理解Python的PoolExecutor

Demo代码和引用知识点都参考自《理解Python并发编程一篇就够了|PoolExecutor篇》–董伟明或作者个人公众号Python之美， 《Python Cookbook》和Python并发编程之线程池/进程池。

ThreadPoolExecutor

和

ProcessPoolExecutor

分别对

threading

和

multiprocessing

进行了高级抽象，暴露出简单的统一接口。

通过

ProcessPoolExecutor

来做示例。

主要提供两个方法

map()

和

submit()

。

map()

方法主要用来针对简化执行相同的方法，如下例：

# -*- coding: utf-8 -*-

from concurrent.futures import ProcessPoolExecutor

def fib(n, test_arg):
if n > 30:
raise Exception('can not > 30, now %s' % n)
if n <= 2:
return 1
return fib(n-1, test_arg) + fib(n-2, test_arg)

def use_map():
nums = [random.randint(0, 33) for _ in range(0, 10)]
with ProcessPoolExecutor() as executor:
try:
results = executor.map(fib, nums, nums)
for num, result in zip(nums, results):
print('fib(%s) result is %s.' % (num, result))
except Exception as e:
print(e)

执行上例，输出如下，当

num

为30时抛出异常捕获后程序停止运行。

...
fib(19) result is 4181.
fib(11) result is 89.
fib(2) result is 1.
fib(5) result is 5.
fib(24) result is 46368.
fib(2) result is 1.
can not > 30, now 33

使用

submit()

方法。

# -*- coding: utf-8 -*-

from concurrent.futures import ProcessPoolExecutor, as_completed
import random

def fib(n, test_arg):
if n > 30:
raise Exception('can not > 30, now %s' % n)
if n <= 2:
return 1
return fib(n-1, test_arg) + fib(n-2, test_arg)

def use_submit():
nums = [random.randint(0, 33) for _ in range(0, 10)]
with ProcessPoolExecutor() as executor:
futures = {executor.submit(fib, n, n): n for n in nums}
for f in as_completed(futures):
try:
print('fib(%s) result is %s.' % (futures[f], f.result()))
except Exception as e:
print(e)

执行上例，输出如下，可见当抛出异常并捕获后，继续向后输出，并没有向

map()

一样停止，除了

as_completed()

，还有

wait()

等方法。

fib(3) result is 2.
fib(15) result is 610.
can not > 30, now 31
fib(23) result is 28657.
fib(1) result is 1.
can not > 30, now 32
fib(14) result is 377.
fib(12) result is 144.
fib(26) result is 121393.
fib(29) result is 514229.

但

try/except

的代码块包括

as_completed()

则不会继续输出，直接停止，暂时未找到原因。

def use_submit():
nums = [random.randint(0, 33) for _ in range(0, 10)]
with ProcessPoolExecutor() as executor:
futures = {executor.submit(fib, n, n): n for n in nums}
try:
for f in as_completed(futures):
print('fib(%s) result is %s.' % (futures[f], f.result()))
except Exception as e:
print(e)

其他：

1.

map()

是根据传入的参数然后顺序输出的，

as_completed()

是按完成时间输出的，上面的例子不明显，可以参考Python并发编程之线程池/进程池，但都跟

max_workers

参数和方法执行时间挂钩。

import time
def test_sleep(n):
time.sleep(n)
return True
def use_submit():
nums = [3, 2, 1, 3]
with ProcessPoolExecutor(max_workers=3) as executor:
futures = {executor.submit(test_sleep, n): n for n in nums}
for f in as_completed(futures):
try:
print('%s result is %s.' % (futures[f], f.result()))
except Exception as e:
print(e)
def use_map():
nums = [3, 2, 1, 3]
with ProcessPoolExecutor(max_workers=3) as executor:
try:
results = executor.map(test_sleep, nums)
for num, result in zip(nums, results):
print('%s result is %s.' % (num, result))
except Exception as e:
print(e)

use_submit()

输出如下，耗时3+1=4s，且完成一个输出一个，指定

max_workers=3

，第一个耗时1s的完成后就会执行第四个耗时3s的任务。

1 result is True.
2 result is True.
3 result is True.
3 result is True.

use_map()

输出如下，同样是耗时3+1=4s，但是是按传入参数顺序输入，因为指定

max_workers=3

，所以前三个是在耗时3s后一起输出的，第四个在耗时4s后再输出。

3 result is True.
2 result is True.
1 result is True.
3 result is True.

阅读部分

map()

源码。

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: The size of the chunks the iterable will be broken into
before being passed to a child process. This argument is only
used by ProcessPoolExecutor; it is ignored by
ThreadPoolExecutor.

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 timeout is not None:
end_time = timeout + time.time()

fs = [self.submit(fn, *args) for args in zip(*iterables)]

# Yield must be hidden in closure so that the futures are submitted
# before the first iterator value is required.
def result_iterator():
try:
for future in fs:
if timeout is None:
yield future.result()
else:
yield future.result(end_time - time.time())
finally:
for future in fs:
future.cancel()
return result_iterator()

fs

存放了

submit()

后返回的

future

实例，是按传入的参数顺序排序的，返回了

result_iterator()

。至于为什么会按

max_workers

数一组返回输出，暂时不清楚。

3.

as_completed()

源码，理解略有困难。

4.

ProcessExecutorPool()

的实现：



我们结合源码和上面的数据流分析一下：

executor.map会创建多个_WorkItem对象(ps. 实际上是执行了多次submit())，每个对象都传入了新创建的一个Future对象。

把每个_WorkItem对象然后放进一个叫做「Work Items」的dict中，键是不同的「Work Ids」。

创建一个管理「Work Ids」队列的线程「Local worker thread」，它能做2件事：

从「Work Ids」队列中获取Work Id, 通过「Work Items」找到对应的_WorkItem。如果这个Item被取消了，就从「Work Items」里面把它删掉，否则重新打包成一个_CallItem放入「Call Q」这个队列。executor的那些进程会从队列中取_CallItem执行，并把结果封装成_ResultItems放入「Result Q」队列中。

从「Result Q」队列中获取_ResultItems，然后从「Work Items」更新对应的Future对象并删掉入口。

简单分析

submit()

。

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

创建

Future()

实例

f

，和

_WorkItem()

实例

w

。

_pending_work_items

即上述所说的

Work Items

字典，key为

_queue_count

，初始化为0；value为

w

。并将

_queue_count

添加到

_work_ids

队列中。

Wake up queue management thread

，即唤醒上述图中的

Local Work Thread

。

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

_adjust_process_count()

开启

max_wokers

个进程，执行

_process_worker()

。

开启

_queue_management_thread()

线程，即Local Worker Thread。

_queue_management_thread()

线程中将调用

_add_call_item_to_queue()

将

_CallItem

置于

call_queue

，并删除引用等操作，该方法理解有困难。

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))

执行任务进程，从

call_queue

中获取

_CallItem

并调用其

fn

，将结果放进

result_queue

中。