线程池，进程池
python的多线程并不是完全鸡肋的存在，得分情况来看。在IO密集型任务下，能提高多倍效率。在CPU密集型任务下，使用多进程也能规避GIL锁。
python3标准库concurrent.futures比原Thread封装更高，多线程concurrent.futures.ThreadPoolExecutor，多进程concurrent.futures.ProcessPoolExecutor
利用concurrent.futures.Future来进行各种便捷的数据交互，包括处理异常，都在result()中再次抛出。
模板
import time
from concurrent import futures
from concurrent.futures import ThreadPoolExecutor
def display(args):
print(time.strftime(\'[%H:%M:%S]\', time.localtime()), end=\' \')
print(args)
def task(n):
"""只是休眠"""
display(\'begin sleep {}s.\'.format(n))
time.sleep(n)
display(\'ended sleep {}s.\'.format(n))
def do_many_task_inorder():
"""多线程
按任务发布顺序依次等待完成
"""
tasks = [5, 4, 3, 2, 1]
with ThreadPoolExecutor(max_workers=3) as executor:
future_list = [executor.submit(task, arg) for arg in tasks]
display(\'非阻塞运行\')
for future in future_list:
display(future)
display(\'统一结束(有序)\')
for future in future_list:
display(future.result())
def do_many_task_disorder():
"""多线程执行
先完成先显示
"""
tasks = [5, 4, 3, 2, 1]
with ThreadPoolExecutor(max_workers=3) as executor:
future_list = [executor.submit(task, arg) for arg in tasks]
display(\'非阻塞运行\')
for future in future_list:
display(future)
display(\'统一结束(无序)\')
done_iter = futures.as_completed(future_list) # generator
for done in done_iter:
display(done)
if __name__ == \'__main__\':
do_many_task_inorder()
do_many_task_disorder()
