Case Study
Distributed Rate Limiter
Redis-based traffic control system designed to keep APIs fast while preventing abuse at scale.
Architecture
Client requests are routed to a rate-limiter service that evaluates policy state before forwarding decisions.
Redis stores counters and windows atomically through Lua, removing race conditions during concurrent traffic.
Policy configuration is route-aware so throttling can differ between public and internal APIs.
Screenshots
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Architecture
Service flow and Redis-backed policy evaluation.
Challenges
- Preserving low latency while keeping decisions atomic across distributed traffic.
- Designing a data model that supports multiple limiting strategies without duplicating logic.
- Balancing configurability with straightforward operational behavior.
Benchmarks
10,000+ requests/sec
Sub-5ms latency
Horizontal scalability
Lessons Learned
- Atomic primitives matter more than complex coordination when latency budgets are tight.
- A small, explicit policy surface is easier to reason about under load.
Tech Stack
PythonRedisLuaDocker
Key Results
10,000+ requests/sec
Sub-5ms latency
Horizontal scalability