使用多进程
multiprocessing模块提供了使用进程的方法，使用起来和线程threading模块非常类似。
multiprocessing模块包含一个与Thread类非常相似的Process类。可以将Python中的并发编程（2）线程的实现的Thread直接替换成Process。
import itertools
import time
# 从multiprocessing导入
from multiprocessing import Process, Event
from multiprocessing import synchronize
# 旋转
def spin(msg: str, done: synchronize.Event ) -> None:
for char in itertools.cycle(r'|/-'):
status = f'r{char} {msg}'
print(status, end='', flush=True)
if done.wait(.1):
break
blanks = ' ' * len(status)
print(f'r{blanks}r', end='')
# 阻塞3秒，并返回42
def slow() -> int:
time.sleep(3)
return 42
def supervisor() -> int:
done = Event()
# Thread 替换成Process
spinner = Process(target=spin, args=('thinking!', done))
print(f'spinner object: {spinner}')
spinner.start()
result = slow()
done.set()
spinner.join()
return result
def main() -> None:
result = supervisor()
print(f'Answer: {result}')
if __name__ == '__main__':
main()
同样，我们用进程改写线程Python中的并发编程（2）线程的实现的计算。
代码语言：javascript复制import time
from multiprocessing import Process
# 计算number的因子
def factorize(number):
for i in range(1, number 1):
if number % i == 0:
yield i
numbers = [2139079, 1214759, 1516637, 1852285, 14256346, 12456533]
def get_factor(number):
factors = list(factorize(number))
return factors
if __name__ == '__main__':
start = time.time()
processes = []
for number in numbers:
process = Process(target=get_factor, args=(number,))
process.start() # 启动
processes.append(process)
for process in processes:
process.join() # 等待完成
end = time.time()
delta = end - start
print(f'Process {delta:.3f} 秒')我们发现修改为进程后，计算耗费时间减少了一些：
代码语言：javascript复制（之前的）顺序执行花费 2.478 秒
Process 1.744 秒由于进程启动和通信需要耗费一定时间，所以并不明显。如果把numbers中的数字加大，时间减少的会更明显：
代码语言：javascript复制numbers = [4139079, 2214759, 4516637, 6852285, 44256346, 62456533]顺序执行花费 11.079 秒
Process 6.870 秒
multiprocessing还提供了进程池Pool，可以方便地处理一系列输入：
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
with Pool(5) as p:
print(p.map(f, [1, 2, 3]))
