What is the concurrent.futures module and how does it simplify thread/process pools? concurrent.futures module क्या है और thread/process pools को कैसे simplify करता है?

concurrent.futures provides a high-level interface (ThreadPoolExecutor and ProcessPoolExecutor) for running tasks concurrently, abstracting away manual thread/process creation and management.

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed
import time

def fetch_url(url):
    time.sleep(1)  # simulating network delay
    return f'Data from {url}'

urls = ['url1', 'url2', 'url3', 'url4']

# ThreadPoolExecutor - good for I/O-bound tasks
with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(fetch_url, urls))
print(results)  # all 4 urls fetched concurrently, ~1s total instead of 4s

# Using submit() for more control, and as_completed() for results as they finish
with ThreadPoolExecutor(max_workers=4) as executor:
    futures = {executor.submit(fetch_url, url): url for url in urls}
    for future in as_completed(futures):
        url = futures[future]
        try:
            result = future.result()
            print(f'{url}: {result}')
        except Exception as e:
            print(f'{url} failed: {e}')

# ProcessPoolExecutor - same API, but for CPU-bound tasks (true parallelism)
def cpu_intensive(n):
    return sum(i * i for i in range(n))

if __name__ == '__main__':
    with ProcessPoolExecutor(max_workers=4) as executor:
        results = list(executor.map(cpu_intensive, [10_000_000] * 4))

# Advantage over raw threading/multiprocessing: consistent, simple API
# (.submit, .map, .result) that works the same way for both thread
# and process pools - just swap the executor class