Python: How to estimate / calculate memory footprint of data structures?
What's a good way to estimate the memory footprint of an object?
Conversely, what's a good way to measure the footprint?
For example, say I have a dictionary whose values are lists of integer,float tuples:
d['key'] = [ (1131, 3.11e18), (9813, 2.48e19), (4991, 9.11e18) ]
I have 4G of physical memory and would like to figure out approximately how many rows (key:values) I can store in memory before I spill into swap. This is on linux/ubuntu 8.04 and OS X 10.5.6 .
Also, what's the best way to figure out the actual in-memory footprint of my program? How do I best figure out when it's exhausting physical memory and spilling?
Guppy has a nice memory profiler (Heapy):
>>> from guppy import hpy
>>> hp = hpy()
>>> hp.setrelheap() # ignore all existing objects
>>> d = {}
>>> d['key'] = [ (1131, 3.11e18), (9813, 2.48e19), (4991, 9.11e18) ]
>>> hp.heap()
Partition of a set of 24 objects. Total size = 1464 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 2 8 676 46 676 46 types.FrameType
1 6 25 220 15 896 61 str
2 6 25 184 13 1080 74 tuple
...
Heapy is a little underdocumented, so you might have to dig through the web page or source code a little, but it's very powerful. There are also some articles which might be relevant.
You can do this with a memory profiler, of which there are a couple I'm aware of:
PySizer - poissibly obsolete, as the homepage now recommends:
Heapy.
This is possibly a duplicate of this question.
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