I'm working on a Django project and encountering an issue with Django forms. 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.urls.exceptions.NoReverseMatch: Reverse for 'article_detail' not found"

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: macOS Ventura

Has anyone encountered this before? Any guidance would be greatly appreciated!

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)

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)

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

The difference between threading and multiprocessing in Python is crucial for performance:

Threading (shared memory, GIL limitation):

import threading
import time

def io_bound_task(name):
    print(f'Starting {name}')
    time.sleep(2)  # Simulates I/O operation
    print(f'Finished {name}')

# Good for I/O-bound tasks
threads = []
for i in range(3):
    t = threading.Thread(target=io_bound_task, args=(f'Task-{i}',))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

Multiprocessing (separate memory, no GIL):

import multiprocessing
import time

def cpu_bound_task(name):
    # CPU-intensive calculation
    result = sum(i * i for i in range(1000000))
    return f'{name}: {result}'

# Good for CPU-bound tasks
if __name__ == '__main__':
    with multiprocessing.Pool(processes=4) as pool:
        tasks = [f'Process-{i}' for i in range(4)]
        results = pool.map(cpu_bound_task, tasks)
        print(results)

Concurrent.futures (unified interface):

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

# For I/O-bound tasks
with ThreadPoolExecutor(max_workers=4) as executor:
    futures = [executor.submit(io_bound_task, f'Task-{i}') for i in range(4)]
    results = [future.result() for future in futures]

# For CPU-bound tasks
with ProcessPoolExecutor(max_workers=4) as executor:
    futures = [executor.submit(cpu_bound_task, f'Process-{i}') for i in range(4)]
    results = [future.result() for future in futures]