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：

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。

as_completed()源码，理解略有困难。

ProcessExecutorPool()的实现：

process.png

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。