ProcessPoolExecutor de Python: un pool de procesos personalizado

"""Download flags of top 20 countries by population
ProcessPoolExecutor version
Sample run::
    $ python3 flags_threadpool.py    BD retrieved.    EG retrieved.    CN retrieved.    ...    PH retrieved.    
    US retrieved.    IR retrieved.    20 flags downloaded in 0.93s
"""
# BEGIN FLAGS_PROCESSPOOL

from concurrent import futures
from flags import save_flag, get_flag, show, main

MAX_WORKERS = 20

def download_one(cc):
    image = get_flag(cc)
    show(cc)
    save_flag(image, cc.lower() + '.gif')
    return cc


def download_many(cc_list):
    with futures.ProcessPoolExecutor() as executor:  # <1>        
        res = executor.map(download_one, sorted(cc_list))

    return len(list(res))


if __name__ == '__main__':
    main(download_many)
# END FLAGS_PROCESSPOOL

p = multiprocessing.Process(
        target=_process_worker,
        args=(self._call_queue,
              self._result_queue,
              self._initial_args,
              self._initial_kwargs))

class ProcessPoolExecutor(_base.Executor):
    def __init__(self, max_workers=None, *args, **kwargs):
        """Initializes a new ProcessPoolExecutor instance.
        Args:            max_workers: The maximum number of processes that can be used to               
         execute the given calls. If None or not given then as many                
         worker processes will be created as the machine has processors.        
        """        
        _check_system_limits()

        if max_workers is None:
            self._max_workers = multiprocessing.cpu_count()
        else:
            self._max_workers = max_workers

        # Make the call queue slightly larger than the number of processes to        
        # prevent the worker processes from idling. But don't make it too big        
        # because futures in the call queue cannot be cancelled.        
        self._call_queue = multiprocessing.Queue(self._max_workers + EXTRA_QUEUED_CALLS)
        self._result_queue = multiprocessing.Queue()
        self._work_ids = queue.Queue()
        self._queue_management_thread = None        
        self._processes = set()

        # Shutdown is a two-step process.        self._shutdown_thread = False        self._shutdown_lock = threading.Lock()
        self._queue_count = 0  
        self._pending_work_items = {}
        self._initial_args = args
        self._initial_kwargs = kwargs

def _adjust_process_count(self):
    for _ in range(len(self._processes), self._max_workers):
        p = multiprocessing.Process(
                target=_process_worker,
                args=(self._call_queue,
                      self._result_queue,
                      self._initial_args,
                      self._initial_kwargs))
        p.start()
        self._processes.add(p)

def _process_worker(call_queue, result_queue, args, kwargs):
    """Evaluates calls from call_queue and places the results in result_queue.
    This worker is run in a separate process.
    Args:        call_queue: A multiprocessing.Queue of _CallItems that will be read and            
    evaluated by the worker.        
    result_queue: A multiprocessing.Queue of _ResultItems that will written            
    to by the worker.        
    shutdown: A multiprocessing.Event that will be set as a signal to the            
    worker that it should exit when call_queue is empty.    
    """    
    while True:
        call_item = call_queue.get(block=True)
        if call_item is None:
            # Wake up queue management thread
            result_queue.put(None)
            return        
        try:
            args += call_item.args
            kwargs.update(**call_item.kwargs)
            r = call_item.fn(*args, **kwargs)
        except BaseException:
            e = sys.exc_info()[1]
            result_queue.put(_ResultItem(call_item.work_id, exception=e))
        else:
            result_queue.put(_ResultItem(call_item.work_id, result=r))