AlkantarClanX12
Your IP : 18.119.125.61
# 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 queues or their
# locks may be in a dirty state and block forever.
for p in processes.values():
p.terminate()
shutdown_worker()
return
if isinstance(result_item, int):
# Clean shutdown of a worker using its PID
# (avoids marking the executor broken)
assert shutting_down()
p = processes.pop(result_item)
p.join()
if not processes:
shutdown_worker()
return
elif result_item is not None:
work_item = pending_work_items.pop(result_item.work_id, None)
# work_item can be None if another process terminated (see above)
if work_item is not None:
if result_item.exception:
work_item.future.set_exception(result_item.exception)
else:
work_item.future.set_result(result_item.result)
# Delete references to object. See issue16284
del work_item
# Check whether we should start shutting down.
executor = executor_reference()
# No more work items can be added if:
# - The interpreter is shutting down OR
# - The executor that owns this worker has been collected OR
# - The executor that owns this worker has been shutdown.
if shutting_down():
try:
# Since no new work items can be added, it is safe to shutdown
# this thread if there are no pending work items.
if not pending_work_items:
shutdown_worker()
return
except Full:
# This is not a problem: we will eventually be woken up (in
# result_queue.get()) and be able to send a sentinel again.
pass
executor = None
_system_limits_checked = False
_system_limited = None
def _check_system_limits():
global _system_limits_checked, _system_limited
if _system_limits_checked:
if _system_limited:
raise NotImplementedError(_system_limited)
_system_limits_checked = True
try:
nsems_max = os.sysconf("SC_SEM_NSEMS_MAX")
except (AttributeError, ValueError):
# sysconf not available or setting not available
return
if nsems_max == -1:
# indetermined limit, assume that limit is determined
# by available memory only
return
if nsems_max >= 256:
# minimum number of semaphores available
# according to POSIX
return
_system_limited = "system provides too few semaphores (%d available, 256 necessary)" % nsems_max
raise NotImplementedError(_system_limited)
class BrokenProcessPool(RuntimeError):
"""
Raised when a process in a ProcessPoolExecutor terminated abruptly
while a future was in the running state.
"""
class ProcessPoolExecutor(_base.Executor):
def __init__(self, max_workers=None):
"""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 = os.cpu_count() or 1
else:
if max_workers <= 0:
raise ValueError("max_workers must be greater than 0")
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)
# Killed worker processes can produce spurious "broken pipe"
# tracebacks in the queue's own worker thread. But we detect killed
# processes anyway, so silence the tracebacks.
self._call_queue._ignore_epipe = True
self._result_queue = SimpleQueue()
self._work_ids = queue.Queue()
self._queue_management_thread = None
# Map of pids to processes
self._processes = {}
# Shutdown is a two-step process.
self._shutdown_thread = False
self._shutdown_lock = threading.Lock()
self._broken = False
self._queue_count = 0
self._pending_work_items = {}
def _start_queue_management_thread(self):
# When the executor gets lost, the weakref callback will wake up
# the queue management thread.
def weakref_cb(_, q=self._result_queue):
q.put(None)
if self._queue_management_thread is None:
# Start the processes so that their sentinels are known.
self._adjust_process_count()
self._queue_management_thread = threading.Thread(
target=_queue_management_worker,
args=(weakref.ref(self, weakref_cb),
self._processes,
self._pending_work_items,
self._work_ids,
self._call_queue,
self._result_queue))
self._queue_management_thread.daemon = True
self._queue_management_thread.start()
_threads_queues[self._queue_management_thread] = self._result_queue
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))
p.start()
self._processes[p.pid] = p
def submit(self, fn, *args, **kwargs):
with self._shutdown_lock:
if self._broken:
raise BrokenProcessPool('A child process terminated '
'abruptly, the process pool is not usable anymore')
if self._shutdown_thread:
raise RuntimeError('cannot schedule new futures after shutdown')
f = _base.Future()
w = _WorkItem(f, fn, args, kwargs)
self._pending_work_items[self._queue_count] = w
self._work_ids.put(self._queue_count)
self._queue_count += 1
# Wake up queue management thread
self._result_queue.put(None)
self._start_queue_management_thread()
return f
submit.__doc__ = _base.Executor.submit.__doc__
def map(self, fn, *iterables, timeout=None, chunksize=1):
"""Returns an iterator equivalent to map(fn, iter).
Args:
fn: A callable that will take as many arguments as there are
passed iterables.
timeout: The maximum number of seconds to wait. If None, then there
is no limit on the wait time.
chunksize: If greater than one, the iterables will be chopped into
chunks of size chunksize and submitted to the process pool.
If set to one, the items in the list will be sent one at a time.
Returns:
An iterator equivalent to: map(func, *iterables) but the calls may
be evaluated out-of-order.
Raises:
TimeoutError: If the entire result iterator could not be generated
before the given timeout.
Exception: If fn(*args) raises for any values.
"""
if chunksize < 1:
raise ValueError("chunksize must be >= 1.")
results = super().map(partial(_process_chunk, fn),
_get_chunks(*iterables, chunksize=chunksize),
timeout=timeout)
return itertools.chain.from_iterable(results)
def shutdown(self, wait=True):
with self._shutdown_lock:
self._shutdown_thread = True
if self._queue_management_thread:
# Wake up queue management thread
self._result_queue.put(None)
if wait:
self._queue_management_thread.join()
# To reduce the risk of opening too many files, remove references to
# objects that use file descriptors.
self._queue_management_thread = None
self._call_queue = None
self._result_queue = None
self._processes = None
shutdown.__doc__ = _base.Executor.shutdown.__doc__
atexit.register(_python_exit)