Common Django ORM Mistakes to fix

Django ORM is one of the most powerful features of django. It abstracts away much of the complexity of interacting with databases, letting developers manipulate data with Pythonic syntax rather than raw SQL. All these ORM functions generate SQL queries that can become a bottleneck if not handled carefully.
This blog highlights common mistakes when using Django ORM and also offers tips for keeping queries efficient, maintainable, and performant.

1. The N+1 Query Problem

The N+1 query problem occurs when your code triggers one query to fetch a set of records and then runs N additional queries again to fetch related data.

blogs = Blog.objects.all()    # 1 Query
for blog in blogs:
    print(blog.author.name)   # N additional queries

In the example above, accessing blog.author.name inside the loop causes Django to fetch the author record for each blog individually, leading to N additional queries.

How to Fix it
Use select_related for single related objects (e.g., ForeignKey or OneToOneField), as it performs a SQL JOIN to retrieve the main object and its related object(s) in one query. For many-to-many, many-to-one, or reverse relationships, use prefetch_related, which fetches related data in separate queries but combines them efficiently in Python, avoiding the N+1 issue.

# With select_related
blogs = Blog.objects.select_related('author').all()

# With prefetch_related
authors = Author.objects.prefetch_related('blogs').all()

2. Overuse of .all() and .filter()

Developers often chain multiple filters or use .all() followed by repeated queries on the same queryset:

blogs = Blog.objects.all()
active_blogs = blogs.filter(is_archived=False)
popular_blogs = blogs.filter(views__gte=1000)

Although Django tries to optimize querysets by lazily evaluating them only when needed, repeatedly calling filters on the same queryset data can still cause unnecessary hits to the database.

How to Fix it
Combining filters in one statement allows django to generate a single SQL query.

popular_active_blogs = Blog.objects.filter(is_archived=False, views__gte=1000)

3. Not Taking Advantage of values() or values_list()

Sometimes we only need specific fields rather than all field data of the model. During this using .values() or .values_list() can be more efficient.

titles = Blog.objects.values('title')
or
titles = Blog.objects.values_list('title', flat=True)
# values() returns a list of dictionaries.
# values_list() can return tuples or flat values if flat=True is provided.

By fetching only the needed columns, you reduce the amount of data transferred from the database, improving performance.

4. Inefficient Aggregations and Annotations

Repeatedly calling .aggregate() or .annotate() can cause multiple queries. Complex queries with multiple annotations might lead to inefficient SQL queries, which might lead to heavy database operations.

# Example of multiple aggregate
total_count = Blog.objects.aggregate(Count('id'))
author_count = Blog.objects.aggregate(Count('author'))
average_views = Blog.objects.aggregate(Avg('views'))

Recommendation

stats = Blog.objects.aggregate(
    total_count=Count('id'), 
    author_count=Count('author'),
    avg_average_views =Avg('views')
)

5. Not using Database Indexes

Indexing improves query performance by enabling the database to quickly locate and retrieve data, avoiding slow full table scans. Indexes optimize operations like filtering, sorting, and joining, making queries on frequently accessed fields much faster. A missing database index on frequently queried fields can drastically reduce performance.
How to Add Indexes in Django

# Model Field Index
class Blog(models.Model):
    title = models.CharField(max_length=255, db_index=True)
    slug = models.SlugField(max_length=255, db_index=True)

# Meta Indexes
class Blog(models.Model):
    title = models.CharField(max_length=255)
    views = models.IntegerField(default=0)

    class Meta:
        indexes = [
            models.Index(fields=['title', 'views']),
        ]

Indexes can speed up reading but slow down writing speed. So, only index only those fields which you often need to query.

6. Not using Caching

Use caching, when we have to query data that is expensive to calculate or rarely changes. Caching even for 5 min can save repeated queries, complex calculations, and infrequently changing queries.

from django.core.cache import cache

def get_popular_blogs():
    popular_blogs = cache.get('popular_blogs_cache_key')
    if popular_blogs is None:
        popular_blogs = Blog.objects.filter(views__gte=1000)
        cache.set('popular_blogs_cache_key', popular_blogs, 300)
    return popular_blogs

7. Raw SQL

Sometimes, the Django ORM cannot efficiently express a complex query or a bulk operation. While Django offers .extra() or .raw(), raw SQL usage should be a last resort because it:

• Loses many of the benefits of the ORM
• Can lead to unreadable or error-prone code

Ensuring that inputs are properly sanitized and keep raw SQL queries maintainable.

Applying these tips, you'll improve the performance of your Django app while keeping the code clean and maintainable. And also suggested to use Django Debug Toolbar in your development environment to monitor and analyze how many queries are executed, their execution time, and SQL statements.