如何释放python删除对象的内存? [英] How to free memory of python deleted object?

问题描述

似乎python3.5不能完全释放任何已删除对象的内存，这可能是因为python内部维护了某种内存池以供重用，但是，我不想重用它们，并且我想释放它们它们使内存可供Linux上运行的其他程序使用.

It seems python3.5 does not completely free memory of any deleted object, this may because of python internally maintaining some kind of memroy pool for reusing purpose, however, I don't want to reuse them, and I want to free them to make memory available for other programs running on linux.

>>> psutil.Process().memory_info().rss / 2**20
11.47265625
>>> d = {x:x for x in range(10**7)}
>>> psutil.Process().memory_info().rss / 2**20
897.1796875
>>> del d
>>> gc.collect()
0
>>> psutil.Process().memory_info().rss / 2**20
15.5859375

这只是一个玩具示例，真正的问题是正在运行的服务器上占用20GB的不可释放内存.

This is just a toy example, the real problem is on a running server, taking 20GB of unfreeable memory.

这是另一个示例:(wd1是带有字符串键的字典的字典)

here is another example: (wd1 is a dict of dict with string keys)

>>> psutil.Process().memory_info().rss / 2**20
28.1796875
>>> wd1 = {x:{i:i for i in d} for x in k}
>>> psutil.Process().memory_info().rss / 2**20
682.78125
>>> del wd1
>>> psutil.Process().memory_info().rss / 2**20
186.21484375

推荐答案

一旦删除对象，就可以对其进行垃圾回收，而不是立即删除-因此，给它一些时间，它将释放或触发gc.collect()加快速度.

Once you delete an object it is available to garbage collected rather than deleted immediately - so just give it some time and it will free up or trigger a gc.collect() to speed things up.

python.exe
Python 3.5.1 (v3.5.1:37a07cee5969, Dec  6 2015, 01:38:48) [MSC v.1900 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import psutil
>>> import gc
>>> psutil.Process().memory_info().rss / 2**20
13.2890625
>>> d = {x:x for x in range(10**7)}
>>> psutil.Process().memory_info().rss / 2**20
359.13671875
>>> del d
>>> psutil.Process().memory_info().rss / 2**20
13.5234375
>>> gc.collect()
0
>>> psutil.Process().memory_info().rss / 2**20
13.4375
>>>

仅供参考，Python 3 shell实际上更像ipython 2，因为历史记录等占用了一定数量的存储空间，仅供参考:

Just for reference the Python 3 shell is actually more like ipython 2 in that there is a certain amount of storage taken up with history, etc., just for reference:

Python 3.5.1 (v3.5.1:37a07cee5969, Dec  6 2015, 01:38:48) [MSC v.1900 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import psutil
>>> psutil.Process().memory_info().rss / 2**20
13.1875
>>> psutil.Process().memory_info().rss / 2**20
13.20703125
>>> psutil.Process().memory_info().rss / 2**20
13.20703125
>>> psutil.Process().memory_info().rss / 2**20
13.20703125
>>> psutil.Process().memory_info().rss / 2**20
13.20703125
>>> 22*3
66
>>> psutil.Process().memory_info().rss / 2**20
13.25390625
>>> import gc
>>> psutil.Process().memory_info().rss / 2**20
13.25390625
>>> gc.collect()
0
>>> psutil.Process().memory_info().rss / 2**20
13.171875
>>>

第二天早上检查在函数中执行dict更新是否不同:

Next Morning to check if doing dict update in a function is different:

>>> psutil.Process().memory_info().rss / 2**20
13.1484375
>>> D = {}
>>> psutil.Process().memory_info().rss / 2**20
13.1484375
>>> def UpdateD(d, v):
...     """ Add the text and value for v to dict d """
...     d[v] = str(v)
...
>>> psutil.Process().memory_info().rss / 2**20
13.16015625
>>> for x in range(10**7):
...     UpdateD(D, x)
...
>>> psutil.Process().memory_info().rss / 2**20
666.6328125
>>> del D
>>> psutil.Process().memory_info().rss / 2**20
10.765625
>>> gc.collect()
0
>>> psutil.Process().memory_info().rss / 2**20
12.8984375
>>>

因此，看来您的生产代码可能挂在了您仍然需要跟踪的引用上.

So it looks like your production code might be hanging onto a reference that you still have to track down.

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