[Python-Dev] Discussion related to memory leaks requested

[Matthew Paulson]

Hi Andrew:

These are all good points, and I defer to your experience -- I am new to 
python internals, but the fact remains that after multiple iterations of 
our embedded test case, we are seeing continued allocations (DS2015) and 
growth of the working set (windows task manager).  If your are pooling 
resources on the free list, wouldn't you expect these items to get 
reused and for things to stabilize after a while?  We're not seeing that.

I think Victor's suggestion of a very simple test case is probably the 
best idea.  I'll try to put that together in the next few days and if it 
also demonstrates the problem, then I'll submit it here.

Thanks for your time and help.

Best,

Matt

On 1/13/2016 6:45 PM, Andrew Barnert wrote:
> On Jan 13, 2016, at 14:49, Matthew Paulson <paulson at busiq.com 
> <mailto:paulson at busiq.com>> wrote:
>
>> Hi Victor:
>>
>> No, I'm using the new heap analysis functions in DS2015.
>
> Isn't that going to report any memory that Python's higher level 
> allocators hold in their freelists as leaked, even though it isn't leaked?
>
>> We think we have found one issue. In the following sequence, dict has 
>> no side effects, yet it is used -- unless someone can shed light on 
>> why dict is used in this case:
>
> Where do you see an issue here? The dict will have one ref, so the 
> decref at the end should return it to the freelist.
>
> Also, it looks like there _is_ a side effect here. When you add a 
> bunch of elements to a dict, it grows. When you delete a bunch of 
> elements, it generally doesn't shrink. But when you clear the dict, it 
> does shrink. So, copying it to a temporary dict, clearing it, updating 
> it from the temporary dict, and then releasing the temporary dict 
> should force it to shrink.
>
> So, the overall effect should be that you have a smaller hash table 
> for the builtins dict, and a chunk of memory sitting on the freelists 
> ready to be reused. If your analyzer is showing the freelists as 
> leaked, this will look like a net leak rather than a net recovery, but 
> that's just a problem in the analyzer.
>
> Of course I could be wrong, but I think the first step is to rule out 
> the possibility that you're measuring the wrong thing...
>
>> /* Clear the modules dict. */
>>     PyDict_Clear(modules);
>>     /* Restore the original builtins dict, to ensure that any
>>        user data gets cleared. */
>>     dict = PyDict_Copy(interp->builtins);
>>     if (dict == NULL)
>>         PyErr_Clear();
>>     PyDict_Clear(interp->builtins);
>>     if (PyDict_Update(interp->builtins, interp->builtins_copy))
>>         PyErr_Clear();
>>     Py_XDECREF(dict);
>>
>> And removing dict from this sequence seems to have fixed one of the 
>> issues, yielding 14k per iteration.
>
>> Simple program: Good idea.  We will try that -- right now it's 
>> embedded in a more complex environment, but we have tried to strip it 
>> down to a very simple sequence.
>>
>> The next item on our list is memory that is not getting freed after 
>> running simple string.  It's in the parsertok sequence -- it seems 
>> that the syntax tree is not getting cleared -- but this opinion is 
>> preliminary.
>>
>> Best,
>>
>> Matt
>>
>> On 1/13/2016 5:10 PM, Victor Stinner wrote:
>>> Hi,
>>>
>>> 2016-01-13 20:32 GMT+01:00 Matthew Paulson<paulson at busiq.com>:
>>>> I've spent some time performing memory leak analysis while using Python in an embedded configuration.
>>> Hum, did you try tracemalloc?
>>>
>>> https://docs.python.org/dev/library/tracemalloc.html
>>> https://pytracemalloc.readthedocs.org/
>>>
>>>> Is there someone in the group that would like to discuss this topic.  There seems to be other leaks as well.  I'm new to Python-dev, but willing to help or work with someone who is more familiar with these areas than I.
>>> Are you able to reproduce the leak with a simple program?
>>>
>>> Victor
>>>
>>>
>>
>> -- 
>> <MattSig.JPG>
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