How do you create a rate-limiting filter in Spring Boot?

Table of Contents

• Introduction
• Steps to Create a Rate-Limiting Filter in Spring Boot
  • 1. Create a Rate Limiting Filter
    • Step 1: Define the Filter Class
    • Explanation of the Filter Logic:
  • 2. Register the Filter in Spring Boot
    • Step 1: Create a Configuration Class to Register the Filter
  • 3. Test the Rate Limiting Filter
  • 4. Optional Enhancements
    • 4.1. Custom Error Response
    • 4.2. Distributed Rate Limiting with Redis
• Conclusion

Introduction

Rate limiting is essential for controlling the amount of traffic that hits your Spring Boot application’s endpoints. By implementing rate-limiting mechanisms, you can prevent abuse, reduce the risk of server overload, and ensure fair resource usage. One common way to implement rate limiting is by creating a rate-limiting filter in Spring Boot, which is responsible for intercepting and controlling incoming requests based on predefined rules.

In this guide, we'll show you how to create a custom rate-limiting filter in Spring Boot and apply it to control the number of requests from users within a specified time window.

Steps to Create a Rate-Limiting Filter in Spring Boot

1. Create a Rate Limiting Filter

Filters in Spring Boot provide a powerful mechanism to intercept and manipulate HTTP requests before they reach the controller. To create a custom rate-limiting filter, we will build a filter that checks the request count for each client (based on IP address or user) and applies a rate limit.

Here’s how to create a basic rate-limiting filter:

Step 1: Define the Filter Class

Create a class called RateLimitingFilter that implements javax.servlet.Filter. This filter will track the number of requests for each user or IP address and apply a limit.

Explanation of the Filter Logic:

• Map for Request Counts: The filter maintains a ConcurrentHashMap to track the number of requests for each IP address. The key is the IP address, and the value contains the request count and the timestamp of the first request in the current time window.
• Rate Limit Logic: Each time a request comes in, the filter checks if the request count for the IP address exceeds the defined limit (MAX_REQUESTS) within the specified time window (LIMIT_TIME_PERIOD).
• Request Throttling: If the limit is exceeded, a 429 Too Many Requests status code is returned to the client. If the request is within the allowed limit, it proceeds to the next filter or controller.

2. Register the Filter in Spring Boot

To apply this rate-limiting filter across your application, you need to register it in the Spring Boot configuration class. This can be done using a FilterRegistrationBean.

Step 1: Create a Configuration Class to Register the Filter

Create a WebConfig class to register the RateLimitingFilter:

3. Test the Rate Limiting Filter

After implementing and registering the filter, you can test it by sending multiple requests to an endpoint that matches the filter’s URL pattern (e.g., /api/*). If the request count exceeds the limit (5 requests per minute), you should receive a 429 Too Many Requests response.

4. Optional Enhancements

4.1. Custom Error Response

You can customize the response when the rate limit is exceeded by modifying the doFilter method to return a detailed error message or JSON response:

4.2. Distributed Rate Limiting with Redis

If your application is distributed and you need to share the rate limit state across multiple instances, you can integrate Redis or another distributed caching solution with your filter. This would ensure that rate limiting is applied uniformly across all instances of your application.

To use Redis with rate limiting, you could leverage a library like Bucket4j with Redis support, or you can manually configure Redis to store request counts and timestamps.

Conclusion

Creating a custom rate-limiting filter in Spring Boot is an effective way to control API request flow and protect your application from excessive traffic. The filter ensures that each client (e.g., identified by their IP address) adheres to the rate limit you define, preventing overuse of resources and mitigating the risk of denial-of-service attacks. With the flexibility to apply rate limits to specific URL patterns and customize error handling, this solution can be tailored to fit your specific use case.