python多處理模塊中的map（block）和map_async（非block）有什麼不同？

Question

在計算器的前面的討論：

python-multiprocessing-map-vs-map-async

由於quikst3r說：你會注意到地圖會順序執行，但map_async沒有。

這是我的地圖（塊）vs map_async（非塊）的例子。

map-sync.py代碼。

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() 

有時map_sync不能爲了執行在地圖的功能在這裏。

map-async.py代碼。

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() 

有時map_async可以爲了執行在這裏爲map_async功能。

對於多進程，所有進程都是搶先式多任務處理，無需執行map和map_async命令。

地圖和map_async沒有絕對執行，運行時間幾乎相同--9秒（3 * 3 = 9）。

我們來看看多處理模塊應用功能塊。

apply.py代碼。

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（所有進程阻塞）

我困惑如何證明在多處理模塊地圖和map_async之間的塊和非塊歸因？
map（塊）和map_async（非塊）多處理模塊之間有什麼區別？

Answer 1

也許你誤解了quikst3r的話。

池創建許多工作進程來做某件事情，關於是否阻塞，它表示工作進程是否會阻塞主進程。並且map將阻止主進程util完成其工作，並且map_asyc不會阻塞主進程，所以在map_async中，所有進程都將同時進行。他們不確保工作流程的順序。

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() 

如果是地圖，輸出將是這樣的：

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 

而如果是map_async，輸出爲：

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 

至於申請或apply_async，這是關於塊的含義與map或map_async相同。這是說工人進程阻止了主流程。