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: "KeyError: 'missing_key'"

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!

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]

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}')