How do I implement Django custom user model without breaking existing code?
I'm working on a Django project and encountering an issue with Django models. Here's my current implementation:
# models.py
from django.db import models
class UserProfile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE)
bio = models.TextField()
# Signal handler
@receiver(post_save, sender=User)
def create_profile(sender, instance, created, **kwargs):
if created:
UserProfile.objects.create(user=instance)
The specific error I'm getting is: "django.core.exceptions.ValidationError: Enter a valid email address"
I've already tried the following approaches:
- Checked Django documentation and Stack Overflow
- Verified my database schema and migrations
- Added debugging prints to trace the issue
- Tested with different data inputs
Environment details:
- Django version: 5.0.1
- Python version: 3.11.0
- Database: PostgreSQL 15
- Operating system: Ubuntu 22.04
Has anyone encountered this before? Any guidance would be greatly appreciated!
3 Answers
The choice between Django signals and overriding save() depends on your use case:
Use save() method when:
- The logic is directly related to the model
- You need to modify the instance before saving
- The operation is essential for data integrity
class Article(models.Model):
title = models.CharField(max_length=200)
slug = models.SlugField(unique=True)
def save(self, *args, **kwargs):
if not self.slug:
self.slug = slugify(self.title)
super().save(*args, **kwargs)
Use signals when:
- You need decoupled logic
- Multiple models need the same behavior
- You're working with third-party models
from django.db.models.signals import post_save
from django.dispatch import receiver
@receiver(post_save, sender=User)
def create_user_profile(sender, instance, created, **kwargs):
if created:
UserProfile.objects.create(user=instance)
Comments
joseph: This decorator pattern is exactly what I needed for my Django middleware. Much appreciated! 1 week, 4 days ago
Here's a comprehensive approach to implementing JWT authentication in Django REST Framework:
# settings.py
INSTALLED_APPS = [
'rest_framework',
'rest_framework_simplejwt',
]
REST_FRAMEWORK = {
'DEFAULT_AUTHENTICATION_CLASSES': (
'rest_framework_simplejwt.authentication.JWTAuthentication',
),
'DEFAULT_PERMISSION_CLASSES': [
'rest_framework.permissions.IsAuthenticated',
],
}
from datetime import timedelta
SIMPLE_JWT = {
'ACCESS_TOKEN_LIFETIME': timedelta(minutes=60),
'REFRESH_TOKEN_LIFETIME': timedelta(days=7),
'ROTATE_REFRESH_TOKENS': True,
}
# urls.py
from rest_framework_simplejwt.views import (
TokenObtainPairView,
TokenRefreshView,
)
urlpatterns = [
path('api/token/', TokenObtainPairView.as_view()),
path('api/token/refresh/', TokenRefreshView.as_view()),
]
# Custom serializer for additional user data
from rest_framework_simplejwt.serializers import TokenObtainPairSerializer
class CustomTokenObtainPairSerializer(TokenObtainPairSerializer):
@classmethod
def get_token(cls, user):
token = super().get_token(user)
token['username'] = user.username
token['email'] = user.email
return token
Comments
abdullah: Excellent solution! This fixed my Django N+1 query problem immediately. Performance improved by 80%. 1 week, 4 days ago
abaditaye: What about handling this in a Docker containerized environment? Any special considerations? 1 week, 4 days ago
Here's how to optimize Python code performance using profiling tools:
1. Use cProfile for function-level profiling:
import cProfile
import pstats
# Profile your code
cProfile.run('your_function()', 'profile_output.prof')
# Analyze results
stats = pstats.Stats('profile_output.prof')
stats.sort_stats('cumulative')
stats.print_stats(10) # Top 10 functions
2. Use line_profiler for line-by-line analysis:
# Install: pip install line_profiler
# Add @profile decorator to functions
@profile
def slow_function():
# Your code here
pass
# Run: kernprof -l -v script.py
3. Memory profiling with memory_profiler:
# Install: pip install memory_profiler
from memory_profiler import profile
@profile
def memory_intensive_function():
# Your code here
pass
# Run: python -m memory_profiler script.py
4. Use timeit for micro-benchmarks:
import timeit
# Compare different approaches
time1 = timeit.timeit('sum([1,2,3,4,5])', number=100000)
time2 = timeit.timeit('sum((1,2,3,4,5))', number=100000)
print(f'List: {time1}, Tuple: {time2}')
Comments
abdullah: This threading vs multiprocessing explanation cleared up my confusion. Saved me hours of debugging! 1 week, 4 days ago
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