What are the best practices for using thread pools in Python?

What are the best practices for using thread pools in Python?

Using thread pools in Python can greatly enhance performance. Key practices include using the concurrent.futures.ThreadPoolExecutor, managing the number of threads based on your workload, and ensuring thread safety when accessing shared resources.

How do I determine the optimal number of threads for my application?

A good starting point is to use the number of available CPU cores as a baseline. You can also experiment with different thread counts and monitor performance to find the optimal number.

What are some common pitfalls to avoid when using thread pools?

Common pitfalls include not handling exceptions properly, creating too many threads which can lead to context switching overhead, and not ensuring thread safety when accessing shared data.

Can you give an example of using ThreadPoolExecutor?

Sure! Here's a simple example:

from concurrent.futures import ThreadPoolExecutor

def task(n):
    return n * n

with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(task, range(10)))
print(results)