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Microsoft·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

Microsoft system design round focused entirely on building a rate limiter from scratch. One question, lots of depth expected, and I felt the pressure pretty quickly once the follow-ups started stacking up.

Questions Asked (1)

Q1

Design a rate limiter service. Walk through your algorithm choice, how you'd enforce it locally vs across distributed instances, how counters get shared, and how you'd handle bursts versus steady traffic while balancing accuracy, latency, and availability.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

I jumped straight to token bucket because it felt cleanest for burst handling and kind of glossed over fixed vs sliding window.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., scale, accuracy, latency, burst handling) and then propose a rate limiting algorithm (e.g., token bucket) that balances accuracy and burst tolerance. Explain how to enforce limits locally with in-memory counters and synchronize across distributed instances using a shared store like Redis, discussing trade-offs between consistency, latency, and availability.

Pro tip: Emphasize that rate limiting is often about protecting downstream services, so discuss how you'd handle failures gracefully (e.g., fail-open vs fail-closed) and how you'd monitor and adjust limits dynamically.

1. Clarify Requirements and Constraints

Ask about expected traffic volume, number of instances, required accuracy, latency tolerance, and whether bursts should be allowed. This shapes algorithm and architecture choices.

2. Choose a Rate Limiting Algorithm

Compare algorithms like token bucket, leaky bucket, fixed window, and sliding window. Recommend token bucket for its burst handling and smoothness, and justify your choice based on requirements.

3. Design Local Enforcement

Describe how each instance enforces limits using in-memory counters (e.g., token bucket per user). Discuss thread safety and local caching to minimize latency.

4. Design Distributed Coordination

Explain how to share counters across instances using a centralized store (e.g., Redis) with atomic operations. Discuss trade-offs: using Redis for accuracy vs. local counters with periodic sync for lower latency and higher availability.

5. Address Bursts, Accuracy, Latency, and Availability

Detail how the algorithm handles bursts (e.g., token bucket capacity). Discuss strategies to balance accuracy (e.g., sliding window) with latency (e.g., local decisions) and availability (e.g., fallback to local limits if Redis is down).

Key Points to Mention

  • Token bucket algorithm and its parameters (capacity, refill rate) for burst handling.
  • Local vs. distributed enforcement: in-memory counters vs. Redis with atomic increments.
  • Trade-offs between accuracy (sliding window) and performance (fixed window).
  • Handling bursts: allowing short bursts up to bucket capacity while smoothing steady traffic.
  • Latency considerations: using local caches and asynchronous sync to reduce round-trips.
  • Availability: fail-open vs. fail-closed strategies and graceful degradation during Redis outages.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.