from multiprocessing import Process, Queue
from random import randint

def do_stuff(queue, table):
    """some irrelevant, long computation"""
    print(f"Doing table {table}")
    result = sum([randint(0, 40) for i in range(1_000_000)])
    # 4) When we are done, we send the result through the queue
    queue.put(result)

def main():
    # 0) The queue will be our communication channel
    queue = Queue()

    # 1) Create our list of processes
    tables = [i for i in range(40)]
    processes = []
    for table in tables:
        process = Process(target=do_stuff, args=(queue, table))
        process.start()
        processes.append((process, queue))

    # 2) we run them all
    out = []
    for process, queue in processes:
        # 3) individual processes block until they terminate
        process.join()  # 
        # 5) We now have a result we can append to a global state
        result = queue.get()
        out.append(result)
    print(out)

if __name__ == "__main__":
    main()

from multiprocessing import Pool
import multiprocessing
from random import randint

def do_stuff(table):
    """some irrelevant, long computation"""
    print(f"Doing table {table}")
    return sum([randint(0, 40) for i in range(1_000_000)])
  
def main():
    n_cores = multiprocessing.cpu_count()

    # Generate a list of some input data
    tables = [i for i in range(20)]

    # Create the process pool, with half the availables cores
    # https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool
    with Pool(n_cores//2) as pool:
        # same as map(do_stuff, tables), except
        # it’s using CPU processes and
        # splits the workload (tables) in pieces of size 'chunksize'.
        results = set(pool.map(do_stuff, tables, chunksize=1))
    print(f'{len(results)} results:')
    print(results)
 
if __name__ == '__main__':
    main()