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

SeniorPrefer not to say

Apr 2026

Summary

Roblox system design round focused entirely on building a rate-limiter service. Pretty deep dive, they wanted real trade-offs not just a surface-level answer.

Questions Asked (1)

Design a rate-limiter service, including the API interface, choice of algorithm, single-node vs distributed architecture, handling of clock skew and bursty traffic, multi-tier limits, fail-open vs fail-closed behavior, and how to communicate limits back to clients.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This is one of those questions where you think you know it and then you open your mouth and realize how many corners there are.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., Roblox's massive concurrent user base), then propose a distributed rate limiter using a sliding window or token bucket algorithm with Redis for shared state. Walk through trade-offs for each design decision, covering API design, clock skew handling, burst management, multi-tier limits, failure modes, and client communication.

Pro tip: Emphasize idempotency and graceful degradation: use client-side rate limiting to reduce server load, and consider fail-open with monitoring for non-critical paths to avoid cascading failures. Mention that Roblox likely needs per-user, per-IP, and per-endpoint limits with different thresholds.

1. Clarify Requirements and Scope

Ask about scale (QPS, number of users), latency requirements, and whether limits are per-user, per-IP, or global. Confirm if the service must be highly available and what consistency guarantees are needed.

2. Design API and Algorithm

Define a simple API (e.g., check_limit(key, cost) returning allowed/denied and remaining quota). Choose an algorithm like sliding window log or token bucket, explaining trade-offs in memory, accuracy, and burst handling.

3. Architecture: Single-Node vs Distributed

Discuss single-node limitations (no horizontal scaling, single point of failure) and propose a distributed design using Redis or a dedicated service with consistent hashing. Address clock skew by using a centralized time source or logical timestamps.

4. Handle Edge Cases and Policies

Explain how to handle bursty traffic (e.g., token bucket with burst capacity), multi-tier limits (global, per-user, per-endpoint), and fail-open vs fail-closed behavior based on criticality. Describe how to communicate limits via headers (X-RateLimit-Limit, X-RateLimit-Remaining, Retry-After).

5. Summarize Trade-offs and Monitoring

Recap key decisions and their trade-offs (e.g., accuracy vs performance, consistency vs availability). Mention monitoring and alerting for rate limiter effectiveness and abuse detection.

Key Points to Mention

API design: check_limit(key, cost) with response including allowed, remaining, and reset time.
Algorithm choice: sliding window log for accuracy vs token bucket for efficiency and burst handling.
Distributed architecture: Redis with Lua scripts for atomic operations, or a dedicated rate limiter service with local caching.
Clock skew mitigation: use Redis server time or NTP-synced clocks, and avoid relying on client timestamps.
Multi-tier limits: combine global, per-user, and per-endpoint limits with different thresholds and priorities.
Fail-open vs fail-closed: fail-open for non-critical paths to maintain availability, fail-closed for security-critical paths; include monitoring and fallback mechanisms.
Client communication: standard HTTP headers (X-RateLimit-*, Retry-After) and clear error responses (429 Too Many Requests).

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