How can I implement Django caching with Redis for better performance?
I'm working on a Django project and encountering an issue with Django authentication. 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.db.utils.IntegrityError: UNIQUE constraint failed: auth_user.username"
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
michael_code: I'm getting a similar error but with PostgreSQL instead of SQLite. Any differences in the solution? 2 months ago
sarah_tech: Have you considered using Django's async views for this use case? Might be more efficient for I/O operations. 2 months ago
jane_smith: Could you elaborate on the select_related vs prefetch_related usage? When should I use each? 2 months ago
1 Answer
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]Your Answer
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