#Python's thread pool

Creating and terminating threads incurs overhead. If threads are repeatedly created and destroyed, program performance may suffer.

In such scenarios, thread reuse is often adopted—threads are not destroyed after finishing execution but are instead paused. When new tasks arise, previously paused threads are resumed to run the new code.

The object that manages these threads is called a thread pool. In Python, you can create a thread pool using the ThreadPoolExecutor class from the concurrent.futures module. The constructor parameter specifies the number of threads.

For CPU-bound tasks, it is generally recommended to set the number of threads to the number of logical CPU cores, which you can retrieve using the os.cpu_count function.

Example:

from concurrent.futures import ThreadPoolExecutor
import time

def task(name):
    print(f"Task {name} started")
    time.sleep(2)
    return f"Task {name} completed"

# Create a thread pool with 4 threads
with ThreadPoolExecutor(max_workers=4) as executor:
    # Submit tasks to the thread pool
    futures = [executor.submit(task, i) for i in range(10)]
    for future in futures:
        print(future.result())  # Get the results in order of completion