This is the kind of question where you can talk for 40 minutes and still feel like you left half of it on the table.
Start by clarifying requirements and constraints, then propose a hybrid architecture that combines local rate limiting with a globally consistent counter store. Discuss trade-offs between accuracy, latency, and availability, and justify your design choices for Plaid's financial-grade reliability.
Pro tip: Emphasize that perfect global accuracy is often unnecessary; propose a tunable trade-off (e.g., allowing small overages) to avoid cross-region latency and single points of failure. Show awareness of Plaid's need for auditability and compliance by including logging and monitoring.
Ask about expected scale (API keys, RPS), latency SLAs, consistency requirements (strict vs eventual), and failure tolerance. Confirm that the limit is per API key globally and that requests can hit any region.
Propose a multi-region deployment with a global rate limiter service. Consider using a globally distributed data store (e.g., Redis with CRDTs, DynamoDB global tables) or a gossip-based approach to share counters.
Choose an algorithm (e.g., sliding window, token bucket) and design the data model for per-key counters. Discuss how to handle atomic increments across regions.
Explain how to balance strong consistency (e.g., using consensus like Raft) against latency and availability. Propose a hybrid: local enforcement with periodic global sync, allowing small overages.
Describe fallback strategies (e.g., fail-open vs fail-closed), handling region failures, and monitoring for abuse. Include logging for audit and compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second because this felt like it came from a completely different interview.
Start by clarifying the system's goals and constraints, such as rate limits, task costs, and business priorities. Then compare the two strategies in terms of throughput, latency, fairness, and resource utilization. Finally, discuss scenarios where each strategy is preferable, emphasizing trade-offs and potential hybrid approaches.
Pro tip: Mention that the choice often depends on whether the system is optimizing for user-perceived latency or overall throughput, and that a hybrid approach like weighted fair queuing can balance both. Also, highlight the importance of monitoring and adapting the strategy based on real-time metrics.
Ask about the rate limiting specifics (e.g., requests per second), task cost distribution, and business SLAs. Understand what 'cost' means (e.g., execution time, resource consumption).
Identify key metrics: throughput (tasks/sec), latency (especially for high-cost tasks), fairness, and resource utilization. Consider both average and tail latencies.
Discuss benefits: reduces latency for expensive tasks, prevents starvation of long tasks, and may improve overall system efficiency if high-cost tasks are bottlenecks. Drawbacks: may reduce total number of tasks completed, increase latency for small tasks, and lead to unfairness.
Discuss benefits: increases throughput and fairness for small tasks, improves user-perceived responsiveness for simple operations. Drawbacks: may starve high-cost tasks, leading to timeouts or SLA violations, and can cause resource underutilization if high-cost tasks are critical.
Suggest when to use each: highest-cost-first for batch processing or when long tasks are critical; maximize-number for interactive systems with many small tasks. Mention hybrid strategies like weighted fair queuing or priority aging to balance both.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.