How can I optimize performance in concurrent futures?

How can I optimize performance in concurrent futures?

To optimize performance in concurrent futures, consider using a thread pool executor for I/O-bound tasks and a process pool executor for CPU-bound tasks.

What are the key differences between thread pool and process pool?

Thread pools use threads to manage tasks, which is efficient for I/O-bound tasks, while process pools use separate processes, making them better for CPU-bound tasks due to avoiding the Global Interpreter Lock (GIL).

How do I choose the right executor for my tasks?

Choose a thread pool executor for tasks that spend time waiting for external resources, and a process pool executor for tasks that require heavy computation.

Can you give me an example of using a thread pool executor?

Sure! Here's a simple example:

from concurrent.futures import ThreadPoolExecutor

def fetch_data(url):
    # Simulate a network call
    return f'Data from {url}'

with ThreadPoolExecutor(max_workers=5) as executor:
    results = executor.map(fetch_data, ['url1', 'url2', 'url3'])
    for result in results:
        print(result)