python multiprocessing.pool.apply_async 占用内存多 解决方法

multiprocessing.pool.apply_async 可以执行并行的进程，但是会将所有进程先读入列表，对于不是很多数量的进程来说没有问题，但是如果进程数量很多，比如100万条，1000万条，而进程不能很快完成，内存就会占用很多，甚至挤爆内存。那么如何限制内存的占有量呢。网上查询，找到一种解决方法：可以检测pool._cache的长度，如果超过一定的长度，就让最后进入pool中的进程等待，等这个进程结束，再读入一定长度的进程，以达到减少内存占有的目的。

from multiprocessing import Pool
import time

def downloadGif(arg):
    print(arg[0])
    time.sleep(1)

def downloading_over(arg):
    pass

def foo(num):
    for i in range(num,1000001):
        pic_info=[]
        pic_info.append(str(i)+'gif')

        txt_info=[]
        txt_info.append(str(i)+'txt')
        yield pic_info,txt_info

if __name__ == '__main__':
    pool = Pool(processes=5)    # set the processes max number
    count=1
    for download in foo(2):
        pool.apply_async(func=downloadGif, args=(download[0],),callback=downloading_over)
        last=pool.apply_async(func=downloadGif, args=(download[1],),callback=downloading_over)

        count=count+1
        print(count)

        if len(pool._cache) > 1e3:
            print("waiting for cache to clear...")
            last.wait()

#1e3,500条,占有内存10M
#1e4,5000条,占有内存20M
#1e5,50000条,占有内存200M
#1e6,500000条,占有内存2000M

    pool.close()
    pool.join()

核心代码：

        if len(pool._cache) > 1e3:
            print("waiting for cache to clear...")
            last.wait()

last 是 AsyncResult的实例，是pool的返回值

https://docs.python.org/3/library/multiprocessing.html

classmultiprocessing.pool.AsyncResult¶

The class of the result returned by Pool.apply_async() and Pool.map_async().

get([timeout])

Return the result when it arrives. If timeout is not None and the result does not arrive within timeout seconds then multiprocessing.TimeoutError is raised. If the remote call raised an exception then that exception will be reraised by get().

wait([timeout])

Wait until the result is available or until timeout seconds pass.

ready()

Return whether the call has completed.

successful()

Return whether the call completed without raising an exception. Will raise ValueError if the result is not ready.

本文参考下面链接回答：

https://stackoverflow.com/questions/18414020/memory-usage-keep-growing-with-pythons-multiprocessing-pool