摘要： 之前我们使用多线程(threading)和多进程(multiprocessing)完成常规的需求，在启动的时候start、jon等步骤不能省，复杂的需要还要用1-2个队列。随着需求越来越复杂，如果没有良好的设计和抽象这部分的功能层次，代码量越多调试的难度就越大。

之前我们使用多线程(threading)和多进程(multiprocessing)完成常规的需求，在启动的时候start、jon等步骤不能省，复杂的需要还要用1-2个队列。随着需求越来越复杂，如果没有良好的设计和抽象这部分的功能层次，代码量越多调试的难度就越大。有没有什么好的方法把这些步骤抽象一下呢，让我们不关注这些细节，轻装上阵呢？

答案是：有的。

从Python3.2开始一个叫做concurrent.futures被纳入了标准库，而在Python2它属于第三方的futures库，需要手动安装：

pip install futures```                                             这个模块中有2个类：ThreadPoolExecutor和ProcessPoolExecutor，也就是对threading和multiprocessing的进行了高级别的抽象，暴露出统一的接口，帮助开发者非常方便的实现异步调用：```pythonimport timefrom concurrent.futures import ProcessPoolExecutor, as_completedNUMBERS = range(25, 38) def fib(n): if n<= 2: return 1 return fib(n-1) + fib(n-2) start = time.time() with ProcessPoolExecutor(max_workers=3) as executor: for num, result in zip(NUMBERS, executor.map(fib, NUMBERS)): print 'fib({}) = {}'.format(num, result) print 'COST: {}'.format(time.time() - start)

感受下是不是很轻便呢？看一下花费的时间：

python fib_executor.pyfib(25) = 75025 fib(26) = 121393 fib(27) = 196418 fib(28) = 317811 fib(29) = 514229 fib(30) = 832040 fib(31) = 1346269 fib(32) = 2178309 fib(33) = 3524578 fib(34) = 5702887 fib(35) = 9227465 fib(36) = 14930352 fib(37) = 24157817 COST: 10.8920350075

除了用map，另外一个常用的方法是submit。如果你要提交的任务的函数是一样的，就可以简化成map。但是假如提交的任务函数是不一样的，或者执行的过程之可能出现异常（使用map执行过程中发现问题会直接抛出错误）就要用到submit：

from concurrent.futures import ThreadPoolExecutor, as_completedNUMBERS = range(30, 35) def fib(n): if n == 34: raise Exception("Don't do this") if n<= 2: return 1 return fib(n-1) + fib(n-2) with ThreadPoolExecutor(max_workers=3) as executor: future_to_num = {executor.submit(fib, num): num for num in NUMBERS} for future in as_completed(future_to_num): num = future_to_num[future] try: result = future.result() except Exception as e: print 'raise an exception: {}'.format(e) else: print 'fib({}) = {}'.format(num, result) with ThreadPoolExecutor(max_workers=3) as executor: for num, result in zip(NUMBERS, executor.map(fib, NUMBERS)): print 'fib({}) = {}'.format(num, result)

执一下：

python fib_executor_with_raise.pyfib(30) = 832040 fib(31) = 1346269 raise an exception: Don't do this fib(32) = 2178309 fib(33) = 3524578 Traceback (most recent call last): File "fib_executor_with_raise.py", line 28, in 
     
       for num, result in zip(NUMBERS, executor.map(fib, NUMBERS)): File "/Library/Python/2.7/site-packages/concurrent/futures/_base.py", line 580, in map yield future.result() File "/Library/Python/2.7/site-packages/concurrent/futures/_base.py", line 400, in result return self.__get_result() File "/Library/Python/2.7/site-packages/concurrent/futures/_base.py", line 359, in __get_result reraise(self._exception, self._traceback) File "/Library/Python/2.7/site-packages/concurrent/futures/_compat.py", line 107, in reraise exec('raise exc_type, exc_value, traceback', { }, locals_) File "/Library/Python/2.7/site-packages/concurrent/futures/thread.py", line 61, in run result = self.fn(*self.args, **self.kwargs) File "fib_executor_with_raise.py", line 9, in fib raise Exception("Don't do this") Exception: Don't do this
     

可以看到，第一次捕捉到了异常，但是第二次执行的时候错误直接抛出来了。

上面说到的map，有些同学马上会说，这不是进程（线程）池的效果吗？看起来确实是的：

import timefrom multiprocessing.pool import PoolNUMBERS = range(25, 38) def fib(n): if n<= 2: return 1 return fib(n-1) + fib(n-2) start = time.time() pool = Pool(3) results = pool.map(fib, NUMBERS) for num, result in zip(NUMBERS, pool.map(fib, NUMBERS)): print 'fib({}) = {}'.format(num, result) print 'COST: {}'.format(time.time() - start)

好像代码量更小哟。好吧，看一下花费的时间：

python fib_pool.pyfib(25) = 75025fib(26) = 121393fib(27) = 196418fib(28) = 317811fib(29) = 514229fib(30) = 832040fib(31) = 1346269fib(32) = 2178309fib(33) = 3524578fib(34) = 5702887fib(35) = 9227465fib(36) = 14930352fib(37) = 24157817COST: 17.1342718601

WhatTF竟然花费了1.7倍的时间。为什么？

BTW，有兴趣的同学可以对比下ThreadPool和ThreadPoolExecutor，由于GIL的缘故，对比的差距一定会更多。

原理

我们就拿ProcessPoolExecutor介绍下它的原理，引用官方代码注释中的流程图：

|======================= 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 | <= | | | | +------------+ | | +