#!/usr/bin/env python
# -*- coding:utf-8 -*-
from multiprocessing import Process
import time# 多进程 multiprocessing 并非是python的一个模块，而是python中多进程管理的一个包，
# 在学习的时候可以与threading这个模块作类比
def func(name):for i in range(3):time.sleep(1000)print("hello", name)if __name__ == "__main__":p = Process(target=func, args=("guolei",))# 类似 线程(thread) 的 setDaemon()# p.daemon = True
    p.start()# p.join()p2 = Process(target=func, args=("xiaming",))# p2.daemon = True
    p2.start()# p2.join()print("2222222")

#!/usr/bin/env python
# -*- coding:utf-8 -*-
import multiprocessing
import threadingLI = []# 进程之间是独立的, 默认数据是无法被共享的
def append_to_li(item, p_name):LI.append(item)print(p_name, LI, id(LI))if __name__ == '__main__':for i in range(10):# process 数据不共享p = multiprocessing.Process(target=append_to_li, args=(i, "p_name" + str(i),))# thread 数据是共享的# p = threading.Thread(target=append_to_li, args=(i, "p_name" + str(i),))
        p.start()print("end ", LI, id(LI))

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from multiprocessing import Process, Array, Manager
import timedef Foo(i, temp):# time.sleep(2)temp[i] = 100 + ifor item in temp:print(i, "----->", item)def Foo2(i, dic):dic[i] = 100 + iprint(dic.values())if __name__ == '__main__':temp = Array("i", [11, 22, 33, 44])for i in range(2):p = Process(target=Foo, args=(i, temp,))p.start()for i in temp:print(i)# m = Manager()# dic = m.dict()#
    # for i in range(2):#     p = Process(target=Foo2, args=(i, dic,))#     p.start()#     # join()必须有,因为子进程在运行过程中,主进程已经结束了#     # p.join()# time.sleep(10)

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from multiprocessing import Pool
import time# 进程池demo
def foo(i):time.sleep(1)return i + " hello"def bar(arg):print(arg)if __name__ == '__main__':pool = Pool(5)# print(pool.apply_async(func=foo, args=(1,)).get())for i in range(10):# 每一个任务是排队进行的, apply()是阻塞的 里面有 进程.join()# pool.apply(foo, (str(i),))# 每一个任务都并发执行, 可以设置回调函数, apply_async()是异步非阻塞的 里面有 daemon = Truepool.apply_async(func=foo, args=("process_" + str(i),), callback=bar)print("11111111111111111111111")print("end")# pool.close()# pool.join()  # 进程池中的进程执行完毕后再关闭，如果注释，那么程序直接关闭

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from greenlet import greenlet
from gevent import monkey;monkey.patch_all()  # 这一句必须要,猴子补丁
import gevent
import requestsdef foo(url):print("Get: %s" % url)resp = requests.get(url)data = resp.textprint("%d bytes received from %s" % (len(data), url))# 协程可以一次发出多个请求，适用于需要等待的网络I/O操作
gevent.joinall([gevent.spawn(foo, "https://www.python.org/"),gevent.spawn(foo, "https://www.yahoo.com/"),gevent.spawn(foo, "https://github.com/")
])# 协程高级模块gevent
# def foo():
#     print("11111111111")
#     gevent.sleep(0)
#     print("22222222222")
#
#
# def bar():
#     print("33333333333")
#     gevent.sleep(0)
#     print("44444444444")
#
#
# gevent.joinall([
#     gevent.spawn(foo),
#     gevent.spawn(bar),
# ])# 协程模块greenlet, 功能很弱, 一般不用这个, 使用高级模块gevent【基于greenlet】
# def test1():
#     print(12)
#     gr2.switch()
#     print(34)
#     gr2.switch()
#
#
# def test2():
#     print(56)
#     gr1.switch()
#     print(78)
#
#
# gr1 = greenlet(test1)
# gr2 = greenlet(test2)
#
# gr1.switch()