Question

在之前的stackoverflow讨论中：

正如quikst3r所说：你会注意到map会按顺序执行，但map_async不会。

这是我的map（block）vs map_async（non block）的例子。

map-sync.py代码。

import multiprocessing
import os
import time
from datetime import datetime

def subprocess(number):
    print('this is the %d th subprocess' % number)
    time.sleep(3)  

def mainprocess():
    print('this is the main process ,process number is : %d' % os.getpid())
    pool = multiprocessing.Pool(3)
    list=range(9)
    pool.map(subprocess,list)
    pool.close()
    pool.join()

if __name__ == '__main__':
    mainprocess()

有时map_sync不能执行以便在这里执行map函数。

map-async.py code。

import multiprocessing
import os
import time
from datetime import datetime

def subprocess(number):
    print('this is the %d th subprocess' % number)
    time.sleep(3)  

def mainprocess():
    print('this is the main process ,process number is : %d' % os.getpid())
    pool = multiprocessing.Pool(3)
    list=range(9)
    pool.map_async(subprocess,list)
    pool.close()
    pool.join()

if __name__ == '__main__':
    mainprocess()

有时map_async可以在这里执行map_async函数。

对于多处理，所有进程都是抢先式多任务处理，没有map和map_async的顺序。

map和map_async没有绝对执行，运行时间几乎相同--9秒（3 * 3 = 9）。

让我们看一下多处理模块应用功能中的块。

apply.py code。

import multiprocessing
import os
import time
from datetime import datetime

def subprocess(number):
    print('this is the %d th subprocess' % number)
    time.sleep(3)  

def mainprocess():
    print('this is the main process ,process number is : %d' % os.getpid())
    pool = multiprocessing.Pool(3)
    for i in range(9):    
        pool.apply(subprocess,args=(i,))
    pool.close()
    pool.join()

if __name__ == '__main__':
    mainprocess()

27 = 3 * 9（所有进程都被阻止）

我很困惑如何在多处理模块中演示map和map_async之间的块和非块属性？
map（块）和map_async（非块）多处理模块之间有什么区别？

Answer 1

也许你误解了quikst3r的话。

pool创建许多工作进程来执行某些操作，关于是否阻止，这意味着工作进程是否会阻止主进程。 map会阻止主进程使用它完成其工作，而map_asyc不会阻塞主进程，所以在map_async中所有进程都将同时进行。他们无法确保工人流程的顺序。

 import multiprocessing
import os   
import time 
from datetime import datetime

def subprocess(number):
    print('this is the %d th subprocess' % number)
    time.sleep(3)  

def mainprocess():
    print('this is the main process ,process number is : %d' % os.getpid())
    pool = multiprocessing.Pool(3)
    list=range(9)
    pool.map(subprocess,list)    # or map_aysnc                                                                                                                                                             
    print "hallo"
    print "hallo again"
    pool.close()
    pool.join()

if __name__ == '__main__':
    mainprocess()

如果是map，输出将如下：

this is the main process ,process number is : 24812
this is the 0 th subprocess
this is the 1 th subprocess
this is the 2 th subprocess
this is the 3 th subprocess
this is the 4 th subprocess
this is the 5 th subprocess
this is the 6 th subprocess
this is the 7 th subprocess
this is the 8 th subprocess
hallo
hallo again

如果是map_async，则输出为：

this is the main process ,process number is : 24775
hallo
hallo again
this is the 0 th subprocess
this is the 1 th subprocess
this is the 2 th subprocess
this is the 3 th subprocess
this is the 4 th subprocess
this is the 5 th subprocess
this is the 6 th subprocess
this is the 7 th subprocess
this is the 8 th subprocess

对于apply或apply_async，它对block的含义与map或map_async相同。它说工人进程是否阻止主要进程。

python多处理模块中map（block）和map_async（非块）之间有什么区别？

1 个答案: