API Rate Limiting System Design: Interview Deep Dive
February 15, 2026
Technical Tips5 min read
API Rate Limiting System Design
Rate limiting is a critical component tested in system design interviews. It protects services from abuse, ensures fair usage, and prevents cascading failures. Every major API (GitHub, Twitter, Stripe) implements rate limiting.
The four rate limiting algorithms you must know: Token Bucket (smooth, allows bursts), Leaky Bucket (constant rate), Fixed Window Counter (simple but boundary spike issue), and Sliding Window Log (precise but memory-intensive).
Algorithm Comparison
| Algorithm | Pros | Cons |
|---|---|---|
| Token Bucket | Allows bursts, smooth | Two parameters to tune |
| Leaky Bucket | Constant output rate | No burst handling |
| Fixed Window | Simple, low memory | Boundary spike (2x burst) |
| Sliding Window | Precise, no boundary issues | Higher memory usage |
Implementation with Redis
Use Redis INCR + EXPIRE for fixed window, or sorted sets for sliding window. Distributed rate limiting requires either a centralized Redis cluster or approximate algorithms (each node tracks locally with eventual sync).
Related: API design best practices, caching strategies.
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