Why Rate Limiting Matters?
APIs power the web, but without rate limiting, a single user (or bot) can overload your system. Think about how login attempts, API calls, and DDOS attacks could take down your app.
Let’s see if you can design a rate-limiting system like the pros!
🛡️ Challenge #1: Implement Basic Rate Limiting
The Problem
Your API is getting too many requests from a single user. You need to limit how often they can hit an endpoint.
The Solution
1️⃣ Use a token bucket or fixed window algorithm to track requests.
2️⃣ Allow users X requests per minute (e.g., 100 requests/min).
3️⃣ Return 429 Too Many Requests when the limit is hit.
💡 Bonus Challenge: Implement different rate limits for free and premium users.
🔄 Challenge #2: Scaling Rate Limiting with Redis
The Problem
Your rate-limiting logic fails at scale—you need to distribute it across multiple servers.
The Solution
1️⃣ Store request counts in Redis (fast & scalable).
2️⃣ Sync rate limits across all API servers in real-time.
3️⃣ Implement IP-based & user-based rate limits for more security.
💡 Bonus Challenge: Implement Geo-based rate limiting (e.g., limit per region).
Final Thoughts
Rate limiting isn’t just about stopping spam—it’s about:
✅ Preventing abuse & DDOS attacks
✅ Scaling APIs without crashes
✅ Fair usage between free & premium users
🚀 Want more challenges like this? Start learning here 👉 Backend Challenges
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