python基础-UDP、进程、进程池、paramike模块

1 基于UDP套接字
1.1 介绍
　　udp是无连接的，是数据报协议，先启动哪端都不会报错
　　udp服务端

import socket

sk = socket() #创建一个服务器的套接字
sk.bind() #绑定服务器套接字
while True: #服务器无限循环
cs = sk.recvfrom()/sk.sendto() # 对话(接收与发送)
sk.close() # 关闭服务器套接字

　　udp客户端

import socket
client = socket() # 创建客户套接字
while True: # 通讯循环
client.sendto()/client.recvfrom() # 对话(发送/接收)
client.close() # 关闭客户套接字

1.2 基本实例
1.2.1 服务端

import socket

udp_server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
udp_server.bind(('127.0.0.1', 9999))

while True:
data,client_addr = udp_server.recvfrom(512)
print(data, client_addr)
udp_server.sendto(data.upper(), client_addr)

1.2.2 客户端

import socket

udp_client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)

while True:
msg = input('>>').strip()
udp_client.sendto(msg.encode('utf-8'), ('127.0.0.1',9999))
data,server_addr = udp_client.recvfrom(512)
print(data.decode('utf-8'))

1.3 udp不会粘包
　　udp是基于数据报协议，发送一份信息，有完整的报头的主题，不会像tcp那样基于数据流的，没有开头、没有结尾；而udp有开头（报头），也有结尾，所以不会出现像tcp那样粘包现象。
1.3.1 服务端

import socket

udp_server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
udp_server.bind(('127.0.0.1',9999))

info1,client_addr = udp_server.recvfrom(1)
print('info1', info1)
info2,client_addr = udp_server.recvfrom(512)
print('info2', info2)

1.3.2 客户端

import socket

udp_client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)

udp_client.sendto('welcome'.encode('utf-8'), ('127.0.0.1',9999))
udp_client.sendto('beijing'.encode('utf-8'), ('127.0.0.1',9999))

1.4 udp并发
1.4.1 服务端

import socketserver
class MyUDPhandler(socketserver.BaseRequestHandler):
def handle(self):
print(self.request)
self.request[1].sendto(self.request[0].upper(), self.client_address)

if __name__ == '__main__':
udp_server = socketserver.ThreadingUDPServer(('127.0.0.1',8080), MyUDPhandler)
udp_server.serve_forever()

1.4.2 客户端

import socket

udp_client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)

while True:
info = input('>>').strip()
udp_client.sendto(info.encode('utf-8'), ('127.0.0.1',9999))
body,server_addr = udp_client.recvfrom(512)
print(body.decode('utf-8'))

2 进程
2.1 介绍
　　进程：正在运行的一个过程或者任务，是对正在运行程序的一个抽象。

2.2 开启进程
示例1

from multiprocessing import Process
import time
def my_run(info):
print('task <%s> is running' %info)
time.sleep(0.5)
print('task <%s> is done' % info)

if __name__ == '__main__':
process1 = Process(target = my_run, args=('mary',))
process2 = Process(target = my_run, args=('jack',))
process1.start()
process2.start()

示例2

from multiprocessing import Process
import time
class MyMulProcess(Process):
def __init__(self,name):
super().__init__()
self.name = name

def my_run(self):
print('task <%s> is runing' % self.name)
time.sleep(0.5)
print('task <%s> is done' % self.name)

if __name__ == '__main__':
process = MyMulProcess('jack')
process.my_run()
process.start()

2.3 并发通信
2.3.1 服务端

from multiprocessing import Pool
import os
import socket
tcp_server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tcp_server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # 解决Address already in use
tcp_server.bind(('127.0.0.1',9999))
tcp_server.listen(5)
def work(conn, addr):
print(os.getpid())
print(addr)
while True:
try:
data = conn.recv(1024)
if not data:break
conn.send(data.upper())
except Exception:
break
conn.close()

if __name__ == '__main__':
pool = Pool(4)
while True:
conn,addr = tcp_server.accept()
pool.apply_async(work, args = (conn, addr))
tcp_server.close()

2.3.2 客户端

import socket

tcp_client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tcp_client.connect(('127.0.0.1',9999))

while True:
info = input('>>').strip()
if not info:continue
tcp_client.send(info.encode('utf-8'))
data = tcp_client.recv(1024)
print(data.decode('utf-8'))

tcp_client.close()

2.4 join方法
　　主进程，等待子进程运行完，才执行下面内容；
　　p.join只能join住start开启的进程，而不能join住run开启的进程
　　主进程，等待p1执行结束，才执行主进程下面的内容

from multiprocessing import Process
import time

def work(name):
print('task <%s> is runing' %name)
time.sleep(0.5)
print('task <%s> is done' % name)

if __name__ == '__main__':
process1 = Process(target = work, args=('jack',))
process2 = Process(target = work, args=('mary',))

process_list = [process1, process2]
for process in process_list:
process.start()

for process in process_list:
process.join()

2.6 守护进程
　　主进程代码运行完毕，守护进程就会结束
　　主进程创建守护进程
　　1.守护进程会在主进程代码执行结束后就终止
　　2.守护进程内无法再开启子进程，否则抛出异常。

　　守护进程，守护者主进程，主进程结束，守护进程随即结束；主进程代码结束后，守护进程随之结束

from multiprocessing import Process
import time
def work(name):
print('task <%s> is runing' %name)
time.sleep(0.5)
print('task <%s> is done' % name)

if __name__ == '__main__':
p1=Process(target=work,args=('jack',))
p1.daemon = True # 必须在进程开启之前，设置为守护进程
p1.start()

　　重复守护进程概念，守护进程什么时间结束；在主进程代码结束，就会结束

from multiprocessing import Process
import time
def foo():
print("from foo start")
time.sleep(0.5)
print("from foo end")

def bar():
print("from bar start")
time.sleep(0.8)
print("from bar end")
if __name__ == '__main__':
process1 = Process(target = foo)
process2 = Process(target = bar)

process1.daemon = True
process1.start()
process2.start()
print("主进程")

#打印该行则主进程代码结束,则守护进程process1应该被终止,
# 可能会有process1任务执行的打印信息from foo start,
# 因为主进程打印主进程时,process1也执行了,但是随即被终止

2.7 进程同步锁
　　核心点：保证一个进程用完一个终端，再交个另一个终端使用，独享终端，保证有序；

2.7.1 基本用法
　　加锁，变为串行，保证数据不会错乱；效率与错乱之间做出取舍

from multiprocessing import Process,Lock
import time
def work(name, mutex):
mutex.acquire()
print('task <%s> is runing' %name)
time.sleep(0.5)
print('task <%s> is done' % name)
mutex.release()

if __name__ == '__main__':
mutex = Lock()
process1 = Process(target = work, args = ('jack', mutex))
process2 = Process(target = work, args = ('mary', mutex))
process1.start()
process2.start()

2.7.2 模拟购票
　　模拟购票，查询票的余额，不要考虑先后顺序；而到真正购票环境，需要保证一张票不被多次购买，需要加锁。

import json,time,os
from multiprocessing import Process,Lock
def search():
dic = json.load(open('ticket.txt'))
print('\033[32m[%s] 看到剩余票数<%s>\033[0m' %(os.getpid(), dic['count']))
def get_ticket():
dic = json.load(open('ticket.txt'))
time.sleep(0.5) # 模拟读数据库的网络延迟
if dic['count'] > 0:
dic['count'] -= 1
time.sleep(0.5) # 模拟写数据库的网络延迟
json.dump(dic,open('ticket.txt','w'))
print('\033[31m%s 购票成功\033[0m' %os.getpid())
def work(mutex):
search()
mutex.acquire()
get_ticket()
mutex.release()
if __name__ == '__main__':
mutex = Lock()
for index in range(10):
process = Process(target = work, args = (mutex,))
process.start()

2.7.3 共享数据通信
　　基于共享内存方式，进行数据通信，需要考虑锁的形式。

from multiprocessing import Process,Manager,Lock

def work(dic, mutex):
with mutex:
dic['count'] -= 1

if __name__ == '__main__':
mutex = Lock()
manager = Manager()
dic = manager.dict({'count':100})
p_list = []
for i in range(100):
process = Process(target = work, args = (dic, mutex))
p_list.append(process)
process.start()

for process in p_list:
process.join()
print(dic)

2.7.3 进程间通信
　　进程间的通信，有很多方式，例如：管道、共享数据、消息队列等；推荐的方式是：通过消息队列的方式进行通信。
　　Queue，常用方法：put、get；队列就是管道加锁，进行实现的

from multiprocessing import Queue

queue = Queue(3) # 队列的最大长度为3

queue.put('first')
queue.put('second')
queue.put('third')
queue.put('fourth') # 超过队列长度，满了会卡着

print(queue.get())
print(queue.get())
print(queue.get())
print(queue.get()) # 队列空了，一直卡着，等待队里有值，进行获取

# 了解知识点
queue = Queue(3)
queue.put('first',block = False) # 队列满了，不进行锁住，会抛异常
queue.put('second', block = False)
queue.put('third', block = False)
queue.put_nowait('fourth') # 等价queue.put('fourth', block = False)
queue.put('fourth',timeout = 3) # 默认等待3秒钟，指定超时时间

3 生产者、消费者
　　应该具有两类模型，生产者和消费者
3.1 基本版本的生产者消费者

from multiprocessing import Process,Queue
import time,os
def producer(q,name):
for i in range(5):
time.sleep(0.5)
res='%s%s' %(name,i)
q.put(res)
print('\033[42m<%s> 制造 [%s]\033[0m' %(os.getpid(),res))

def consumer(q):
while True:
res=q.get()
if res is None:break
time.sleep(0.8)
print('\033[31m<%s> 购买 [%s]\033[0m' % (os.getpid(), res))

if __name__ == '__main__':
queue = Queue()
# 生产者
producer1 = Process(target = producer, args = (queue, '自行车'))
producer2 = Process(target = producer, args = (queue, '汽车'))
producer3 = Process(target = producer, args = (queue, '飞机'))

# 消费者
consumer1 = Process(target = consumer, args = (queue,))
consumer2 = Process(target = consumer, args = (queue,))

producer1.start()
producer2.start()
producer3.start()
consumer1.start()
consumer2.start()

producer1.join()
producer2.join()
producer3.join()
queue.put(None) # 利用None通知消费者，东西已经生产完了
queue.put(None) # 有几个消费者，就要通知几次

3.2 JoinableQueue改进生产者消费者模型
　　生产者等待消费者把队列的内容全部去空，就结束生产过程；消费等待主进程结束，也就随之结束
　　主进程等待生产者结束，才执行剩余代码；需要消费者利用守护进程，随者主进程结束也就结束。
　　逻辑性比较强，利用JoinableQueue和守护进程和join。

from multiprocessing import Process,JoinableQueue
import time,os
def producer(queue, name):
for i in range(5):
time.sleep(0.5)
res = '%s%s' %(name,i)
queue.put(res)
print('\033[42m<%s> 制造 [%s]\033[0m' %(os.getpid(),res))
queue.join() # 生产者等待queue里面的所有内容都被卖掉了，就结束了

def consumer(queue):
while True:
res = queue.get()
if res is None:break
time.sleep(0.8)
print('\033[31m<%s> 购买 [%s]\033[0m' % (os.getpid(), res))
queue.task_done() # 确定购买了生产者的一个内容，通知生产者
# 通知queue，已经取走一个东西

if __name__ == '__main__':
queue = JoinableQueue()
# 生产者
producer1 = Process(target = producer, args = (queue, '自行车'))
producer2 = Process(target = producer, args = (queue, '汽车'))
producer3 = Process(target = producer, args = (queue, '飞机'))

# 消费者
consumer1 = Process(target = consumer, args = (queue,))
consumer2 = Process(target = consumer, args = (queue,))

consumer1.daemon = True # 消费者利用守护进程，随着主进程结束也就结束
consumer2.daemon = True
producer1.start()
producer2.start()
producer3.start()
consumer1.start()
consumer2.start()

producer1.join()

4 进程池
　　默认开启进程池的个数，是cpu核数的个数
4.1 同步提交任务
　　同步提交任务，损失效率，保证数据有序和安全

from multiprocessing import Pool
import os,time

def task(n):
print('task <%s> is runing' %os.getpid())
time.sleep(0.5)
return n**3

if __name__ == '__main__':
print(os.cpu_count())
p = Pool(4)
for index in range(10):
res = p.apply(task, args = (index,))
print(res)

4.2 异步提交任务
　　等待进程池中的所有任务结束，就可以拿到运行结果
　　p.close(),禁止向进程池中提交新任务

from multiprocessing import Pool
import os,time

def task(n):
print('task <%s> is runing' %os.getpid())
time.sleep(0.5)
return n**3

if __name__ == '__main__':
print(os.cpu_count())
pool = Pool(4)
res_list = []
for index in range(10):
res = pool.apply_async(task, args = (index,)) # 只负责向队列仍任务，不等任务结束
res_list.append(res)

for res in res_list:
print(res.get())

pool.close()
pool.join()

4.3 进程池控制并发
　　利用进程池，控制并发的进程数量
4.3.1 服务端

from multiprocessing import Pool
import os
import socket

server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server.bind(('127.0.0.1',9999))
server.listen(5)
def work(conn, addr):
print(os.getpid())
print(addr)
while True:
try:
data=conn.recv(1024)
if not data:break
conn.send(data.upper())
except Exception:
break
conn.close()

if __name__ == '__main__':
pool = Pool(4)
while True:
conn,addr = server.accept()
pool.apply_async(work, args = (conn, addr))
server.close()

4.3.2 客户端

import socket
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(('127.0.0.1',9999))

while True:
info = input('>>').strip()
if not info:continue
client.send(info.encode('utf-8'))
data = client.recv(1024)
print(data.decode('utf-8'))

client.close()

4.4 回调函数
　　让下载的函数进行并发，解析的函数进行串行执行（利用回调函数进行执行）
　　耗时时间长的利用进程池进行并发处理，利用异步进行处理

import requests # pip3 install requests
import os,time
from multiprocessing import Pool
def get_info(url):
print('<%s> get :%s' %(os.getpid(),url))
respone = requests.get(url)
if respone.status_code == 200:
return {'url':url, 'text':respone.text}

def parse_page(dic):
print('<%s> parse :%s' %(os.getpid(),dic['url']))
time.sleep(0.5)
parse_res='url:<%s> size:[%s]\n' %(dic['url'],len(dic['text'])) # 模拟解析网页内容
with open('reptile.txt','a') as f:
f.write(parse_res)

if __name__ == '__main__':
pool = Pool(4)
urls = [
'https://www.baidu.com',
'https://www.python.org',
'https://www.openstack.org',
'https://help.github.com',
'https://www.sina.com.cn',
]

for url in urls:
pool.apply_async(get_info, args = (url,), callback = parse_page)
# 利用回调函数，通知主进程，调用parse_page，需要执行parse_page函数了
# 把get_page执行结果，传递给parse_page作为参数进行传递

pool.close()
pool.join()

5 paramike模块
5.1 介绍
　　paramiko是一个用于做远程控制的模块，使用该模块可以对远程服务器进行命令或文件操作，值得一说的是，fabric和ansible内部的远程管理就是使用的paramiko来现实。

5.2 基于密码连接

import paramiko

# 创建SSH对象
ssh = paramiko.SSHClient()
# 允许连接不在know_hosts文件中的主机
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
# 连接服务器
ssh.connect(hostname='127.0.0.1', port=22, username='root', password='xxx')

# 执行命令
stdin, stdout, stderr = ssh.exec_command('df')
# 获取命令结果
result = stdout.read()
print(result.decode('utf-8'))
# 关闭连接
ssh.close()

5.3 基于秘钥链接
　　客户端文件名：id_rsa
　　服务端必须有文件名：authorized_keys(在用ssh-keygen时，必须制作一个authorized_keys,可以用ssh-copy-id来制作)

import paramiko

private_key = paramiko.RSAKey.from_private_key_file('id_rsa')

# 创建SSH对象
ssh = paramiko.SSHClient()
# 允许连接不在know_hosts文件中的主机
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
# 连接服务器
ssh.connect(hostname='127.0.0.1', port=22, username='root', pkey=private_key)

# 执行命令
stdin, stdout, stderr = ssh.exec_command('df -h')
# 获取命令结果h
result = stdout.read()
print(result.decode('utf-8'))
# 关闭连接
ssh.close()

import paramiko
from io import StringIO
key_str = """-----BEGIN RSA PRIVATE KEY-----
MIIEpQIBAAKCAQEAl6OGU27q3YV1LMdAZH02PWFtPJaxQ68zbWIBmgP0NfnVQz30
Bvhe+rZCFEQCwLoeld4jj7vA+Vk8nKPoBhX1WVvq9MfSF+YRXeu1+XAXvL1pH+Q9
6gJ1l4NmwHq8DVgUCx3TsAdK0wOqEIUDSOu+2/nECK1lmKN1Cf5Va20r+B0CDjkT
RRA6C9MIb2WWwf4EiEWjBl+lnZ0vDXoB38oZQR71tdLsvrVumUDzyGvF79jWW3O6
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quqVloSwrw2P/BE+QxwW5Ec7eA1qrRx+x4pNYgyfiQeVUODvwED86WaxgMoGRzJG
53IluVH/SApuAfzCj5OwMWkSOMYr1TiutkQ/JIMvj9n6gPcqNnbEcSefyew5x3aq
2IxuMlECgYEAtVuORz9C7WnJIVW6HwNiS4OBs7becOgXDHAOw0hnu+3mxVm/NIkX
yeGK7rP1KKbS4I3pbG+H0nWAQkfQtW6nQjU5nvoCTX8Yyk6ZNC0mhGNTqKRqpjI/
eXe8nUib71izC6g6kJY66+BTg2SCBsHUAqAB7L4gvFHGt8sq46TQ3jw=
-----END RSA PRIVATE KEY-----"""

private_key = paramiko.RSAKey(file_obj=StringIO(key_str))
transport = paramiko.Transport(('127.0.0.1', 22))
transport.connect(username='root', pkey=private_key)

ssh = paramiko.SSHClient()
ssh._transport = transport

stdin, stdout, stderr = ssh.exec_command('df')
result = stdout.read()
print(result.decode('utf-8'))
transport.close()

print(result)