Reducing Django Memory Usage. Low hanging fruit?

Make sure you are not keeping global references to data. That prevents the python garbage collector from releasing the memory.

Don't use mod_python. It loads an interpreter inside apache. If you need to use apache, use mod_wsgi instead. It is not tricky to switch. It is very easy. mod_wsgi is way easier to configure for django than brain-dead mod_python.

If you can remove apache from your requirements, that would be even better to your memory. spawning seems to be the new fast scalable way to run python web applications.

EDIT: I don't see how switching to mod_wsgi could be "tricky". It should be a very easy task. Please elaborate on the problem you are having with the switch.


If you are running under mod_wsgi, and presumably spawning since it is WSGI compliant, you can use Dozer to look at your memory usage.

Under mod_wsgi just add this at the bottom of your WSGI script:

from dozer import Dozer
application = Dozer(application)

Then point your browser at http://domain/_dozer/index to see a list of all your memory allocations.

I'll also just add my voice of support for mod_wsgi. It makes a world of difference in terms of performance and memory usage over mod_python. Graham Dumpleton's support for mod_wsgi is outstanding, both in terms of active development and in helping people on the mailing list to optimize their installations. David Cramer at curse.com has posted some charts (which I can't seem to find now unfortunately) showing the drastic reduction in cpu and memory usage after they switched to mod_wsgi on that high traffic site. Several of the django devs have switched. Seriously, it's a no-brainer :)


Additionally, check if you do not use any of known leakers. MySQLdb is known to leak enormous amounts of memory with Django due to bug in unicode handling. Other than that, Django Debug Toolbar might help you to track the hogs.


These are the Python memory profiler solutions I'm aware of (not Django related):

  • Heapy
  • pysizer (discontinued)
  • Python Memory Validator (commercial)
  • Pympler

Disclaimer: I have a stake in the latter.

The individual project's documentation should give you an idea of how to use these tools to analyze memory behavior of Python applications.

The following is a nice "war story" that also gives some helpful pointers:

  • Reducing the footprint of python applications