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：
```python
import time
from concurrent.futures import ProcessPoolExecutor, as_completed
NUMBERS = 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.py
fib(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_completed
NUMBERS = 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.py
fib(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 <module>
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 time
from multiprocessing.pool import Pool
NUMBERS = 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：
|
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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 | <= | |
| | +------------+ | | +