MultiProcessing模組是一個優秀的類似多線程MultiThreading模組處理並發的包
之前接觸過一點這個庫，但是並沒有深入研究，這次閒著無聊就研究了一下，算是解惑吧。
今天先研究下apply_async與map方法。傳聞就是這兩個方法分配進程池中的進程給相關函數，我想驗證下。
看下官網對這兩個的解釋：
apply_async(func[, args[, kwds[, callback[, error_callback]]]])
A variant of the apply() method which returns a result object.
If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed. instead.
If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance#exception call#callback is called with the exception instance. since otherwise the thread which handles the results will get blocked.
map(func, iterable[, chunksize])A parallel equivalent of the map() built-in function (it #A parallel equivalent of the map() built-in one iterable argument though). It blocks until the result is ready.
This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (appromate) 款式be specified by setting chunksize to a positive integer.
Pool可以提供指定數量的進程供用戶調用，當有新的請求提交到pool中時，如果池還沒有滿，那麼就會創建一個新的程序用來執行該請求；但如果池中的進程數已經達到規定最大值，那麼該請求就會等待，直到池中有進程結束，才會創建新的進程來運行它
#下面看下程式：
from multiprocessing import Poolimport timeimport osdef func(msg):print('msg: %s %s' % (msg, os.getpid()))
time.sleep(3)print("end")if __name__ == '__main__':
pool = Pool(4)for i in range(4):
msg = 'hello %d' % (i)
pool.apply_async(func, (msg, ))# pool.map(func, range(4))print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")
pool.close()
pool.join() # 调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束print("Sub-process(es) done.")
去掉map註釋，在apply_async函數處加上註解
看下進程池進程不夠的情況下的程式及執行結果：
from multiprocessing import Poolimport timeimport osdef func(msg):print('msg: %s %s' % (msg, os.getpid()))
time.sleep(3)print("end")if __name__ == '__main__':
pool = Pool(3)'''for i in range(4):
msg = 'hello %d' % (i)
pool.apply_async(func, (msg, ))'''pool.map(func, range(4))print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")
pool.close()
pool.join() # 调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束print("Sub-process(es) done.")
可以看到，如果進程池的進程數量大於等於要執行的函數的次數，那就可以很順利，而且看著結果也很理所當然；但是如果進程池的進程的數量小於要運行的函數的次數，那麼就會有一個進程發生阻塞，即兩個或多個函數共用一個進程.
順便吐槽下，全英文的 MultiProcessing官網 看的很懵逼痛苦，又很有意思，不得不說，對英語還是很有幫助的.....
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