I've followed excellent post here how to setup heapy with Django: http://www.toofishes.net/blog/using-guppy-debug-django-memory-leaks/
I've commanded hp.setref() and now after awhile I get also data with hp.heap():
>>> hp.heap()
Partition of a set of 12075 objects. Total size = 1515496 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 4048 34 339656 22 339656 22 str
1 3112 26 269368 18 609024 40 tuple
2 171 1 169992 11 779016 51 dict (no owner)
3 1207 10 144440 10 923456 61 list
4 49 0 102040 7 1025496 68 dict of module
5 591 5 66984 4 1092480 72 unicode
6 498 4 59760 4 1152240 76 function
7 433 4 51960 3 1204200 79 types.CodeType
8 57 0 50480 3 1254680 83 type
9 36 0 31584 2 1286264 85 dict of class
But what now? What I should understand from this output? How I should start tracking down where those 'str' and 'tuple' objects belong?
With get_rp, I get following output:
>>> hp.heap().get_rp()
Reference Pattern by <[dict of] class>.
0: _ --- [-] 12000 (0xd1d340 | 0xd88b50 | 0xf63f00 | __builtin__.Struct | __...
1: a [-] 137 dict (no owner): 0x761c30*160, 0x7655d0*1491, 0x781640*9...
2: aa ---- [-] 45 dict of django.db.models.options.Options: 0xcf3110...
3: a3 [-] 45 django.db.models.options.Options: 0xcf3110, 0xf0bb10...
4: a4 ------ [-] 140 dict of django.db.models.related.RelatedObject: 0x10bec...
5: a5 [-] 140 django.db.models.related.RelatedObject: 0xf14450...
6: a6 -------- [-] 63 dict of django.db.models.fields.related.ForeignKey: 0x...
7: a7 [+] 63 django.db.models.fields.related.ForeignKey: 0xf0e690...
8: a5b ------- [-] 7 dict of django.db.models.fields.related.OneToOneField: ...
9: a5ba [+] 7 django.db.models.fields.related.OneToOneField: 0x15447...
Is the correct assumption now that it's Django that is leaking memory? But what are those dict's who don't have owner?
I don't have any experience with heapy, but in my experience, Django (and most other Python programs) don't leak memory, but they also don't clean up memory as pristinely as some would like.
Also, Django has settings that cause it to consume memory for diagnostic reasons. For example, setting DEBUG=True can cause it to hold on to all SQL queries, so the longer the process runs, the more memory it uses.
UPDATE: Your problem isn't in your Python code. Look at the summary heapy is giving you: the total size of memory represented there is 1.5Mb! When Python programs truly leak, the most common cause is a leaky C extension. Do you have any C extensions that you are running under your Django process?