The two-limiter part is what trips you up.
Start by clarifying requirements and assumptions, then outline the architecture with two rate limiters and a queue between them. Choose a rate limiting algorithm (e.g., token bucket) and justify it based on burst handling and simplicity. Implement the solution with code and tests, and discuss trade-offs like latency, throughput, and complexity.
Pro tip: Demonstrate awareness of back-pressure by explaining how the queue size and rejection strategy prevent system overload, and mention that you would monitor queue depth and adjust limits dynamically.
Ask questions to confirm expected traffic patterns, burst tolerance, latency requirements, and failure modes. State assumptions explicitly to guide design decisions.
Sketch the flow: inbound requests -> first limiter (100 rps) -> queue -> second limiter (10 rps) -> downstream. Select a rate limiting algorithm (e.g., token bucket) and justify why it fits (e.g., allows bursts, simple to implement).
Write clean, modular code for both limiters and the queue, ensuring thread-safety if needed. Include unit tests for rate limiting behavior, queue overflow, and back-pressure scenarios.
Analyze trade-offs: latency introduced by queuing, memory usage, fairness, and potential for dropped requests. Discuss how to handle bursts, queue full conditions, and downstream failures.
Conclude with a summary of the solution and mention possible enhancements like distributed rate limiting, dynamic adjustment, or monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.