我有一个python脚本，它使用apply_async生成multipe子进程，该脚本使用管道与主进程进行通信。主进程从子进程接收输入，并进行一些处理。主进程也在kafka使用者循环内运行，因此主进程在处理后不会退出。问题在于，当自然生成子进程时，内存消耗会增加，但是当子进程完成时，主进程内存会增加600 MB。在主进程处理完每条kafka消息之后，整个处理周期后内存一直在增加。是否有一种方法可以构建类似的流而无需使用apply_async以及如何知道主进程中有哪些保留内存。

from multiprocessing import Pool,Pipe,Lock
import multiprocessing
lock = Lock()


def processSomethingInmainProcess(p_output):

    while True:
        result = p_output.recv()
        if type(result) == str:
            break
        else:
            """Process something computational expensive
            """
            print(result)


def childProcessspawn(p_input,numbers):
    """Child process for Computing something and send the intermediate result to the main process. 

    Arguments:
        p_input {[type]} -- [description]
        numbers {[type]} -- [description]
    """
    print("Child process called",numbers)
    global lock
    for i in range(numbers,100):
        lock.acquire()
        p_input.send(i)
        lock.release()
    lock.acquire()
    p_input.send("end")
    lock.release()


def mainProcess():
    p_output,p_input = Pipe()
    pool = Pool(multiprocessing.cpu_count()-1)
    test = [1,2,3,4,5]
    for i in test:
        pool.apply_async(childProcessspawn,args=(p_input,i))
    processSomethingInmainProcess(p_output)
    p_input.close()
    p_output.close()
    pool.close()
    pool.join()



## Works in Kafka Consumer loop. 
mainProcess()