Python: How to implement proper unit testing with mocking and fixtures?
I'm working on a Python application and running into an issue with Python performance. Here's the problematic code:
# Current implementation
import threading
import time
def worker():
global counter
for _ in range(100000):
counter += 1 # Race condition here
counter = 0
threads = [threading.Thread(target=worker) for _ in range(4)]
for t in threads:
t.start()
The error message I'm getting is: "ImportError: cannot import name 'some_function' from 'module'"
What I've tried so far:
- Used pdb debugger to step through the code
- Added logging statements to trace execution
- Checked Python documentation and PEPs
- Tested with different Python versions
- Reviewed similar issues on GitHub and Stack Overflow
Environment information:
- Python version: 3.11.0
- Operating system: Ubuntu 22.04
- Virtual environment: venv (activated)
- Relevant packages: django, djangorestframework, celery, redis
Any insights or alternative approaches would be very helpful. Thanks!
Comments
michael_code: Could you provide the requirements.txt for the packages used in this solution? 1 week, 4 days ago
lisa_data: Perfect! This JWT authentication setup works flawlessly with my React frontend. 1 week, 4 days ago
abdullah3: How would you modify this approach for a high-traffic production environment? 1 week, 4 days ago
2 Answers
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 functions2. 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.py3. 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.py4. 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}')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 tokenYour Answer
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