mirror of
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364 lines
15 KiB
Python
364 lines
15 KiB
Python
# Copyright 2009 Brian Quinlan. All Rights Reserved.
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# Licensed to PSF under a Contributor Agreement.
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"""Implements ProcessPoolExecutor.
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The follow diagram and text describe the data-flow through the system:
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|======================= In-process =====================|== Out-of-process ==|
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+----------+ +----------+ +--------+ +-----------+ +---------+
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| | => | Work Ids | => | | => | Call Q | => | |
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| | +----------+ | | +-----------+ | |
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| | | ... | | | | ... | | |
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| | | 6 | | | | 5, call() | | |
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| | | 7 | | | | ... | | |
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| Process | | ... | | Local | +-----------+ | Process |
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| Pool | +----------+ | Worker | | #1..n |
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| Executor | | Thread | | |
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| | +----------- + | | +-----------+ | |
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| | <=> | Work Items | <=> | | <= | Result Q | <= | |
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| | +------------+ | | +-----------+ | |
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| | | 6: call() | | | | ... | | |
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| | | future | | | | 4, result | | |
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| | | ... | | | | 3, except | | |
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+----------+ +------------+ +--------+ +-----------+ +---------+
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Executor.submit() called:
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- creates a uniquely numbered _WorkItem and adds it to the "Work Items" dict
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- adds the id of the _WorkItem to the "Work Ids" queue
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Local worker thread:
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- reads work ids from the "Work Ids" queue and looks up the corresponding
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WorkItem from the "Work Items" dict: if the work item has been cancelled then
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it is simply removed from the dict, otherwise it is repackaged as a
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_CallItem and put in the "Call Q". New _CallItems are put in the "Call Q"
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until "Call Q" is full. NOTE: the size of the "Call Q" is kept small because
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calls placed in the "Call Q" can no longer be cancelled with Future.cancel().
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- reads _ResultItems from "Result Q", updates the future stored in the
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"Work Items" dict and deletes the dict entry
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Process #1..n:
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- reads _CallItems from "Call Q", executes the calls, and puts the resulting
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_ResultItems in "Request Q"
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"""
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import atexit
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from concurrent.futures import _base
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import Queue as queue
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import multiprocessing
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import threading
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import weakref
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import sys
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__author__ = 'Brian Quinlan (brian@sweetapp.com)'
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# Workers are created as daemon threads and processes. This is done to allow the
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# interpreter to exit when there are still idle processes in a
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# ProcessPoolExecutor's process pool (i.e. shutdown() was not called). However,
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# allowing workers to die with the interpreter has two undesirable properties:
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# - The workers would still be running during interpretor shutdown,
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# meaning that they would fail in unpredictable ways.
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# - The workers could be killed while evaluating a work item, which could
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# be bad if the callable being evaluated has external side-effects e.g.
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# writing to a file.
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#
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# To work around this problem, an exit handler is installed which tells the
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# workers to exit when their work queues are empty and then waits until the
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# threads/processes finish.
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_threads_queues = weakref.WeakKeyDictionary()
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_shutdown = False
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def _python_exit():
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global _shutdown
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_shutdown = True
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items = list(_threads_queues.items()) if _threads_queues else ()
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for t, q in items:
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q.put(None)
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for t, q in items:
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t.join(sys.maxint)
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# Controls how many more calls than processes will be queued in the call queue.
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# A smaller number will mean that processes spend more time idle waiting for
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# work while a larger number will make Future.cancel() succeed less frequently
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# (Futures in the call queue cannot be cancelled).
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EXTRA_QUEUED_CALLS = 1
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class _WorkItem(object):
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def __init__(self, future, fn, args, kwargs):
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self.future = future
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self.fn = fn
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self.args = args
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self.kwargs = kwargs
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class _ResultItem(object):
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def __init__(self, work_id, exception=None, result=None):
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self.work_id = work_id
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self.exception = exception
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self.result = result
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class _CallItem(object):
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def __init__(self, work_id, fn, args, kwargs):
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self.work_id = work_id
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self.fn = fn
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self.args = args
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self.kwargs = kwargs
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def _process_worker(call_queue, result_queue):
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"""Evaluates calls from call_queue and places the results in result_queue.
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This worker is run in a separate process.
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Args:
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call_queue: A multiprocessing.Queue of _CallItems that will be read and
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evaluated by the worker.
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result_queue: A multiprocessing.Queue of _ResultItems that will written
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to by the worker.
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shutdown: A multiprocessing.Event that will be set as a signal to the
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worker that it should exit when call_queue is empty.
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"""
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while True:
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call_item = call_queue.get(block=True)
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if call_item is None:
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# Wake up queue management thread
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result_queue.put(None)
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return
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try:
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r = call_item.fn(*call_item.args, **call_item.kwargs)
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except:
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e = sys.exc_info()[1]
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result_queue.put(_ResultItem(call_item.work_id,
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exception=e))
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else:
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result_queue.put(_ResultItem(call_item.work_id,
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result=r))
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def _add_call_item_to_queue(pending_work_items,
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work_ids,
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call_queue):
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"""Fills call_queue with _WorkItems from pending_work_items.
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This function never blocks.
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Args:
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pending_work_items: A dict mapping work ids to _WorkItems e.g.
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{5: <_WorkItem...>, 6: <_WorkItem...>, ...}
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work_ids: A queue.Queue of work ids e.g. Queue([5, 6, ...]). Work ids
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are consumed and the corresponding _WorkItems from
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pending_work_items are transformed into _CallItems and put in
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call_queue.
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call_queue: A multiprocessing.Queue that will be filled with _CallItems
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derived from _WorkItems.
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"""
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while True:
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if call_queue.full():
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return
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try:
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work_id = work_ids.get(block=False)
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except queue.Empty:
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return
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else:
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work_item = pending_work_items[work_id]
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if work_item.future.set_running_or_notify_cancel():
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call_queue.put(_CallItem(work_id,
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work_item.fn,
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work_item.args,
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work_item.kwargs),
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block=True)
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else:
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del pending_work_items[work_id]
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continue
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def _queue_management_worker(executor_reference,
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processes,
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pending_work_items,
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work_ids_queue,
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call_queue,
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result_queue):
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"""Manages the communication between this process and the worker processes.
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This function is run in a local thread.
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Args:
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executor_reference: A weakref.ref to the ProcessPoolExecutor that owns
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this thread. Used to determine if the ProcessPoolExecutor has been
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garbage collected and that this function can exit.
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process: A list of the multiprocessing.Process instances used as
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workers.
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pending_work_items: A dict mapping work ids to _WorkItems e.g.
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{5: <_WorkItem...>, 6: <_WorkItem...>, ...}
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work_ids_queue: A queue.Queue of work ids e.g. Queue([5, 6, ...]).
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call_queue: A multiprocessing.Queue that will be filled with _CallItems
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derived from _WorkItems for processing by the process workers.
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result_queue: A multiprocessing.Queue of _ResultItems generated by the
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process workers.
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"""
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nb_shutdown_processes = [0]
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def shutdown_one_process():
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"""Tell a worker to terminate, which will in turn wake us again"""
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call_queue.put(None)
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nb_shutdown_processes[0] += 1
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while True:
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_add_call_item_to_queue(pending_work_items,
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work_ids_queue,
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call_queue)
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result_item = result_queue.get(block=True)
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if result_item is not None:
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work_item = pending_work_items[result_item.work_id]
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del pending_work_items[result_item.work_id]
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if result_item.exception:
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work_item.future.set_exception(result_item.exception)
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else:
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work_item.future.set_result(result_item.result)
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# Delete references to object. See issue16284
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del work_item
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# Check whether we should start shutting down.
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executor = executor_reference()
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# No more work items can be added if:
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# - The interpreter is shutting down OR
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# - The executor that owns this worker has been collected OR
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# - The executor that owns this worker has been shutdown.
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if _shutdown or executor is None or executor._shutdown_thread:
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# Since no new work items can be added, it is safe to shutdown
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# this thread if there are no pending work items.
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if not pending_work_items:
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while nb_shutdown_processes[0] < len(processes):
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shutdown_one_process()
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# If .join() is not called on the created processes then
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# some multiprocessing.Queue methods may deadlock on Mac OS
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# X.
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for p in processes:
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p.join()
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call_queue.close()
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return
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del executor
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_system_limits_checked = False
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_system_limited = None
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def _check_system_limits():
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global _system_limits_checked, _system_limited
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if _system_limits_checked:
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if _system_limited:
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raise NotImplementedError(_system_limited)
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_system_limits_checked = True
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try:
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import os
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nsems_max = os.sysconf("SC_SEM_NSEMS_MAX")
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except (AttributeError, ValueError):
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# sysconf not available or setting not available
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return
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if nsems_max == -1:
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# indetermine limit, assume that limit is determined
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# by available memory only
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return
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if nsems_max >= 256:
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# minimum number of semaphores available
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# according to POSIX
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return
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_system_limited = "system provides too few semaphores (%d available, 256 necessary)" % nsems_max
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raise NotImplementedError(_system_limited)
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class ProcessPoolExecutor(_base.Executor):
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def __init__(self, max_workers=None):
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"""Initializes a new ProcessPoolExecutor instance.
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Args:
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max_workers: The maximum number of processes that can be used to
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execute the given calls. If None or not given then as many
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worker processes will be created as the machine has processors.
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"""
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_check_system_limits()
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if max_workers is None:
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self._max_workers = multiprocessing.cpu_count()
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else:
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if max_workers <= 0:
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raise ValueError("max_workers must be greater than 0")
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self._max_workers = max_workers
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# Make the call queue slightly larger than the number of processes to
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# prevent the worker processes from idling. But don't make it too big
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# because futures in the call queue cannot be cancelled.
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self._call_queue = multiprocessing.Queue(self._max_workers +
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EXTRA_QUEUED_CALLS)
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self._result_queue = multiprocessing.Queue()
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self._work_ids = queue.Queue()
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self._queue_management_thread = None
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self._processes = set()
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# Shutdown is a two-step process.
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self._shutdown_thread = False
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self._shutdown_lock = threading.Lock()
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self._queue_count = 0
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self._pending_work_items = {}
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def _start_queue_management_thread(self):
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# When the executor gets lost, the weakref callback will wake up
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# the queue management thread.
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def weakref_cb(_, q=self._result_queue):
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q.put(None)
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if self._queue_management_thread is None:
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self._queue_management_thread = threading.Thread(
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target=_queue_management_worker,
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args=(weakref.ref(self, weakref_cb),
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self._processes,
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self._pending_work_items,
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self._work_ids,
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self._call_queue,
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self._result_queue))
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self._queue_management_thread.daemon = True
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self._queue_management_thread.start()
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_threads_queues[self._queue_management_thread] = self._result_queue
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def _adjust_process_count(self):
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for _ in range(len(self._processes), self._max_workers):
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p = multiprocessing.Process(
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target=_process_worker,
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args=(self._call_queue,
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self._result_queue))
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p.start()
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self._processes.add(p)
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def submit(self, fn, *args, **kwargs):
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with self._shutdown_lock:
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if self._shutdown_thread:
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raise RuntimeError('cannot schedule new futures after shutdown')
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f = _base.Future()
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w = _WorkItem(f, fn, args, kwargs)
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self._pending_work_items[self._queue_count] = w
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self._work_ids.put(self._queue_count)
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self._queue_count += 1
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# Wake up queue management thread
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self._result_queue.put(None)
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self._start_queue_management_thread()
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self._adjust_process_count()
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return f
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submit.__doc__ = _base.Executor.submit.__doc__
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def shutdown(self, wait=True):
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with self._shutdown_lock:
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self._shutdown_thread = True
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if self._queue_management_thread:
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# Wake up queue management thread
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self._result_queue.put(None)
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if wait:
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self._queue_management_thread.join(sys.maxint)
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# To reduce the risk of openning too many files, remove references to
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# objects that use file descriptors.
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self._queue_management_thread = None
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self._call_queue = None
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self._result_queue = None
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self._processes = None
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shutdown.__doc__ = _base.Executor.shutdown.__doc__
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atexit.register(_python_exit)
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