python中socket、进程、线程、协程、池的创建方式和应用场景
程序员文章站
2022-07-12 11:12:24
...
一、TCP-socket
服务端:
import socket
tcp_sk = socket.socket()
tcp_sk.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)
tcp_sk.bind(('127.0.0.1',8000))
tcp_sk.listen()
conn,addr = tcp_sk.accept()
conn.send('你好'.encode('utf-8'))
print(conn.recv(1024).decode('utf-8'))
conn.close()
tcp_sk.close()
客户端:
import socket
sk = socket.socket()
sk.connect(('127.0.0.1',8000))
print(sk.recv(1024).decode('utf-8'))
sk.send('嘿嘿嘿'.encode('utf-8'))
sk.close()
二、UDP-socket
服务端:
import socket
udp_sk = socket.socket(type=socket.SOCK_DGRAM)
udp_sk.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)
udp_sk.bind(('127.0.0.1',8001))
msg,addr = udp_sk.recvfrom(1024)
print(msg.decode('utf-8'))
udp_sk.sendto('你好'.encode('utf-8'),addr)
udp_sk.close()
客户端:
import socket
sk = socket.socket(type=socket.SOCK_DGRAM)
sk.sendto('哈哈'.encode('utf-8'),('127.0.0.1',8001))
msg,addr = sk.recvfrom(1024)
print(msg.decode('utf-8'))
sk.close()
三、socketserver
服务端:
import socketserver
class Myserver(socketserver.BaseRequestHandler):
def handle(self):
conn = self.request
while True:
conn.send(b'hello')
print(conn.recv(1024).decode('utf-8'))
socketserver.TCPServer.allow_reuse_address = True
server = socketserver.ThreadingTCPServer(('127.0.0.1',8080),Myserver)
server.serve_forever()
客户端:
import socket
sk = socket.socket()
sk.connect(('127.0.0.1',8080))
while True:
ret = sk.recv(1024)
print(ret.decode('utf-8'))
sk.send(b'hiworld')
sk.close()
四、进程
方式一、
from multiprocessing import Process
def func(arg):
print(arg)
if __name__ == '__main__':
p = Process(target=func,args=('子进程',))
p.start()
p.join()
print('主进程')
方式二、
from multiprocessing import Process
class MyProcess(Process):
def __init__(self,name):
super().__init__()
self.name = name
def run(self):
print(self.name)
if __name__ == '__main__':
p = MyProcess('小明')
p.start()
五、线程
方式一、
from threading import Thread
import time
def sleep_boy(name):
time.sleep(1)
print('%s is sleeping' %name)
t = Thread(target=sleep_boy,args=('xiaoming',)) # 这里可以不需要main,因为现在只是在一个进程内操作,不需要导入进程就不会import主进程了
t.start()
print('主线程')
方式二、
from threading import Thread
import time
class Sleep_boy(Thread):
def __init__(self,name):
super().__init__()
self.name = name
def run(self):
time.sleep(1)
print('%s is sleeping' % self.name)
t = Sleep_boy('xiaoming')
t.start()
print('主线程')
六、协程
1、greenlet例子:
import time
from greenlet import greenlet
def cooking():
print('cooking 1')
g2.switch() # 切换到g2,让g2的函数工作
time.sleep(1)
print('cooking 2')
def watch():
print('watch TV 1')
time.sleep(1)
print('watch TV 2')
g1.switch() # 切换到g1,让g1的函数工作
g1 = greenlet(cooking)
g2 = greenlet(watch)
g1.switch() # 切换到g1,让g1的函数工作
greenlet的缺陷:很显然greenlet实现了协程的切换功能,可以自己设置什么时候切,在哪切,但是它遇到阻塞并没有自动切换,
因此并不能提高效率。所以一般我们都使用gevent模块实现协程
2、gevent例子1:
from gevent import monkey
monkey.patch_all()
import time
import gevent
def cooking():
print('cooking 1')
time.sleep(1)
print('cooking 2')
def watch():
print('watch TV 1')
time.sleep(1)
print('watch TV 2')
g1 = gevent.spawn(cooking) # 自动检测阻塞事件,遇见阻塞了就会进行切换
g2 = gevent.spawn(watch)
g1.join() # 阻塞直到g1结束
g2.join() # 阻塞直到g2结束
gevent例子2:
import gevent
def cooking(i):
print('%s号在煮饭' %i)
return i
g_lst = []
for i in range(10):
g = gevent.spawn(cooking,i) # 函数名,参数
g_lst.append(g) # 把协程对象放入列表
for g in g_lst:
g.join()
print(g.value) # 打印返回值
# gevent.joinall(g_lst) # joinall一次性把全部对象都阻塞
七、进程池
1、同步提交apply:
import os
import time
from multiprocessing import Pool
def test(num):
time.sleep(1)
print('%s:%s' %(num,os.getpid()))
return num*2
if __name__ == '__main__':
p = Pool()
for i in range(20):
res = p.apply(test,args=(i,)) # 提交任务的方法 同步提交
print('-->',res) # res就是test的return的值,同步提交的返回值可以直接使用
2、异步提交apply_async:
2-1无返回值:
import time
from multiprocessing import Pool
def func(num):
time.sleep(1)
print('做了%s件衣服'%num)
if __name__ == '__main__':
p = Pool(4) # 进程池中创建4个进程,不写的话,默认值为你电脑的CUP数量
for i in range(50):
p.apply_async(func,args=(i,)) # 异步提交func到一个子进程中执行,没有返回值的情况
p.close() # 关闭进程池,用户不能再向这个池中提交任务了
p.join() # 阻塞,直到进程池中所有的任务都被执行完
2-2有返回值:
import time
import os
from multiprocessing import Pool
def test(num):
time.sleep(1)
print('%s:%s' %(num,os.getpid()))
return num*2
if __name__ == '__main__':
p = Pool()
res_lst = []
for i in range(20):
res = p.apply_async(test,args=(i,)) # 提交任务的方法 异步提交
res_lst.append(res)
for res in res_lst:
print(res.get()) # 异步提交的返回值需要get,get有阻塞效果,此时就不需要close和join
2-3map:
map接收一个函数和一个可迭代对象,是异步提交的简化版本,自带close和join方法
可迭代对象的每一个值就是函数接收的实参,可迭代对象的长度就是创建的任务数量
map可以直接拿到返回值的可迭代对象(列表),循环就可以获取返回值
import time
from multiprocessing import Pool
def func(num):
print('子进程:',num)
# time.sleep(1)
return num
if __name__ == '__main__':
p = Pool()
ret = p.map(func,range(10)) # ret是列表
for i in ret:
print('返回值:',i)
2-4回调函数:
import os
from multiprocessing import Pool
def func(i):
print('子进程:',os.getpid())
return i
def call_back(res):
print('回调函数:',os.getpid())
print('res--->',res)
if __name__ == '__main__':
p = Pool()
print('主进程:',os.getpid())
p.apply_async(func,args=(1,),callback=call_back) # callback关键字传参,参数是回调函数
p.close()
p.join()
八、进程池、线程池
线程池:
1、
import time
from concurrent.futures import ThreadPoolExecutor
def func(i):
print('thread',i)
time.sleep(1)
print('thread %s end'%i)
tp = ThreadPoolExecutor(5) # 相当于tp = Pool(5)
tp.submit(func,1) # 相当于tp.apply_async(func,args=(1,))
tp.shutdown() # 相当于tp.close() + tp.join()
print('主线程')
2、
import time
from concurrent.futures import ThreadPoolExecutor
from threading import currentThread
def func(i):
print('thread',i,currentThread().ident)
time.sleep(1)
print('thread %s end'%i)
tp = ThreadPoolExecutor(5)
for i in range(20):
tp.submit(func,i)
tp.shutdown() # shutdown一次就够了,会自动把所有的线程都join()
print('主线程')
3、返回值
import time
from concurrent.futures import ThreadPoolExecutor
from threading import currentThread
def func(i):
print('thread',i,currentThread().ident)
time.sleep(1)
print('thread %s end' %i)
return i * '*'
tp = ThreadPoolExecutor(5)
ret_lst = []
for i in range(20):
ret = tp.submit(func,i)
ret_lst.append(ret)
for ret in ret_lst:
print(ret.result()) # 相当于ret.get()
print('主线程')
4、map
map接收一个函数和一个可迭代对象
可迭代对象的每一个值就是函数接收的实参,可迭代对象的长度就是创建的线程数量
map可以直接拿到返回值的可迭代对象(列表),循环就可以获取返回值
import time
from concurrent.futures import ThreadPoolExecutor
def func(i):
print('thread',i)
time.sleep(1)
print('thread %s end'%i)
return i * '*'
tp = ThreadPoolExecutor(5)
ret = tp.map(func,range(20))
for i in ret:
print(i)
5、回调函数
回调函数在进程池是由主进程实现的
回调函数在线程池是由子线程实现的
import time
from concurrent.futures import ThreadPoolExecutor
from threading import currentThread
def func(i):
print('thread',i,currentThread().ident)
time.sleep(1)
print('thread %s end'%i)
return i * '*'
def call_back(arg):
print('call back : ',currentThread().ident)
print('ret : ',arg.result()) # multiprocessing的Pool回调函数中的参数不需要get(),这里需要result()
tp = ThreadPoolExecutor(5)
ret_lst = []
for i in range(20):
tp.submit(func,i).add_done_callback(call_back) # 使用add_done_callback()方法实现回调函数
print('主线程',currentThread().ident)
上一篇: drf环境搭建
下一篇: DRF项目工程基础包