What is the optimal rate limiting practice for REST APIs?

For optimal rate limiting in REST APIs, consider factors like client needs, API usage patterns, and server capacity. Define rate limits based on the API's criticality, resource availability, and potential impact on users. Implement granular rate limits per endpoint or user to prevent abuse and ensure fair access. Utilize token buckets or sliding window algorithms for precise rate limiting control. Provide clear documentation and error messages to inform clients about rate limit policies and enforcement mechanisms. Continuously monitor and adjust rate limits based on usage trends and performance metrics to maintain optimal API availability and performance.

The optimal rate-limiting practice for REST APIs involves: 1. Setting reasonable default limits. 2. Allowing customization based on user needs. 3. Implementing the token bucket algorithm. 4. Using exponential backoff for retries. 5. Monitoring and adjusting limits based on usage. 6. Clearly communicating limits to users. 7. Considering different strategies for different requests. 8. Handling rate limiting at the edge for scalability. This approach ensures protection against abuse while maintaining a good user experience.

It is helpful to look at the traffic patterns on your API. For example, an API might have 500 requests every second throughout a day, 24x7, while another might have 500 requests in one second every Sunday at 2pm.

Here are some practical methods: Fixed Window: Set a fixed number of requests per time window for each client. Simple, but watch for spikes. Sliding Window: Adjust requests based on client activity. It's dynamic and fair. Token Bucket: Give clients a token bucket refilled at a fixed rate. Allows bursts while maintaining flow. Leaky Bucket: Queue requests and process at a fixed rate. Smoothens flow, prevents spikes. Choose wisely based on your API's needs. Each method has its perks!

?? Selecting the appropriate rate-limiting method is crucial for API performance. ?? Fixed window - Sets a fixed number of requests per time window, but can lead to spikes. ?? Sliding window - Dynamically adjusts requests per time window for fairness and accuracy. ?? Token bucket - Grants clients a bucket of tokens replenished at a fixed rate, allowing bursts. ?? Leaky bucket - Processes requests from a queue at a fixed rate, ensuring steady flow and preventing spikes.

Fixed window is the most straightforward and simple one that you can try. However it does not strictly limit the rate as you would expect. e.g. if you rate limit at 1000 per second, and the fixed window is every second on the second, then you are allowing 1000 requests at the end of one window, and another 1000 at the beginning of the second. This means you're allowing 2000 requests per second.

Choosing the right tool plays a crucial part and depends on your specific needs and trade-offs. ?? Middleware: Easily customizable but adds complexity to API code. ?? Proxy: Fast and scalable but may introduce a single point of failure. ?? Database: Reliable and flexible but may increase latency and contention.

In your Github codebase, you HAVE to have CI/CD workflows that scan the repository for vulnerabilities. CodeQL namely scans for Top 10 OWASP vulnerabilities and I have done this for my internship at Recidiviz, resolving over 200+ vulnerabilities and 20+ rate limiting issues found through this workflow. Git is super powerful in this respect - use the tools it provides you to do this efficiently.

In my opinion, maintaining the balance between security and usability is crucial for rate limiting. Also, careful consideration while choosing between identity-based or resource-based limiting!

You should not only rate limit at the ingress points of your application, but also at egress. Your downstream dependencies might have various different rate limits, and you don't want limit breaches happening there bubbling up.