How can I optimize a Django REST API for high concurrency without hitting database limits?
I'm reviewing some code and I'm confused about I've encountered a strange issue with I'm currently developing a Django REST API using Django 3.2 and Django REST Framework 3.12..... My application is experiencing performance optimization when faced with high concurrency due to database connection limits. I'm noticing important slowdowns and occasional `OperationalError: too many connections` errors when multiple users are trying to access the API simultaneously. I've implemented caching with Redis for frequently accessed data, but I believe there’s more I can do at the database level to handle this better. Here’s a snippet of my settings.py: ```python DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'mydatabase', 'USER': 'myuser', 'PASSWORD': 'mypassword', 'HOST': 'localhost', 'PORT': '5432', 'CONN_MAX_AGE': 60, } } ``` I've also tried increasing the `max_connections` parameter in my PostgreSQL configuration, but it doesn't seem to resolve the scenario entirely. The other day, I tried using Django's `@transaction.atomic` to group DB operations, but it didn’t yield the expected improvements. I'm currently using `gunicorn` with 4 workers, which feels insufficient under load. I’ve considered using `asyncio` for non-blocking calls but I'm uncertain about how to integrate it with Django effectively. Should I switch to an async framework like FastAPI instead? What steps can I take to ensure that my API can scale smoothly under high load without running into database connection issues? Any specific patterns or practices that would help in this scenario would be greatly appreciated! I recently upgraded to Python 3.9. Thanks for any help you can provide! I've been using Python for about a year now. Any help would be greatly appreciated! Thanks, I really appreciate it!