I'm working on a Django project and encountering an issue with Django models. Here's my current implementation:

# models.py
class Article(models.Model):
    title = models.CharField(max_length=200)
    author = models.ForeignKey(User, on_delete=models.CASCADE)
    
    def save(self, *args, **kwargs):
        # This is causing issues
        super().save(*args, **kwargs)

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: macOS Ventura

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

To handle Django database transactions properly and avoid data inconsistency, use Django's transaction management:

from django.db import transaction

# Method 1: Decorator
@transaction.atomic
def transfer_money(from_account, to_account, amount):
    from_account.balance -= amount
    from_account.save()
    
    to_account.balance += amount
    to_account.save()

# Method 2: Context manager
def complex_operation():
    with transaction.atomic():
        # All operations in this block are atomic
        user = User.objects.create(username='test')
        profile = UserProfile.objects.create(user=user)
        # If any operation fails, all are rolled back

For more complex scenarios with savepoints:

def nested_transactions():
    with transaction.atomic():
        # Outer transaction
        user = User.objects.create(username='test')
        
        try:
            with transaction.atomic():
                # Inner transaction (savepoint)
                risky_operation()
        except Exception:
            # Inner transaction rolled back, outer continues
            handle_error()

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

To handle Django database transactions properly and avoid data inconsistency, use Django's transaction management:

from django.db import transaction

# Method 1: Decorator
@transaction.atomic
def transfer_money(from_account, to_account, amount):
    from_account.balance -= amount
    from_account.save()
    
    to_account.balance += amount
    to_account.save()

# Method 2: Context manager
def complex_operation():
    with transaction.atomic():
        # All operations in this block are atomic
        user = User.objects.create(username='test')
        profile = UserProfile.objects.create(user=user)
        # If any operation fails, all are rolled back

For more complex scenarios with savepoints:

def nested_transactions():
    with transaction.atomic():
        # Outer transaction
        user = User.objects.create(username='test')
        
        try:
            with transaction.atomic():
                # Inner transaction (savepoint)
                risky_operation()
        except Exception:
            # Inner transaction rolled back, outer continues
            handle_error()

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]

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]