High availability: The system should be highly available as it sits in the critical path of the request flow
• Accuracy: The rate limiter should accurately track and enforce limits
• Scalability: The system should work in distributed environments with multiple servers
• Consistency: Rate limiting decisions should be consistent across multiple instances

Scale and Capacity

Use the workload to justify storage, partitioning, caching, and reliability decisions. Clarify or challenge these assumptions rather than treating them as unquestionable facts:

• Handle thousands to millions of requests per second
• Support millions of unique users/IPs/API keys
• Provide real-time rate limiting decisions with minimal latency (<10ms)
• Work across multiple data centers in a globally distributed environment

Architecture Discussion

Walk through the important read and write paths, identify ownership boundaries, and explain how the design behaves when dependencies fail. Cover these areas explicitly:

• Rate limiting middleware/service
• Rules engine for defining and managing rate limiting policies
• Distributed cache for storing rate limiting counters/state
• Logging and monitoring components
• Client notification mechanism
• Maintaining consistent rate limiting in a distributed environment
• Minimizing the performance impact on the request flow
• Handling clock synchronization issues across servers
• Managing memory usage for tracking millions of users
• Balancing accuracy with performance
• Preventing users from bypassing rate limits through various techniques

Follow-up Questions

Expect the interviewer to test the consequences of your choices. Practise answering these questions with a concrete decision, its benefit, and its cost:

• How would your rate limiter handle distributed environments with multiple instances?
  • Focus: Assess the candidate's answer to "How would your rate limiter handle distributed environments with multiple instances" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
• What trade-offs would you consider between different rate limiting algorithms?
  • Focus: Assess the candidate's answer to "What trade-offs would you consider between different rate limiting algorithms" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
• How would your design handle clock synchronization issues across servers?
  • Focus: Assess the candidate's answer to "How would your design handle clock synchronization issues across servers" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
• What strategies would you implement to minimize the memory footprint of your rate limiter?
  • Focus: Assess the candidate's answer to "What strategies would you implement to minimize the memory footprint of your rate limiter" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
• How would you ensure the high availability of your rate limiting service?
  • Focus: Assess the candidate's answer to "How would you ensure the high availability of your rate limiting service" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
• What approach would you take to prevent users from circumventing the rate limits?
  • Focus: Assess the candidate's answer to "What approach would you take to prevent users from circumventing the rate limits" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.

Evaluation Rubric

MockMe evaluates the answer across the following dimensions. A complete answer should connect claims to requirements and explain consequential trade-offs.

• Requirements and scope (15%): Clarifies and prioritizes the required behavior for The rate limiter should be able to restrict requests based on various identifiers (IP address, user ID, API key, etc.); It should block requests that exceed the defined limits; It should return appropriate response codes (429 Too Many Requests) when a request is blocked. Establishes the constraints that materially affect API Rate Limiter Design, including Low latency: The rate limiter should add minimal overhead to the request processing time; High availability: The system should be highly available as it sits in the critical path of the request flow. Strong evidence includes Separates the critical path from secondary features and resolves ambiguous requirements before choosing components.
• Architecture and interfaces (20%): Presents coherent ownership boundaries and end-to-end flows covering Rate limiting middleware/service; Rules engine for defining and managing rate limiting policies; Distributed cache for storing rate limiting counters/state; Logging and monitoring components. Strong evidence includes Defines interfaces and traces important success, retry, and failure paths across the proposed components.
• Data and scaling (25%): Uses workload assumptions such as Handle thousands to millions of requests per second; Support millions of unique users/IPs/API keys to justify capacity and partitioning decisions. Explains the data, state, or model strategy for Distributed cache for storing rate limiting counters/state. Strong evidence includes Quantifies a dominant workload, identifies the first bottleneck, and explains how the design evolves as that workload grows.
• Reliability, correctness, and safety (20%): Explains concrete failure behavior, recovery, and operational safeguards for Logging and monitoring components; Maintaining consistent rate limiting in a distributed environment; High availability: The system should be highly available as it sits in the critical path of the request flow. Strong evidence includes States the required correctness or consistency boundary and covers retries, partial failure, observability, and safe degradation.
• Communication and trade-offs (20%): Drives a structured discussion and compares consequential alternatives for Managing memory usage for tracking millions of users; Balancing accuracy with performance; Preventing users from bypassing rate limits through various techniques. Strong evidence includes Makes assumptions explicit, answers the question asked, and explains both the benefit and cost of major decisions.