python 多进程 Python并发之多进程的方法实例代码

from multiprocessing import Process
def foo(name):
 print('hello,%s'%name)
if __name__ == '__main__':
 p1=Process(target=foo,args=('world',))
 p2 = Process(target=foo, args=('China',))
 p1.start()
 p2.start()
 print('=====主进程=====')
 # == == =主进程 == == =
 # hello, world
 # hello, China
 #主进程和子进程并发执行 

from multiprocessing import Process
class MyProcess(Process):
 def __init__(self,name):
  super().__init__()
  self.name = name
 def run(self):
  print('hello,%s'%self.name)
if __name__ == '__main__':
 myprocess1 = MyProcess('world')
 myprocess2 = MyProcess('world')
 myprocess1.start()
 myprocess2.start()

from multiprocessing import Process
import time
class MyProcess(Process):
 def __init__(self,name):
  super().__init__()
  self.name = name
 def run(self):
  time.sleep(3)
  print('hello,%s'%self.name)
if __name__ == '__main__':
 myprocess1=MyProcess('world')
 myprocess1.daemon = True
 myprocess1.start()
 print('结束')
#不会输出‘hello world'，因为设置为守护进程，主进程不会等待

from multiprocessing import Process
import time
class MyProcess(Process):
 def __init__(self,name):
  super().__init__()
  self.name = name
 def run(self):
  time.sleep(3)
  print('hello,%s'%self.name)
if __name__ == '__main__':
 myprocess1=MyProcess('world')
 myprocess1.daemon = True
 myprocess1.start()
 myprocess1.join() #程序阻塞
 print('结束')
join()

from multiprocessing import Process,Lock
import time
def a_print(l): #需要传入对象，因为信息不共享
 l.acquire()
 print('我要打印信息')
 time.sleep(1)
 print('我打印完了')
 l.release()
if __name__ == '__main__':
 l = Lock()
 for i in range(20):
  p = Process(target=a_print,args=(l,))
  p.start()

from multiprocessing import Process,Queue,Semaphore
import time,random
def seat(s,n):
 s.acquire()
 print('学生%d坐下了'%n)
 time.sleep(random.randint(1,2))
 s.release()
if __name__ == '__main__':
 s = Semaphore(5)
 for i in range(20):
  p = Process(target=seat,args=(s,i))
  p.start()
 print('-----主进程-------')

from multiprocessing import Process,Event
import time, random
def eating(event):
 event.wait()
 print('去吃饭的路上...')
def makeing(event):
 print('做饭中')
 time.sleep(random.randint(1,2))
 print('做好了，快来...')
 event.set()
if __name__ == '__main__':
 event=Event()
 t1 = Process(target=eating,args=(event,))
 t2 = Process(target=makeing,args=(event,))
 t1.start()
 t2.start()
 # 做饭中
 # 做好了，快来...
 # 去吃饭的路上...

from multiprocessing import Process,Queue
import time
def func1(queue):
 while True:
  info=queue.get()
  if info == None:
   return 
  print(info)
def func2(queue):
 for i in range(10):
  time.sleep(1)
  queue.put('is %d'%i)
 queue.put(None) #结束的标志
if __name__ == '__main__':
 q = Queue()
 p1 = Process(target=func1,args=(q,))
 p2 = Process(target=func2, args=(q,))
 p1.start()
 p2.start()
Queue类的方法，源码如下：
class Queue(object):
 def __init__(self, maxsize=-1): #可以传参设置队列最大容量
  self._maxsize = maxsize
 def qsize(self): #返回当前时刻队列中的个数
  return 0
 def empty(self): #是否为空
  return False
 def full(self): 是否满了
  return False
 def put(self, obj, block=True, timeout=None): #放值,blocked和timeout。如果blocked为True（默认值），并且timeout为正值，该方法会阻塞timeout指定的时间，直到该队列有剩余的空间。如果超时，会抛出Queue.Full异常。如果blocked为False，但该Queue已满，会立即抛出Queue.Full异常
  pass
 def put_nowait(self, obj): #=put(False)
  pass
 def get(self, block=True, timeout=None): 获取值,get方法有两个可选参数：blocked和timeout。如果blocked为True（默认值），并且timeout为正值，那么在等待时间内没有取到任何元素，会抛出Queue.Empty异常。如果blocked为False，有两种情况存在，如果Queue有一个值可用，则立即返回该值，否则，如果队列为空，则立即抛出Queue.Empty异常.
  pass
 def get_nowait(self): # = get(False)
  pass
 def close(self): #将队列关闭
  pass
 def join_thread(self): #略，几乎不用
  pass
 def cancel_join_thread(self):
  pass

from multiprocessing import Pool
import time, random, os
def func(n):
 pid = os.getpid()
 print('进程%s正在处理第%d个任务'%(pid,n),'时间%s'%time.strftime('%H-%M-%S'))
 time.sleep(2)
 res = '处理%s'%random.choice(['成功','失败'])
 return res
if __name__ == '__main__':
 p = Pool(4) #创建4个进程，
 li = []
 for i in range(10):
  res = p.apply(func,args=(i,)) 交给进程池处理，处理完成才返回值，会阻塞，即使池内还有空余进程，相当于顺序执行
  li.append(res)
 for i in li:
  print(i)

from multiprocessing import Pool
import time, random, os
def func(n):
 pid = os.getpid()
 print('进程%s正在处理第%d个任务'%(pid,n),'时间%s'%time.strftime('%H-%M-%S'))
 time.sleep(2)
 res = '处理%s'%random.choice(['成功','失败'])
 return res

if __name__ == '__main__':
 p = Pool(4)
 li = []
 for i in range(10):
  res = p.apply_async(func,args=(i,)) 结果不会立刻返回，遇到阻塞，开启下一个进程，在这，相当于几乎同时出现四个打印结果（一个线程处理一个任务，处理完下个任务才能进来）
  li.append(res)

 p.close() #join之前需要关闭进程池
 p.join() #因为异步，所以需要等待池内进程工作结束再继续
 for i in li:
  print(i.get()) #i是一个对象，通过get方法获取返回值，而同步则没有该方法

from multiprocessing import Pool
import time, random, os
def func(n):
 pid = os.getpid()
 print('进程%s正在处理第%d个任务'%(pid,n),'时间%s'%time.strftime('%H-%M-%S'))
 time.sleep(2)
 res = '处理%s'%random.choice(['成功','失败'])
 return res

def foo(info):
 print(info) #传入值为进程执行结果

if __name__ == '__main__':
 p = Pool(4)
 li = []
 for i in range(10):
  res = p.apply_async(func,args=(i,)，callback = foo) callback()回调函数会在进程执行完之后调用（主进程调用） 
  li.append(res)

 p.close() 
 p.join() 
 for i in li:
  print(i.get())