I built a rate limiter that survives process restarts — no Redis required

1. 1Implement a rate limiter using a Python library like token-bucket or leaky-bucket
2. 2Store the rate limiter state in a persistent storage like a database or a file
3. 3Configure the rate limiter to reload its state after a process restart
4. 4Test the rate limiter with a high volume of requests to ensure it survives process restarts
5. 5Apply the rate limiter to a Python service to prevent abuse and denial-of-service attacks

Developers and DevOps engineers on a team can benefit from this approach as it provides a scalable and reliable way to implement rate limiting without adding extra dependencies

💡 You can build a reliable rate limiter in Python without relying on Redis by using a persistent storage to store the rate limiter state

🚀 Build a rate limiter in Python that survives process restarts without Redis! 💡