简单谈谈python中的多进程


            进程是由系统自己管理的。

1：最基本的写法





from multiprocessing import Pool



def f(x):

  return x*x



if __name__ == '__main__':

  p = Pool(5)

  print(p.map(f, [1, 2, 3]))

[1, 4, 9]







2、实际上是通过os.fork的方法产生进程的

unix中，所有进程都是通过fork的方法产生的。





multiprocessing Process

os



info(title):

  title

  , __name__

  (os, ): , os.getppid()

  , os.getpid()



f(name):

  info()

  , name



__name__ == :

  info()

  p = Process(=f, =(,))

  p.start()

  p.join()







3、线程共享内存





threading



run(info_list,n):

  info_list.append(n)

  info_list



__name__ == :

  info=[]

  i ():

    p=threading.Thread(=run,=[info,i])

    p.start()

[0]

[0, 1]

[0, 1, 2]

[0, 1, 2, 3]

[0, 1, 2, 3, 4]

[0, 1, 2, 3, 4, 5]

[0, 1, 2, 3, 4, 5, 6]

[0, 1, 2, 3, 4, 5, 6, 7]

[0, 1, 2, 3, 4, 5, 6, 7, 8]

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]







进程不共享内存：





multiprocessing Process

run(info_list,n):

  info_list.append(n)

  info_list



__name__ == :

  info=[]

  i ():

    p=Process(=run,=[info,i])

    p.start()

[1]

[2]

[3]

[0]

[4]

[5]

[6]

[7]

[8]

[9]







若想共享内存，需使用multiprocessing模块中的Queue





multiprocessing Process, Queue

f(q,n):

  q.put([n,])



__name__ == :

  q=Queue()

  i ():

    p=Process(=f,=(q,i))

    p.start()

  :

    q.get()







4、锁:仅是对于屏幕的共享，因为进程是独立的，所以对于多进程没有用





multiprocessing Process, Lock

f(l, i):

  l.acquire()

  , i

  l.release()



__name__ == :

  lock = Lock()



  num ():

    Process(=f, =(lock, num)).start()

hello world 0

hello world 1

hello world 2

hello world 3

hello world 4

hello world 5

hello world 6

hello world 7

hello world 8

hello world 9







5、进程间内存共享：Value，Array





multiprocessing Process, Value, Array



f(n, a):

  n.value = i ((a)):

    a[i] = -a[i]



__name__ == :

  num = Value(, )

  arr = Array(, ())



  num.value

  arr[:]



  p = Process(=f, =(num, arr))

  p.start()

  p.join()

0.0

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

3.1415927

[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]







#manager共享方法，但速度慢





multiprocessing Process, Manager



f(d, l):

  d[] = d[] = d[] = l.reverse()



__name__ == :

  manager = Manager()



  d = manager.dict()

  l = manager.list(())



  p = Process(=f, =(d, l))

  p.start()

  p.join()



  d

  l

# print '-------------'这里只是另一种写法

# print pool.map(f,range(10))

{0.25: None, 1: '1', '2': 2}

[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]







#异步：这种写法用的不多





multiprocessing Pool

time

f(x):

  x*x

  time.sleep()

  x*x



__name__ == :

  pool=Pool(=)

  res_list=[]

  i ():

    res=pool.apply_async(f,[i])  res_list.append(res)



  r res_list:

    r.get(timeout=10) #超时时间







同步的就是apply