一、多进程
1.创建并开启子进程的两种方式
#开进程的方法一: import time import random from multiprocessing import Process def piao(name): print(‘%s piaoing‘ %name) time.sleep(random.randrange(1,5)) print(‘%s piao end‘ %name) p1=Process(target=piao,args=(‘egon‘,)) #必须加,号 p2=Process(target=piao,args=(‘alex‘,)) p3=Process(target=piao,args=(‘wupeqi‘,)) p4=Process(target=piao,args=(‘yuanhao‘,)) p1.start() p2.start() p3.start() p4.start() print(‘主线程‘)
#开进程的方法二: import time import random from multiprocessing import Process class Piao(Process): def __init__(self,name): super().__init__() self.name=name def run(self): print(‘%s piaoing‘ %self.name) time.sleep(random.randrange(1,5)) print(‘%s piao end‘ %self.name) p1=Piao(‘egon‘) p2=Piao(‘alex‘) p3=Piao(‘wupeiqi‘) p4=Piao(‘yuanhao‘) p1.start() #start会自动调用run p2.start() p3.start() p4.start() print(‘主线程‘)
2.守护进程
主进程创建守护进程
其一:守护进程会在主进程代码执行结束后就终止
其二:守护进程内无法再开启子进程,否则抛出异常:AssertionError: daemonic processes are not allowed to have children
注意:进程之间是互相独立的,主进程代码运行结束,守护进程随即终止
from multiprocessing import Process import time import random class Piao(Process): def __init__(self,name): self.name=name super().__init__() def run(self): print(‘%s is piaoing‘ %self.name) time.sleep(random.randrange(1,3)) print(‘%s is piao end‘ %self.name) p=Piao(‘egon‘) p.daemon=True #一定要在p.start()前设置,设置p为守护进程,禁止p创建子进程,并且父进程代码执行结束,p即终止运行 p.start() print(‘主‘)
#主进程代码运行完毕,守护进程就会结束 from multiprocessing import Process from threading import Thread import time def foo(): print(123) time.sleep(1) print("end123") def bar(): print(456) time.sleep(3) print("end456") p1=Process(target=foo) p2=Process(target=bar) p1.daemon=True p1.start() p2.start() print("main-------") #打印该行则主进程代码结束,则守护进程p1应该被终止,可能会有p1任务执行的打印信息123,因为主进程打印main----时,p1也执行了,但是随即被终止
3.进程间通信(队列)
from multiprocessing import Process,Queue import time def write(q): for i in range(1000000000): q.put(i) # time.sleep(1) print(q) def read(q): while True: i=q.get() print(i,‘read‘) if __name__ == ‘__main__‘: q=Queue(10000) wp=Process(target=write,args=(q,)) rp=Process(target=read,args=(q,)) wp.start() rp.start() rp.join()
4.进程同步锁
#并发运行,效率高,但竞争同一打印终端,带来了打印错乱 from multiprocessing import Process import os,time def work(): print(‘%s is running‘ %os.getpid()) time.sleep(2) print(‘%s is done‘ %os.getpid()) if __name__ == ‘__main__‘: for i in range(3): p=Process(target=work) p.start()
#由并发变成了串行,牺牲了运行效率,但避免了竞争 from multiprocessing import Process,Lock import os,time def work(lock): lock.acquire() print(‘%s is running‘ %os.getpid()) time.sleep(2) print(‘%s is done‘ %os.getpid()) lock.release() if __name__ == ‘__main__‘: lock=Lock() for i in range(3): p=Process(target=work,args=(lock,)) p.start()
#文件db的内容为:{"count":1}
#注意一定要用双引号,不然json无法识别
from multiprocessing import Process,Lock
import time,json,random
def search():
dic=json.load(open(‘db.txt‘))
print(‘\033[43m剩余票数%s\033[0m‘ %dic[‘count‘])
def get():
dic=json.load(open(‘db.txt‘))
time.sleep(0.1) #模拟读数据的网络延迟
if dic[‘count‘] >0:
dic[‘count‘]-=1
time.sleep(0.2) #模拟写数据的网络延迟
json.dump(dic,open(‘db.txt‘,‘w‘))
print(‘\033[43m购票成功\033[0m‘)
def task(lock):
search()
get()
if __name__ == ‘__main__‘:
lock=Lock()
for i in range(100): #模拟并发100个客户端抢票
p=Process(target=task,args=(lock,))
p.start()
#文件db的内容为:{"count":1}
#注意一定要用双引号,不然json无法识别
from multiprocessing import Process,Lock
import time,json,random
def search():
dic=json.load(open(‘db.txt‘))
print(‘\033[43m剩余票数%s\033[0m‘ %dic[‘count‘])
def get():
dic=json.load(open(‘db.txt‘))
time.sleep(0.1) #模拟读数据的网络延迟
if dic[‘count‘] >0:
dic[‘count‘]-=1
time.sleep(0.2) #模拟写数据的网络延迟
json.dump(dic,open(‘db.txt‘,‘w‘))
print(‘\033[43m购票成功\033[0m‘)
def task(lock):
search()
lock.acquire()
get()
lock.release()
if __name__ == ‘__main__‘:
lock=Lock()
for i in range(100): #模拟并发100个客户端抢票
p=Process(target=task,args=(lock,))
p.start()
二、多线程