objgraph is a module that lets you visually explore Python object graphs.
You’ll need graphviz if you want to draw the pretty graphs.
I recommend xdot for interactive use. pip install xdot should suffice; objgraph will automatically look for it in your PATH.
pip install objgraph or download it from PyPI.
Documentation lives at http://mg.pov.lt/objgraph.
Try this in a Python shell:
>>> x = []
>>> y = [x, [x], dict(x=x)]
>>> import objgraph
>>> objgraph.show_refs([y], filename='sample-graph.png')
Graph written to ....dot (5 nodes)
Image generated as sample-graph.png
(If you’ve installed xdot, omit the filename argument to get the interactive viewer.)
You should see a graph like this:
Now try
>>> objgraph.show_backrefs([x], filename='sample-backref-graph.png')
Graph written to ....dot (8 nodes)
Image generated as sample-backref-graph.png
and you’ll see
To get a quick overview of the objects in memory
>>> objgraph.show_most_common_types()
tuple 5351
function 1369
wrapper_descriptor 967
dict 786
...
The original purpose of objgraph was to help me find memory leaks. The idea was to pick an object in memory that shouldn’t be there and then see what references are keeping it alive.
>>> class MyBigFatObject(object):
... pass
>>> def computate_something(_cache={}):
... _cache[42] = dict(foo=MyBigFatObject(),
... bar=MyBigFatObject())
... # a very explicit and easy-to-find "leak" but oh well
Let’s take a look at object counts before we call our function
>>> objgraph.show_growth()
tuple 12272 +12272
function 3308 +3308
dict 1915 +1915
...
>>> computate_something()
and after
>>> objgraph.show_growth()
wrapper_descriptor 970 +14
tuple 12282 +10
dict 1922 +7
member_descriptor 170 +4
getset_descriptor 191 +3
list 491 +2
MyBigFatObject 2 +2
method_descriptor 343 +1
Now suppose I notice in the list above that there are MyBigFatObject instances in memory where there should be none. I can pick one of them and trace the reference chain back to one of the garbage collector’s roots.
For simplicity’s sake let’s assume all of the roots are modules; if you’ve any examples where that isn’t true, I’d love to hear about them.
>>> import inspect, random
>>> objgraph.show_chain(
... objgraph.find_backref_chain(
... random.choice(objgraph.by_type('MyBigFatObject')),
... inspect.ismodule),
... filename='chain.png')
Graph written to ...dot (13 nodes)
Image generated as chain.png
There are other tools, perhaps better suited for memory leak hunting: heapy, Dozer.
I’ve developed a set of functions that eventually became objgraph when I was hunting for memory leaks in a Python program. The whole story – with illustrated examples – is in this series of blog posts:
And here’s the change log
The source code can be found in this Bazaar repository: https://code.launchpad.net/~mgedmin/objgraph/trunk.
To check it out, use bzr branch lp:objgraph.
Report bugs at https://bugs.launchpad.net/objgraph.
For more information, see Hacking on objgraph.