Standard Library

Threads and Processes

Threads share memory, while processes run in separate interpreters.

ThreadPoolExecutor runs square across worker threads that share this interpreter and its GIL; map() returns results in input order, and the with block joins the workers when the body exits. The in-browser sandbox cannot create native threads, so pressing Run here fails; this thread-pool output was produced under standard CPython at build time.

Source

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor


def square(number):
    return number * number

with ThreadPoolExecutor(max_workers=2) as pool:
    print(list(pool.map(square, [1, 2, 3])))

Output

[1, 4, 9]

ProcessPoolExecutor is the heavier boundary: separate Python processes with isolated memory, for CPU-bound work that the GIL would otherwise serialise. The sandbox cannot spawn processes either, so this cell only inspects the class name rather than running a pool.

Source

print(ProcessPoolExecutor.__name__)

Output

ProcessPoolExecutor

Notes

Threads share memory, so mutable shared state needs care.
Processes avoid shared interpreter state but require values to cross a process boundary.
Prefer asyncio for coroutine-based I/O and executors for ordinary blocking callables.
The thread-pool output came from real worker threads under standard CPython at build time; the in-browser sandbox cannot create threads or processes, so live runs of this page fail there.

Run the complete example

Expected output

[1, 4, 9]
ProcessPoolExecutor

Execution time appears here after you run the example.

Threads and Processes

See also

Run the complete example

Example code

Expected output