Use Python Pool with context manager or close and join

Question

The Python documentation has examples in the format of

with Pool() as p:
    p.map(do)

but I see a lot of people using the format below.

p = Pool()
p.map(do)
p.close()
p.join()

Which is more desirable?

Answer 1

I think using Pool as a context manager (e.g., with ...) is desirable. It's a newer addition to Pool, and it lets you more cleanly encapsulate the lifespan of the pool.

One thing to be aware of is, that when the context manager exits, it will terminate the pool and any ongoing tasks. This means that you still want to do p.join() in some cases. Your example doesn't require this, because p.map will block execution until the task is done anyway:

A parallel equivalent of the map() built-in function (it supports only one iterable argument though). It blocks until the result is ready.

https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing.pool.Pool.map

Therefore, in the second example, the call to .join() is unnecessary, as .map() will block until all tasks have completed.

However, using .map_async would make .join useful:

with Pool() as p:
    p.map_async(do_something, range(100))
    # Do something else while tasks are running
    p.close()
    p.join()

Edit: as Facundo Olano points out, .close() must always be called before .join(), as stated in the docs:

Wait for the worker processes to exit. One must call close() or terminate() before using join().

https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing.pool.Pool.join