Roblox·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

This was a follow-up system design round at Roblox, building on an earlier question about rate limiting. The interviewer pushed into more complex territory by introducing multi-field requests.

Questions Asked (1)

Given a request that contains multiple fields, how would you design a rate limiter that enforces separate limits for each field independently?

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was a part two, so I thought I had some momentum.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: what fields are in the request, what are the separate limits, and what is the desired behavior when a limit is exceeded. Then propose a design that uses a composite key (e.g., field name + field value) for each limit, with a distributed rate limiting algorithm like sliding window or token bucket, and discuss trade-offs around storage, accuracy, and performance.

Pro tip: Mention that you would use a centralized data store like Redis with atomic operations (e.g., Lua scripts) to ensure consistency across multiple rate limiter instances, and consider using a sliding window log for precise per-field limits while being mindful of memory usage.

1. Clarify Requirements

Ask about the fields, their expected cardinality, the specific limits per field, and whether limits are per user, per IP, or global. Also clarify the desired response when a limit is exceeded (e.g., 429 status, retry-after header).

2. Choose Rate Limiting Algorithm

Select an algorithm that supports per-key limits, such as sliding window log, sliding window counter, or token bucket. Discuss trade-offs: sliding window log is precise but memory-intensive; token bucket is memory-efficient but may allow bursts.

3. Design Data Model and Key Structure

Define a composite key for each field limit, e.g., 'rate_limit:{field_name}:{field_value}:{user_id}'. Store counters or timestamps in a fast data store like Redis. Consider using sorted sets for sliding window log or simple counters with TTL for fixed windows.

4. Ensure Atomicity and Scalability

Use atomic operations (e.g., Redis Lua scripts or transactions) to check and update all field limits atomically. Discuss horizontal scaling with sharding or consistent hashing if needed, and how to handle hot keys.

5. Handle Edge Cases and Trade-offs

Address what happens when multiple limits are exceeded (return the most restrictive), how to handle missing fields, and the impact of distributed rate limiting on latency. Discuss monitoring and dynamic limit adjustments.

Key Points to Mention

Composite key design to track each field independently (e.g., field name + value + user identifier).
Choice of rate limiting algorithm (sliding window, token bucket) and its implications on accuracy and memory.
Use of a centralized store like Redis with atomic operations (Lua scripts) for consistency.
Handling multiple limits: check all and return the most restrictive or aggregate errors.
Scalability considerations: sharding, hot keys, and latency impact.
Trade-offs between precision, memory usage, and complexity.

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