How can I optimize performance in Python using concurrency?

How can I optimize performance in Python using concurrency?

You can optimize performance in Python using concurrency by leveraging modules like threading, multiprocessing, or asyncio. Each has its use cases depending on whether your tasks are I/O-bound or CPU-bound.

What are the differences between threading and multiprocessing?

Threading is suitable for I/O-bound tasks as it allows multiple threads to run concurrently, sharing the same memory space. Multiprocessing, on the other hand, is better for CPU-bound tasks as it creates separate processes, avoiding the Global Interpreter Lock (GIL) in Python.

Can you give me an example of using asyncio for concurrency?

Sure! Here's a simple example using asyncio:

import asyncio

async def fetch_data():
    await asyncio.sleep(1)  # Simulate I/O operation
    return 'Data fetched'

async def main():
    result = await fetch_data()
    print(result)

asyncio.run(main())

What are some common pitfalls to avoid when using concurrency in Python?

Common pitfalls include race conditions, deadlocks, and not properly managing shared resources. Always ensure thread safety and consider using locks or other synchronization mechanisms.