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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

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This is a medium difficulty System Design question, commonly asked during Technical Screen rounds at Roblox.

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This question is commonly asked for Software Engineer candidates at Roblox during technical interviews.

Design a rate limiter | Roblox Interview Question

Q: Design a rate limiter

This question evaluates a candidate's competency in designing distributed rate limiting systems, covering rate-limiting algorithms, scalability and fault-tolerance patterns, consistency versus latency trade-offs, and data modeling for distributed state.

Problem: Design a Rate Limiter

Design a rate limiting service to protect an API from abuse and to provide fair usage.

Requirements

The API gateway (or edge service) must enforce limits such as:
- Per-user (or per API key) requests per time window (e.g., 100 req/min).
- Per-IP limits (optional).
- Potentially per-endpoint or per-tenant limits.
Should support common policies:
- Fixed window (acceptable if you discuss edge effects), sliding window, token bucket, or leaky bucket.
Must work in a distributed environment (multiple stateless API servers).
Low latency overhead (rate limiting should be fast).

Clarifications to address

What happens when over limit (HTTP 429, headers like Retry-After )?
How do you handle bursts?
What are the consistency needs (strict vs near-accurate)?
How do you avoid a single point of failure?

Deliverables

Explain:

The chosen algorithm(s) and why.
The high-level architecture and data stores.
Key data model, read/write flow.
Scaling, fault tolerance, and edge cases.

Problem: Design a Rate Limiter

Design a rate limiting service to protect an API from abuse and to provide fair usage.

Requirements

The API gateway (or edge service) must enforce limits such as:

Per-user (or per API key) requests per time window (e.g., 100 req/min).
Per-IP limits (optional).
Potentially per-endpoint or per-tenant limits.

Should support common policies:

Fixed window (acceptable if you discuss edge effects), sliding window, token bucket, or leaky bucket.

Must work in a distributed environment (multiple stateless API servers).

Low latency overhead (rate limiting should be fast).

Clarifications to address

What happens when over limit (HTTP 429, headers like Retry-After )?

How do you handle bursts?

What are the consistency needs (strict vs near-accurate)?

How do you avoid a single point of failure?

Deliverables

Explain:

The chosen algorithm(s) and why.

The high-level architecture and data stores.

Key data model, read/write flow.

Scaling, fault tolerance, and edge cases.

Design a rate limiter

Quick Overview

Problem: Design a Rate Limiter

Requirements

Clarifications to address

Deliverables

Solution

Comments (0)

Design a rate limiter

Quick Overview

Problem: Design a Rate Limiter

Requirements

Clarifications to address

Deliverables

Solution

Comments (0)