🛑 Rate Limiting: Protecting APIs from Overload
By DevTonics – Simplifying System Design, DevOps & Backend Concepts
APIs are the backbone of modern applications — but what happens when they’re flooded with requests? From hackers to misbehaving clients, uncontrolled access can crash your systems.
The hero: Rate Limiting.
It’s one of the most essential backend strategies for protecting APIs and maintaining system stability — and it's frequently discussed in system design interviews.
🚦 What is Rate Limiting?
Rate Limiting is a technique to control how many requests a user/client can make to a service in a fixed period of time.
It helps you:
✅ Prevent abuse (e.g., brute-force attacks)
✅ Maintain service quality under load
✅ Ensure fair usage
✅ Protect backend services from being overwhelmed
🔐 Why Do We Need Rate Limiting?
Imagine an authentication API without limits — anyone could fire off login attempts until your system crashes or gets exploited.
Rate limiting ensures:
Security → Stops spam, bots, DDoS attempts
Reliability → Protects backend resources
Cost-efficiency → Avoids server overuse or cloud billing spikes
User fairness → No single user hogs the API
🧠 Where Is It Used?
🔐 Authentication systems
💬 Messaging apps (message rate per user)
💸 Payment & checkout systems
🚀 Public APIs (GitHub, Twitter, Stripe)
🌐 CDN and edge servers
🛡 API Gateways like AWS, Kong, NGINX
🧩 Popular Rate Limiting Algorithms
Let’s explore the four most common strategies:
1. Fixed Window
Set a limit per time window. Simple but bursty.
Example:
100 requests allowed per 1-minute window → resets every minute.
✅ Easy to implement
❌ Can spike at boundaries (e.g., just before reset)
2. Sliding Window Log
Logs each request timestamp and checks how many fall in the past N seconds.
✅ Fair & accurate
❌ Needs more memory to store logs
3. Sliding Window Counter
Counts requests in fixed sub-windows to approximate smooth limits.
✅ Efficient
❌ Slightly less accurate than full log
4. Token Bucket
Tokens are added over time. Each request uses a token.
When the bucket is empty, requests are denied or delayed.
✅ Allows bursts while maintaining control
✅ Ideal for variable traffic patterns
5. Leaky Bucket
Think of it as a queue with a fixed output rate.
If requests come in faster than the leak rate, excess ones get dropped.
✅ Smooth, consistent rate
✅ Helps with traffic shaping
📚 Real-World Rate Limiting Tools
API Gateway solutions: AWS API Gateway, Kong, Apigee
Redis-based rate limiters (common in custom solutions)
Cloudflare/CDN edge rules
Backend frameworks: Express.js, Django, Spring Boot support plugins/middleware
Interview Questions to Expect
1. What is rate limiting?
Answer:
Rate limiting restricts the number of requests a client can make to a system in a given time frame to protect against abuse, maintain stability, and ensure fair usage.
2. Why is rate limiting important in distributed systems?
Answer:
It prevents system overload, throttles malicious traffic (e.g., brute-force, DDoS), reduces resource exhaustion, and improves overall system reliability and fairness.
3. How would you implement basic rate limiting?
Answer:
Using a key-value store like Redis:
Key: client IP or user ID
Value: counter
Expire the key after a time window (e.g., 1 min)
Increment counter and reject if it exceeds the limit
4. What are common algorithms for rate limiting?
Answer:
Fixed Window
Sliding Window Log
Sliding Window Counter
Token Bucket
Leaky Bucket
5. What is the difference between Token Bucket and Leaky Bucket?
Answer:
Token Bucket: Allows bursts; tokens accumulate over time
Leaky Bucket: Processes at a constant rate; no bursts allowed
6. What is the difference between Fixed Window and Sliding Window?
Answer:
Fixed Window: Simple counter resets after a time unit
Sliding Window: More accurate, smoother; tracks requests over a rolling window
7. How does rate limiting help with security?
Answer:
Prevents brute-force attacks, credential stuffing, denial of service, spam, and misuse of public APIs.
8. Where should rate limiting be applied?
Answer:
At the API Gateway, load balancer, edge/CDN layer, or application middleware — ideally as close to the source as possible.
9. How does rate limiting work with Redis?
Answer:
Redis is fast and supports atomic operations. You can implement rate limits using counters, TTLs, or Lua scripts for advanced algorithms.
10. How do public APIs (e.g., GitHub, Twitter) enforce rate limits?
Answer:
They associate rate limits with tokens or IPs, use Token Bucket or Sliding Window techniques, and return HTTP status codes like 429 (Too Many Requests).
11. What are typical rate limiting headers in APIs?
Answer:
X-RateLimit-LimitX-RateLimit-RemainingX-RateLimit-Reset
12. What is a 429 status code?
Answer:
HTTP 429 = “Too Many Requests.” It's returned when a client exceeds the allowed request rate.
13. How do you design rate limits per user and per IP?
Answer:
Store and track separate counters for user IDs and IP addresses. Enforce both limits using keys like rate:user:{id} and rate:ip:{ip} in Redis.
14. How can you rate limit across distributed systems?
Answer:
Use a centralized store like Redis or Memcached. Alternatively, use rate-limiting middleware integrated with your API Gateway or service mesh.
15. How does rate limiting work in Kubernetes?
Answer:
Via Ingress controllers, API Gateways (e.g., Kong, Ambassador), or sidecars. You can also use Istio with Envoy to configure global or service-level rate limits.
16. How would you handle rate limits differently for premium vs. free users?
Answer:
Assign higher request thresholds to premium users by tagging their identity and customizing the rate limit logic.
17. Can rate limiting be adaptive?
Answer:
Yes. Adaptive rate limiting dynamically adjusts limits based on system load, user behavior, or reputation scores.
18. What challenges exist in rate limiting?
Answer:
Handling burst traffic
Syncing counters in distributed systems
Preventing false positives for shared IPs (e.g., NAT)
Choosing the right algorithm per use case
19. How do you prevent replay attacks while rate limiting?
Answer:
Combine rate limiting with unique request IDs, tokens, and timestamps. Rate limiting alone doesn’t stop replays — use it alongside authentication and nonce verification.
20. How do CDNs apply rate limiting?
Answer:
They throttle requests at the edge, applying rules by IP, user-agent, or geography. Tools like Cloudflare, Akamai, and Fastly use it to block DDoS and scraping.
✅ Bonus Tip for Interviews:
Be ready to:
Sketch a Redis-based rate limiter
Compare algorithm trade-offs
Handle scale (e.g., millions of users/IPs)
Discuss retries, backoff strategies, and error handling
🧠 Key Takeaways
Rate Limiting = Traffic control for APIs
Protects systems from overload & abuse
Multiple strategies: fixed, sliding, token, leaky
Used in all critical systems, from login to payments
Essential topic in system design & backend interviews
📥 Want a Free PDF Cheatsheet?
Comment "RateLimit" or subscribe to get our DevTonics Rate Limiting Guide — with interview Q&A, architecture diagrams, and algorithm visuals.
🔁 Follow @DevTonics or visit DevTonics.in for weekly guides on:
System Design
DevOps
Backend Engineering
Career Prep