Django QuerySet Performance implementing Large Datasets in Filtering Operations
I'm having trouble with I can't seem to get I'm relatively new to this, so bear with me..... I'm currently working with important performance optimization when filtering large datasets using Django's ORM. I have a model defined as follows: ```python from django.db import models class Product(models.Model): name = models.CharField(max_length=255) price = models.DecimalField(max_digits=10, decimal_places=2) created_at = models.DateTimeField(auto_now_add=True) ``` When I attempt to filter products based on a range of prices, the query seems to take an unacceptably long time to execute. Here’s the code I’m using: ```python from django.db.models import Q # Filtering products with price between 50 and 100 queryset = Product.objects.filter(Q(price__gte=50) & Q(price__lte=100)) ``` The database contains over 1 million records, and I've already added an index on the `price` field, but the query still runs for several seconds. I’ve tried using `select_related()` and `prefetch_related()` to optimize related queries, but it doesn't seem to help here since I'm not actually fetching related fields in this case. I also attempted to use raw SQL with `raw()` to see if that would be faster, but it yielded similar performance. I'm running Django 3.2 with PostgreSQL 12. Can anyone suggest additional optimizations or debugging strategies for improving the performance of such filters? Perhaps there are other configurations or indexing strategies I’m overlooking? For context: I'm using Python on macOS. Has anyone else encountered this? I'm developing on Ubuntu 22.04 with Python. Thanks for taking the time to read this!