Modern distributed systems and APIs often face a common challenge: handling too many requests from clients.
Without protection, a sudden surge in traffic can overload servers, cause downtime, or degrade performance.
This is where Rate Limiting becomes essential.
In this article, I will explain how I built a lightweight and extensible Rate Limiter using Java and Spring Boot that supports multiple rate-limiting strategies for microservices and APIs.
🔗 Project Repository
Source Code:
[https://github.com/milanmandal-1/Rate-Limiter]
What is Rate Limiting?
Rate limiting is a technique used to control the number of requests a client can send to a server within a specific time period.
It helps to:
Prevent API abuse
Protect backend services
Improve system stability
Ensure fair resource usage
Many large platforms like Google, Amazon, and Netflix rely heavily on rate limiting to maintain reliability.
Technologies Used
This project uses modern backend technologies:
Java
Spring Boot
Microservices Architecture
API Gateway
Service Registry
Config Server
The system is designed to integrate easily into distributed microservice environments.
🧠 Rate Limiting Algorithms Implemented
This project supports multiple rate limiting strategies.
1️⃣ Token Bucket Algorithm
The Token Bucket algorithm allows a burst of traffic while maintaining an overall rate limit.
How it works:
Tokens are added to a bucket at a fixed rate
Each request consumes a token
If the bucket is empty, the request is rejected
Benefits:
✔ Allows traffic bursts
✔ Smooth request flow
✔ Widely used in APIs
2️⃣ Fixed Window Algorithm
The Fixed Window strategy counts requests within a fixed time window.
Example:
Limit = 100 requests
Time window = 1 minute
If a client sends more than 100 requests within that minute, further requests are rejected.
Advantages:
✔ Simple to implement
✔ Efficient for small systems
3️⃣ Sliding Window Algorithm
The Sliding Window algorithm improves accuracy compared to fixed windows.
Instead of resetting counters abruptly, it calculates requests based on a moving time window.
Benefits:
✔ More precise rate limiting
✔ Prevents traffic spikes at window boundaries
🏗 Microservices Architecture
This project demonstrates rate limiting inside a Spring Boot microservices ecosystem.
Main components include:
API Gateway
Service Registry
Config Server
Hotel Service
Rating Service
User Service
The rate limiter can be integrated at the API Gateway level, ensuring all incoming requests are validated before reaching downstream services.
📂 Project Structure
Rate-Limiter
│
├── ApiGateway
├── ConfigServer
├── ServiceRegistry
├── HotelService
├── RatingService
├── UserService
│
├── README.md
└── LICENSE
This structure demonstrates a production-style microservices setup.
Example Rate Limit Scenario
Example configuration:
Limit: 10 requests
Time Window: 1 minute
If a client sends:
Request 1 → Allowed
Request 2 → Allowed
...
Request 10 → Allowed
Request 11 → Blocked
This ensures backend services remain stable under heavy load.
** Why Rate Limiting is Critical for APIs**
Rate limiting protects APIs from:
🚫 DDoS attacks
🚫 API abuse
🚫 Resource exhaustion
It also helps maintain fair usage among multiple clients.
** Real-World Use Cases**
Rate limiters are widely used in:
Public APIs
Payment systems
Authentication services
Cloud platforms
SaaS platforms
Almost every major API provider uses some form of rate limiting.
Future Improvements
Some potential enhancements for this project include:
Redis-based distributed rate limiting
Kubernetes deployment
Dynamic configuration updates
Advanced monitoring with Grafana
Integration with CI/CD pipelines
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