Scaling a Flask API on Ubuntu: Best Practices for Load Balancing and Caching
I'm working through a tutorial and I'm integrating two systems and This might be a silly question, but Hey everyone, I'm running into an issue that's driving me crazy..... Currently developing a Flask API that serves as the backend for a web application. Our user base is expected to grow significantly, so I'm exploring best practices for scaling this application effectively on an Ubuntu server. I've already set up the API, and it runs smoothly on a single instance. However, load testing with Apache Bench revealed performance bottlenecks when simulating concurrent users. Here’s the command I used: ```bash ab -n 1000 -c 100 http://myapi.com/endpoint ``` The average response time was around 500ms, which is not ideal. To address this, I’m considering implementing a reverse proxy with Nginx, but I’m unsure about the configuration settings needed for optimal performance. Here’s a sample Nginx config I’m contemplating: ```nginx server { listen 80; server_name myapi.com; location / { proxy_pass http://localhost:5000; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; } } ``` In addition to using Nginx, I've been looking into caching strategies. Flask-Caching seems like a good fit, but I’m curious about the best cache backend to use—Redis or Memcached? I’ve read that Redis has better performance with complex data types, but Memcached might be simpler to implement. I’ve also been experimenting with horizontal scaling by deploying multiple instances of the API using Docker Swarm. The problem is that I haven't successfully managed session persistence across containers. Planning to use Redis for session management but would appreciate any insights on configuration specifics or potential pitfalls. In preparation for production, I want to ensure that I have a solid logging mechanism in place. Using the built-in logging module in Python seems sufficient, but I’m contemplating integrating a more comprehensive solution like ELK Stack or Prometheus for better monitoring. What would be a recommended approach for logging in a scalable Flask application? Any guidance on configurations, caching backends, or load balancing strategies would be invaluable. Thank you! I'm working on a API that needs to handle this. I've been using Python for about a year now. Thanks, I really appreciate it! Any advice would be much appreciated. For context: I'm using Python on Windows 10. I'd really appreciate any guidance on this.