How do I handle Python memory leaks and optimize garbage collection?
I'm working on a Python application and running into an issue with Python debugging. Here's the problematic code:
# Current implementation
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
# This causes RecursionError for large n
result = fibonacci(1000)
The error message I'm getting is: "AttributeError: 'NoneType' object has no attribute 'get'"
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!
Comments
joseph: Excellent solution! This fixed my Django N+1 query problem immediately. Performance improved by 80%. 1 week, 4 days ago
abdullah3: Great Python profiling example! The cProfile output helped me identify the bottleneck in my data processing pipeline. 1 week, 4 days ago
jane_smith: Have you considered using Django's async views for this use case? Might be more efficient for I/O operations. 1 week, 4 days 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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