I'm working on a Python application and running into an issue with Python debugging. 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: "ValueError: invalid literal for int() with base 10: 'abc'"

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: macOS Ventura
• Virtual environment: venv (activated)
• Relevant packages: django, djangorestframework, celery, redis

Any insights or alternative approaches would be very helpful. Thanks!

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 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 functions

2. 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.py

3. 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.py

4. 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}')

Python decorators with arguments require a three-level nested function. Here's the proper implementation:

import functools

# Decorator with arguments
def retry(max_attempts=3, delay=1):
    def decorator(func):
        @functools.wraps(func)  # Preserves function metadata
        def wrapper(*args, **kwargs):
            for attempt in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_attempts - 1:
                        raise e
                    time.sleep(delay)
        return wrapper
    return decorator

# Usage
@retry(max_attempts=5, delay=2)
def unreliable_function():
    # Function that might fail
    pass

Class-based decorator (alternative approach):

class Retry:
    def __init__(self, max_attempts=3, delay=1):
        self.max_attempts = max_attempts
        self.delay = delay
    
    def __call__(self, func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(self.max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == self.max_attempts - 1:
                        raise e
                    time.sleep(self.delay)
        return wrapper

# Usage
@Retry(max_attempts=5, delay=2)
def another_function():
    pass

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]