executor和futureconcurrent.futures模块的基础是exectuor，executor是一个抽象类，它不能被直接使用。但是它提供的两个子类threadpoolexecutor和processpoolexecutor却是非常有用，顾名思义两者分别被用来创建线程池和进程池的代码。我们可以将相应的tasks直接放入线程池/进程池，不需要维护queue来操心死锁的问题，线程池/进程池会自动帮我们调度。
future这个概念相信有java和nodejs下编程经验的朋友肯定不陌生了，你可以把它理解为一个在未来完成的操作，这是异步编程的基础，传统编程模式下比如我们操作queue.get的时候，在等待返回结果之前会产生阻塞，cpu不能让出来做其他事情，而future的引入帮助我们在等待的这段时间可以完成其他的操作。关于在python中进行异步io可以阅读完本文之后参考我的python并发编程之协程/异步io。
p.s: 如果你依然在坚守python2.x，请先安装futures模块。
pip install futures
使用submit来操作线程池/进程池我们先通过下面这段代码来了解一下线程池的概念
# example1.py
from concurrent.futures import threadpoolexecutor
import time
def return_future_result(message):
time.sleep(2)
return message
pool = threadpoolexecutor(max_workers=2) # 创建一个最大可容纳2个task的线程池
future1 = pool.submit(return_future_result, (hello)) # 往线程池里面加入一个task
future2 = pool.submit(return_future_result, (world)) # 往线程池里面加入一个task
print(future1.done()) # 判断task1是否结束
time.sleep(3)
print(future2.done()) # 判断task2是否结束
print(future1.result()) # 查看task1返回的结果
print(future2.result()) # 查看task2返回的结果
我们根据运行结果来分析一下。我们使用submit方法来往线程池中加入一个task，submit返回一个future对象，对于future对象可以简单地理解为一个在未来完成的操作。在第一个print语句中很明显因为time.sleep(2)的原因我们的future1没有完成，因为我们使用time.sleep(3)暂停了主线程，所以到第二个print语句的时候我们线程池里的任务都已经全部结束。
ziwenxie :: ~ » python example1.py
false
true
hello
world
# 在上述程序执行的过程中，通过ps命令我们可以看到三个线程同时在后台运行
ziwenxie :: ~ » ps -elf | grep python
ziwenxie 8361 7557 8361 3 3 19:45 pts/0 00:00:00 python example1.py
ziwenxie 8361 7557 8362 0 3 19:45 pts/0 00:00:00 python example1.py
ziwenxie 8361 7557 8363 0 3 19:45 pts/0 00:00:00 python example1.py
上面的代码我们也可以改写为进程池形式，api和线程池如出一辙，我就不罗嗦了。
# example2.py
from concurrent.futures import processpoolexecutor
import time
def return_future_result(message):
time.sleep(2)
return message
pool = processpoolexecutor(max_workers=2)
future1 = pool.submit(return_future_result, (hello))
future2 = pool.submit(return_future_result, (world))
print(future1.done())
time.sleep(3)
print(future2.done())
print(future1.result())
print(future2.result())
下面是运行结果
ziwenxie :: ~ » python example2.py
false
true
hello
world
ziwenxie :: ~ » ps -elf | grep python
ziwenxie 8560 7557 8560 3 3 19:53 pts/0 00:00:00 python example2.py
ziwenxie 8560 7557 8563 0 3 19:53 pts/0 00:00:00 python example2.py
ziwenxie 8560 7557 8564 0 3 19:53 pts/0 00:00:00 python example2.py
ziwenxie 8561 8560 8561 0 1 19:53 pts/0 00:00:00 python example2.py
ziwenxie 8562 8560 8562 0 1 19:53 pts/0 00:00:00 python example2.py
使用map/wait来操作线程池/进程池除了submit，exectuor还为我们提供了map方法，和内建的map用法类似，下面我们通过两个例子来比较一下两者的区别。
使用submit操作回顾# example3.py
import concurrent.futures
import urllib.request
urls = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']
def load_url(url, timeout):
with urllib.request.urlopen(url, timeout=timeout) as conn:
return conn.read()
# we can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.threadpoolexecutor(max_workers=3) as executor:
# start the load operations and mark each future with its url
future_to_url = {executor.submit(load_url, url, 60): url for url in urls}
for future in concurrent.futures.as_completed(future_to_url):
url = future_to_url[future]
try:
data = future.result()
except exception as exc:
print('%r generated an exception: %s' % (url, exc))
else:
print('%r page is %d bytes' % (url, len(data)))
从运行结果可以看出，as_completed不是按照urls列表元素的顺序返回的。
ziwenxie :: ~ » python example3.py
'http://example.com/' page is 1270 byte
'https://api.github.com/' page is 2039 bytes
'http://httpbin.org' page is 12150 bytes
使用map# example4.py
import concurrent.futures
import urllib.request
urls = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']
def load_url(url):
with urllib.request.urlopen(url, timeout=60) as conn:
return conn.read()
# we can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.threadpoolexecutor(max_workers=3) as executor:
for url, data in zip(urls, executor.map(load_url, urls)):
print('%r page is %d bytes' % (url, len(data)))
从运行结果可以看出，map是按照urls列表元素的顺序返回的，并且写出的代码更加简洁直观，我们可以根据具体的需求任选一种。
ziwenxie :: ~ » python example4.py
'http://httpbin.org' page is 12150 bytes
'http://example.com/' page is 1270 bytes
'https://api.github.com/' page is 2039 bytes
第三种选择waitwait方法接会返回一个tuple(元组)，tuple中包含两个set(集合)，一个是completed(已完成的)另外一个是uncompleted(未完成的)。使用wait方法的一个优势就是获得更大的自由度，它接收三个参数first_completed, first_exception 和all_complete，默认设置为all_completed。
我们通过下面这个例子来看一下三个参数的区别
from concurrent.futures import threadpoolexecutor, wait, as_completed
from time import sleep
from random import randint
def return_after_random_secs(num):
sleep(randint(1, 5))
return return of {}.format(num)
pool = threadpoolexecutor(5)
futures = []
for x in range(5):
futures.append(pool.submit(return_after_random_secs, x))
print(wait(futures))
# print(wait(futures, timeout=none, return_when='first_completed'))
如果采用默认的all_completed，程序会阻塞直到线程池里面的所有任务都完成。
ziwenxie :: ~ » python example5.py
doneandnotdonefutures(done={
<future at 0x7f0b06c9bc88 state=finished returned str>,
<future at 0x7f0b06cbaa90 state=finished returned str>,
<future at 0x7f0b06373898 state=finished returned str>,
<future at 0x7f0b06352ba8 state=finished returned str>,
<future at 0x7f0b06373b00 state=finished returned str>}, not_done=set())
如果采用first_completed参数，程序并不会等到线程池里面所有的任务都完成。
ziwenxie :: ~ » python example5.py
doneandnotdonefutures(done={
<future at 0x7f84109edb00 state=finished returned str>,
<future at 0x7f840e2e9320 state=finished returned str>,
<future at 0x7f840f25ccc0 state=finished returned str>},
not_done={<future at 0x7f840e2e9ba8 state=running>,
<future at 0x7f840e2e9940 state=running>})
思考题写一个小程序对比multiprocessing.pool(threadpool)和processpollexecutor(threadpoolexecutor)在执行效率上的差距，结合上面提到的future思考为什么会造成这样的结果。
以上就是python 并发编程之线程池/进程池 的详细内容。