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

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Apr 2026

Summary

Grammarly system design round, one big question that sprawled into a bunch of sub-topics. The rate limiter prompt sounds deceptively contained but they really wanted to see how deep you could go on the tradeoffs.

Questions Asked (1)

Design a rate limiter that enforces per-key request quotas. Cover your choice of algorithm, the API design, data structures, time and space complexity, thread-safety, distributed operation with a shared cache, clock skew, burstiness versus smoothness, and edge cases like bursts at window boundaries.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I went with token bucket pretty fast because I know it well, but then they started pulling on every thread.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., per-key quotas, distributed environment, burst tolerance) and then propose a rate limiting algorithm (e.g., sliding window with counters or token bucket) that balances accuracy and efficiency. Detail the API, data structures, concurrency handling, and distributed coordination using a shared cache like Redis, while addressing clock skew and edge cases. Conclude by discussing trade-offs and potential optimizations.

Pro tip: Demonstrate awareness of real-world constraints by mentioning how you would handle hot keys and cache failures gracefully, and by proposing a hybrid approach that combines the simplicity of fixed windows with the accuracy of sliding logs.

1. Clarify Requirements and Constraints

Ask about expected request rates, burstiness tolerance, distributed setup, and consistency requirements to tailor the design.

2. Choose a Rate Limiting Algorithm

Select an algorithm (e.g., sliding window counter, token bucket) and justify it based on trade-offs between accuracy, memory, and burst handling.

3. Design the API and Data Structures

Define the interface (e.g., allowRequest(key)) and specify data structures (e.g., Redis sorted sets for sliding window) with time/space complexity.

4. Address Concurrency and Distributed Coordination

Explain thread-safety using atomic operations or locks, and distributed operation with a shared cache, including handling clock skew and synchronization.

5. Discuss Edge Cases and Trade-offs

Cover burstiness at window boundaries, hot keys, cache failures, and compare smoothness vs. burstiness, concluding with potential improvements.

Key Points to Mention

Choice of algorithm (e.g., sliding window log vs. counter) and its impact on burstiness and smoothness.
API design: simple interface like allowRequest(key) returning boolean or remaining quota.
Data structures: Redis sorted sets for sliding window, or counters with TTL for fixed window.
Thread-safety: use atomic Redis operations (INCR, EXPIRE) or Lua scripts for atomicity.
Distributed operation: shared cache like Redis, handling network partitions and clock skew with logical timestamps or NTP.
Edge cases: bursts at window boundaries, hot keys, cache failures, and graceful degradation.

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