Django is a powerful web framework that enables developers to build robust applications quickly. However, improper use of Django’s features can lead to messy dependencies and bloated views, making applications difficult to maintain and scale. This article explores common mistakes that contribute to these issues, along with solutions and best practices.

1. Overloading Views with Business Logic

One of the most common mistakes in Django development is placing too much logic inside views. This leads to tightly coupled code, making it difficult to reuse components and test functionality.

Example of a logic-heavy view:

from django.shortcuts import render
from django.http import JsonResponse
from .models import Product
from .utils import apply_discount

def product_detail(request, product_id):
    product = Product.objects.get(id=product_id)
    user = request.user
    
    # Applying discount logic directly in the view
    if user.is_authenticated and user.has_premium:
        discounted_price = product.price * 0.9
    else:
        discounted_price = product.price
    
    data = {
        'name': product.name,
        'price': discounted_price,
        'description': product.description,
    }
    return JsonResponse(data)

Solution: Use Services or Utility Modules

Extract business logic into separate services or utility functions to improve maintainability.

# services/product_service.py

def get_discounted_price(product, user):
    if user.is_authenticated and user.has_premium:
        return product.price * 0.9
    return product.price

Now, modify the view to use the service:

from django.http import JsonResponse
from .models import Product
from .services.product_service import get_discounted_price

def product_detail(request, product_id):
    product = Product.objects.get(id=product_id)
    discounted_price = get_discounted_price(product, request.user)
    
    data = {
        'name': product.name,
        'price': discounted_price,
        'description': product.description,
    }
    return JsonResponse(data)

This keeps the view lean and makes the logic reusable across multiple views.

2. Hardcoding Dependencies in Views

Hardcoding dependencies directly in views makes testing and maintenance difficult.

Example of hardcoded dependency:

from django.core.mail import send_mail

def send_welcome_email(user):
    send_mail(
        'Welcome!',
        'Thanks for signing up.',
        'admin@example.com',
        [user.email],
        fail_silently=False,
    )

Solution: Use Dependency Injection

By injecting dependencies, we make the function more flexible and easier to test.

def send_welcome_email(user, email_service):
    email_service.send(
        subject='Welcome!',
        message='Thanks for signing up.',
        recipient_list=[user.email]
    )

Now we can pass different email services when calling this function, making testing and modifications easier.

3. Using Raw Queries When Django ORM Suffices

Django’s ORM is powerful and should be leveraged instead of writing raw SQL queries, which introduce unnecessary complexity and security risks.

Example of a raw query:

from django.db import connection

def get_active_users():
    with connection.cursor() as cursor:
        cursor.execute("SELECT * FROM users WHERE is_active=1")
        return cursor.fetchall()

Solution: Use Django ORM

from .models import User

def get_active_users():
    return User.objects.filter(is_active=True)

This makes the code more readable, secure, and compatible with Django’s built-in functionalities.

4. Not Using QuerySet Optimization Techniques

Fetching unnecessary data or executing redundant queries can slow down performance.

Example of inefficient queries:

def get_orders():
    orders = Order.objects.all()
    for order in orders:
        print(order.customer.name)  # Triggers a new DB query for each order

Solution: Use select_related or prefetch_related

def get_orders():
    orders = Order.objects.select_related('customer').all()
    for order in orders:
        print(order.customer.name)  # No extra queries per order

5. Not Using Django’s Built-in Features

Django provides various built-in features to simplify development, but many developers ignore them, leading to unnecessary custom implementations.

Example of custom pagination implementation:

def get_paginated_products(request):
    page = int(request.GET.get('page', 1))
    products = Product.objects.all()[(page-1)*10:page*10]
    return JsonResponse(list(products.values()), safe=False)

Solution: Use Django’s built-in Paginator

from django.core.paginator import Paginator

def get_paginated_products(request):
    paginator = Paginator(Product.objects.all(), 10)
    page_number = request.GET.get('page')
    page_obj = paginator.get_page(page_number)
    return JsonResponse(list(page_obj.object_list.values()), safe=False)

Conclusion

Avoiding messy dependencies and logic-heavy views is crucial for building maintainable and scalable Django applications. By applying best practices such as:

• Keeping views clean by delegating logic to services
• Using dependency injection instead of hardcoding dependencies
• Leveraging Django’s ORM instead of raw queries
• Optimizing queries with select_related and prefetch_related
• Utilizing Django’s built-in features rather than reinventing the wheel

You can write cleaner, more efficient Django applications that are easier to test, debug, and extend in the long run.

By following these principles, developers can ensure their code remains modular, reusable, and easy to adapt to future requirements. A well-structured Django project not only improves collaboration among team members but also facilitates long-term maintenance and scalability.

Ultimately, avoiding these common pitfalls will help you build applications that are robust, performant, and capable of handling growing user demands. Taking the time to refactor bloated views, optimize queries, and structure dependencies properly will pay off in reduced technical debt and increased developer productivity. Embracing Django’s best practices will lead to a codebase that is more intuitive, easier to debug, and more enjoyable to work with.