How can I implement custom Django middleware for request/response processing?
I'm working on a Django project and encountering an issue with Django forms. Here's my current implementation:
# models.py
# views.py
from django.shortcuts import render
from .models import Article
def article_list(request):
articles = Article.objects.all()
for article in articles:
print(article.author.username) # N+1 problem here
return render(request, 'articles.html', {'articles': articles})
The specific error I'm getting is: "django.db.utils.DataError: value too long for type character varying(100)"
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!
Comments
david_web: How would you modify this approach for a high-traffic production environment? 1 week, 4 days ago
joseph: What about handling this in a Docker containerized environment? Any special considerations? 1 week, 4 days ago
azzani: Perfect! This JWT authentication setup works flawlessly with my React frontend. 1 week, 4 days ago
2 Answers
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 tokenThe 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]Your Answer
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