当我在for循环中加载大型pickle文件时，如何防止内存泄漏？

Question

I have 50 pickle files that are 0.5 GB each. Each pickle file is comprised of a list of custom class objects. I have no trouble loading the files individually using the following function:

def loadPickle(fp):
    with open(fp, 'rb') as fh:
        listOfObj = pickle.load(fh)
    return listOfObj

However, when I try to iteratively load the files I get a memory leak.

l = ['filepath1', 'filepath2', 'filepath3', 'filepath4']
for fp in l:
    x = loadPickle(fp)
    print( 'loaded {0}'.format(fp) )

My memory overflows before loaded filepath2 is printed. How can I write code that guarantees that only a single pickle is loaded during each iteration?

Answers to related questions on SO suggest using objects defined in the weakref module or explicit garbage collection using the gc module, but I am having a difficult time understanding how I would apply these methods to my particular use case. This is because I have an insufficient understanding of how referencing works under the hood.

Related: Python garbage collection

Answer 1

You can fix that by adding x = None right after for fp in l: .

The reason this works is because it will dereferenciate variable x , hance allowing the python garbage collector to free some virtual memory before calling loadPickle() the second time.