Rate Limiter Service

Overview

Implement the rate limiting system you discuss in system design interviews. Build configurable rate limiters using Redis for distributed state. Support multiple algorithms, per-client rules, and a dashboard showing current usage and throttled requests.

Rate limiting is one of the most commonly asked system design topics, yet most candidates can only describe algorithms at a conceptual level. Building a working distributed rate limiter forces you to confront the real challenges: atomic read-modify-write operations in Redis using Lua scripts, choosing between algorithms with different precision/memory trade-offs, designing a flexible configuration system, and returning standardized rate limit headers.

The project exercises Redis at a deeper level than most applications ever touch. You will write Lua scripts that execute atomically inside Redis, use sorted sets for sliding window logs, and implement the token bucket refill calculation with precision timing. These are skills that transfer directly to building any distributed system component.

For interviews, being able to walk through a working implementation of three different rate limiting algorithms, explain their trade-offs with concrete memory and precision numbers, and show a monitoring dashboard that tracks usage and throttled requests transforms a theoretical system design answer into a credible engineering demonstration.

Interview questions

• How does Token Bucket work in Redis?
• Sliding Window Log vs Counter?
• How do I handle distributed coordination?
• How should the configuration system work?
• What headers should the response include?
• How do I handle Redis unavailability?

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