Design a Simple API Throttling Mechanism
Implement basic throttling at the service level (not distributed). Discuss simple counting window and fixed-window rate limiting algorithms.
Why Interviewers Ask This
Interviewers at IBM ask this to assess your ability to balance system reliability with resource protection. They evaluate whether you understand the trade-offs between implementation simplicity and accuracy in rate limiting, specifically testing your grasp of concurrency issues and data consistency when managing request counts without distributed coordination.
How to Answer This Question
1. Clarify Requirements: Immediately confirm that the solution is service-level only, not distributed, and define the specific constraints like time window size and max requests allowed per user or IP. 2. Propose Algorithms: Outline two distinct approaches. First, explain the Fixed-Window algorithm using a simple counter reset every N seconds, noting its potential for double-spiking at boundaries. Second, introduce the Sliding Window Log or Counter approach to mitigate boundary spikes while maintaining simplicity. 3. Address Concurrency: Discuss how to handle race conditions in a multi-threaded environment, suggesting atomic operations or locks to ensure accurate counting. 4. Define API Behavior: Specify exactly what happens when the limit is hit (e.g., returning HTTP 429) and include necessary headers like Retry-After. 5. Evaluate Trade-offs: Conclude by comparing memory usage and complexity versus accuracy, highlighting why one might be preferred over the other for an internal IBM service.
Key Points to Cover
- Explicitly distinguishing between fixed-window and sliding-window limitations
- Addressing race conditions using atomic operations or locks
- Defining clear error responses with appropriate HTTP status codes
- Justifying the choice of algorithm based on the non-distributed constraint
- Demonstrating awareness of the 'boundary spike' problem
Sample Answer
To design a simple service-level throttling mechanism, I would start by defining the core constraint: preventing any single client from overwhelming our resources. Since this is not a distributed system, we can rely on l…
Common Mistakes to Avoid
- Ignoring concurrency issues and assuming thread-safe counters work automatically
- Over-engineering the solution with Redis or distributed caches when local memory suffices
- Failing to mention how the system handles the exact moment a time window resets
- Neglecting to specify the HTTP response format for rejected requests
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