👀 Views: 0 💬 Answers: 1 📅 Created: 2025-09-21

Django ORM prefetch_related performance scalability Python

I've been struggling with this for a few days now and could really use some help. Could someone explain Working on a project where we're scaling our Django application to handle increased user data efficiently. The current implementation retrieves related user data, but the standard `select_related` isn't performing well due to the volume of data being accessed. I’ve been looking into `prefetch_related` for better optimization. Here’s a simplified version of the code: ```python from django.db import models class User(models.Model): username = models.CharField(max_length=150) email = models.EmailField() class Profile(models.Model): user = models.OneToOneField(User, on_delete=models.CASCADE) bio = models.TextField() # Retrieving user data users = User.objects.prefetch_related('profile').all() ``` This implementation has significantly improved the query performance when accessed in the view, but I suspect there might still be room for refinement as the dataset grows. During code review, my team suggested checking if `only()` could be beneficial to limit the fields being loaded. I tried adding it: ```python users = User.objects.prefetch_related('profile').only('username', 'email') ``` While this reduced the data being fetched, I’m unsure how it affects the behavior of `prefetch_related`. The additional layer of complexity led to some confusion in the testing phase, particularly with accessing the related `profile` fields. Can anyone share insights on the best practices for using `prefetch_related` in a scenario with many-to-one relationships? Should we also consider using raw SQL queries for more complex operations, or is there another ORM optimization method that balances performance with maintainability? Any experiences or examples would be appreciated. My team is using Python for this desktop app.