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__pycache__ dir drwxr-xr-x 2024-10-07 16:37 R D
  __init__.py 0.781 KB -rw-r--r-- 2018-12-23 21:37 R E G D
  _base.py 20.737 KB -rw-r--r-- 2018-12-23 21:37 R E G D
  process.py 20.012 KB -rw-r--r-- 2018-12-23 21:37 R E G D
  thread.py 5.382 KB -rw-r--r-- 2018-12-23 21:37 R E G D
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# Copyright 2009 Brian Quinlan. All Rights Reserved. # Licensed to PSF under a Contributor Agreement. """Implements ProcessPoolExecutor. The follow diagram and text describe the data-flow through the system: |======================= In-process =====================|== Out-of-process ==| +----------+ +----------+ +--------+ +-----------+ +---------+ | | => | Work Ids | => | | => | Call Q | => | | | | +----------+ | | +-----------+ | | | | | ... | | | | ... | | | | | | 6 | | | | 5, call() | | | | | | 7 | | | | ... | | | | Process | | ... | | Local | +-----------+ | Process | | Pool | +----------+ | Worker | | #1..n | | Executor | | Thread | | | | | +----------- + | | +-----------+ | | | | <=> | Work Items | <=> | | <= | Result Q | <= | | | | +------------+ | | +-----------+ | | | | | 6: call() | | | | ... | | | | | | future | | | | 4, result | | | | | | ... | | | | 3, except | | | +----------+ +------------+ +--------+ +-----------+ +---------+ Executor.submit() called: - creates a uniquely numbered _WorkItem and adds it to the "Work Items" dict - adds the id of the _WorkItem to the "Work Ids" queue Local worker thread: - reads work ids from the "Work Ids" queue and looks up the corresponding WorkItem from the "Work Items" dict: if the work item has been cancelled then it is simply removed from the dict, otherwise it is repackaged as a _CallItem and put in the "Call Q". New _CallItems are put in the "Call Q" until "Call Q" is full. NOTE: the size of the "Call Q" is kept small because calls placed in the "Call Q" can no longer be cancelled with Future.cancel(). - reads _ResultItems from "Result Q", updates the future stored in the "Work Items" dict and deletes the dict entry Process #1..n: - reads _CallItems from "Call Q", executes the calls, and puts the resulting _ResultItems in "Result Q" """ __author__ = 'Brian Quinlan (brian@sweetapp.com)' import atexit import os from concurrent.futures import _base import queue from queue import Full import multiprocessing from multiprocessing import SimpleQueue from multiprocessing.connection import wait import threading import weakref from functools import partial import itertools import traceback # Workers are created as daemon threads and processes. This is done to allow the # interpreter to exit when there are still idle processes in a # ProcessPoolExecutor's process pool (i.e. shutdown() was not called). However, # allowing workers to die with the interpreter has two undesirable properties: # - The workers would still be running during interpreter shutdown, # meaning that they would fail in unpredictable ways. # - The workers could be killed while evaluating a work item, which could # be bad if the callable being evaluated has external side-effects e.g. # writing to a file. # # To work around this problem, an exit handler is installed which tells the # workers to exit when their work queues are empty and then waits until the # threads/processes finish. _threads_queues = weakref.WeakKeyDictionary() _shutdown = False def _python_exit(): global _shutdown _shutdown = True items = list(_threads_queues.items()) for t, q in items: q.put(None) for t, q in items: t.join() # Controls how many more calls than processes will be queued in the call queue. # A smaller number will mean that processes spend more time idle waiting for # work while a larger number will make Future.cancel() succeed less frequently # (Futures in the call queue cannot be cancelled). EXTRA_QUEUED_CALLS = 1 # Hack to embed stringification of remote traceback in local traceback class _RemoteTraceback(Exception): def __init__(self, tb): self.tb = tb def __str__(self): return self.tb class _ExceptionWithTraceback: def __init__(self, exc, tb): tb = traceback.format_exception(type(exc), exc, tb) tb = ''.join(tb) self.exc = exc self.tb = '\n"""\n%s"""' % tb def __reduce__(self): return _rebuild_exc, (self.exc, self.tb) def _rebuild_exc(exc, tb): exc.__cause__ = _RemoteTraceback(tb) return exc class _WorkItem(object): def __init__(self, future, fn, args, kwargs): self.future = future self.fn = fn self.args = args self.kwargs = kwargs class _ResultItem(object): def __init__(self, work_id, exception=None, result=None): self.work_id = work_id self.exception = exception self.result = result class _CallItem(object): def __init__(self, work_id, fn, args, kwargs): self.work_id = work_id self.fn = fn self.args = args self.kwargs = kwargs def _get_chunks(*iterables, chunksize): """ Iterates over zip()ed iterables in chunks. """ it = zip(*iterables) while True: chunk = tuple(itertools.islice(it, chunksize)) if not chunk: return yield chunk def _process_chunk(fn, chunk): """ Processes a chunk of an iterable passed to map. Runs the function passed to map() on a chunk of the iterable passed to map. This function is run in a separate process. """ return [fn(*args) for args in chunk] def _process_worker(call_queue, result_queue): """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(os.getpid()) return try: r = call_item.fn(*call_item.args, **call_item.kwargs) except BaseException as e: exc = _ExceptionWithTraceback(e, e.__traceback__) result_queue.put(_ResultItem(call_item.work_id, exception=exc)) else: result_queue.put(_ResultItem(call_item.work_id, result=r)) def _add_call_item_to_queue(pending_work_items, work_ids, call_queue): """Fills call_queue with _WorkItems from pending_work_items. This function never blocks. Args: pending_work_items: A dict mapping work ids to _WorkItems e.g. {5: <_WorkItem...>, 6: <_WorkItem...>, ...} work_ids: A queue.Queue of work ids e.g. Queue([5, 6, ...]). Work ids are consumed and the corresponding _WorkItems from pending_work_items are transformed into _CallItems and put in call_queue. call_queue: A multiprocessing.Queue that will be filled with _CallItems derived from _WorkItems. """ while True: if call_queue.full(): return try: work_id = work_ids.get(block=False) except queue.Empty: return else: work_item = pending_work_items[work_id] if work_item.future.set_running_or_notify_cancel(): call_queue.put(_CallItem(work_id, work_item.fn, work_item.args, work_item.kwargs), block=True) else: del pending_work_items[work_id] continue def _queue_management_worker(executor_reference, processes, pending_work_items, work_ids_queue, call_queue, result_queue): """Manages the communication between this process and the worker processes. This function is run in a local thread. Args: executor_reference: A weakref.ref to the ProcessPoolExecutor that owns this thread. Used to determine if the ProcessPoolExecutor has been garbage collected and that this function can exit. process: A list of the multiprocessing.Process instances used as workers. pending_work_items: A dict mapping work ids to _WorkItems e.g. {5: <_WorkItem...>, 6: <_WorkItem...>, ...} work_ids_queue: A queue.Queue of work ids e.g. Queue([5, 6, ...]). call_queue: A multiprocessing.Queue that will be filled with _CallItems derived from _WorkItems for processing by the process workers. result_queue: A multiprocessing.Queue of _ResultItems generated by the process workers. """ executor = None def shutting_down(): return _shutdown or executor is None or executor._shutdown_thread def shutdown_worker(): # This is an upper bound nb_children_alive = sum(p.is_alive() for p in processes.values()) for i in range(0, nb_children_alive): call_queue.put_nowait(None) # Release the queue's resources as soon as possible. call_queue.close() # If .join() is not called on the created processes then # some multiprocessing.Queue methods may deadlock on Mac OS X. for p in processes.values(): p.join() reader = result_queue._reader while True: _add_call_item_to_queue(pending_work_items, work_ids_queue, call_queue) sentinels = [p.sentinel for p in processes.values()] assert sentinels ready = wait([reader] + sentinels) if reader in ready: result_item = reader.recv() else: # Mark the process pool broken so that submits fail right now. executor = executor_reference() if executor is not None: executor._broken = True executor._shutdown_thread = True executor = None # All futures in flight must be marked failed for work_id, work_item in pending_work_items.items(): work_item.future.set_exception( BrokenProcessPool( "A process in the process pool was " "terminated abruptly while the future was " "running or pending." )) # Delete references to object. See issue16284 del work_item pending_work_items.clear() # Terminate remaining workers forcibly: the