Tutorial - Track Down Memory Leaks¶
This tutorial shows you ways in which muppy can be used to track down memory leaks. From my experience, this can be done in 3 steps, each answering a different question.
Is there a leak?
What objects leak?
Where does it leak?
My first real-life test for muppy was IDLE, which is “the Python IDE built with the Tkinter GUI toolkit.” It offers the following features:
coded in 100% pure Python, using the Tkinter GUI toolkit
cross-platform: works on Windows and Unix (on Mac OS, there are currently problems with Tcl/Tk)
multi-window text editor with multiple undo, Python colorizing and many other features, e.g. smart indent and call tips
Python shell window (a.k.a. interactive interpreter)
debugger (not complete, but you can set breakpoints, view and step)
Because it is integrated in every Python distribution, runs locally and provides easy interactive feedback, it was a nice first candidate to test the tools of muppy.
The task was to check if IDLE leaks memory, if so, what objects are leaking, and finally, why are they leaking.
IDLE is part of every Python distribution and can be found at
Lib/idlelib. The modified version which makes use of muppy can be found
With IDLE having a GUI, I also wanted to be able to interact with muppy through
the GUI. This can be done in
Lib/idlelib/PyShell.py. For details, please refer to the modified
version mentioned above.
Task 1: Is there a leak?¶
At first, we need to find out if there are any objects leaking at all. We will have a look at the objects, invoke an action, and look at the objects again.
from pympler import tracker self.memory_tracker = tracker.SummaryTracker() self.memory_tracker.print_diff()
The last step is repeated after each invocation. Let’s start with something simple which should not leak. We will check the Windows resize. You can invoke it in the menu at Windows->Zoom Height.
At first call print_diff till it has calibrated. That is, the first one or two times, you will get some output because there is still something going on in the background. But then you should get this:
types | # objects | total size ====== | =========== | ============
Which means nothing has changed since the last invocation of print_diff. Now let’s call Windows->Zoom Height and invoke print_diff again.:
types | # objects | total size ================== | =========== | ============ dict | 1 | 280 B list | 1 | 176 B _sre.SRE_Pattern | 1 | 88 B tuple | 1 | 80 B str | 0 | 7 B
Seems as this requires some of the above mentioned objects. Let’s repeat it.:
types | # objects | total size ====== | =========== | ============
Okay, nothing changed, so nothing is leaking. But we see that often, the first call to a function creates some objects, which then exist on a second invocation.
Next, we try something different. We will open a new window. Let’s have a look at the Path Browser at File->Path Browser.:
types | # objects | total size ===================================================== | =========== | ============ dict | 18 | 14.26 KB tuple | 146 | 13.17 KB list | 2 | 11.67 KB str | 97 | 7.85 KB code | 46 | 5.52 KB function | 45 | 5.40 KB classobj | 9 | 864 B instancemethod (<function wakeup>) | 3 | 240 B instancemethod (<function __call__>) | 3 | 240 B instance(<class Tkinter.CallWrapper>) | 3 | 216 B module | 3 | 168 B instance(<class idlelib.WindowList.ListedToplevel>) | 1 | 72 B
Let’s repeat it.:
types | # objects | total size ===================================================== | =========== | ============ dict | 5 | 2.17 KB list | 0 | 384 B str | 5 | 259 B instancemethod (<function wakeup>) | 3 | 240 B instancemethod (<function __call__>) | 3 | 240 B instance(<class Tkinter.CallWrapper>) | 3 | 216 B instance(<class idlelib.WindowList.ListedToplevel>) | 1 | 72 B
Mh, still some new objects. Repeating this procedure several times will reveal that here indeed we have a leak.
Task 2: What objects leak?¶
So let’s have a closer look at the diff. We see 5 new dicts and strings, a bit more memory usage by lists, 3 wakeup and __call__ instance methods, 3 CallWrapper and 1 ListedToplevel. We know the standard types, but the last couple of objects seem to be from IDLE.
We ignore the standard type objects for now. It is more likely that these are only children of some other instances which are causing the leak.
We start with the ListedTopLevel object. One invocation of File->Path Browser and one more of this type looks like this object is not garbage collected, although it should have been. Searching for ListedTopLevel in idlelib/ reveals that is the base class to all window objects of IDLE. We can assume that opening the Path Browser, a new window object is created, but closing the window does not remove the reference.
Next, we take a look at the wakeup instance method of which we have three more on each invocation. Searching the code, we find it to be defined in idlelib/WindowList.py. This piece of code is used to give users of IDLE a list of currently open windows. Every time a new window is created, it will be added to the Windows menu, from where the user can select any open window. wakeup is the method which will bring the selected window up front. Adding a window calls menu.add_command, linking menu and the wakeup command together.
So we are getting closer. Only __call__ and Tkinter.CallWrapper are left. As the name indicates, the latter is located in the Tkinter module, which is part of the standard library of Python. So let’s dive into it. The CallWrapper docstring states:
Internal class. Stores function to call when some user defined Tcl function is called e.g. after an event occurred.
Also, CallWrapper contains a method called __call__, which is used to invoke the stored function call. A CallWrapper is created by the method _register which then creates a command (Tk speak) and adds it’s name to a list called self._tclCommands.
So what do we know so far? Every time a Path Browser is opened, a window is created, but not deleted when closed again. It has something to do with the wakeup method of the window. This method is wrapped as a Tcl command and then linked to the window list menu. Also, we have traced this wrapping back to Tkinter library, where names of the function wrappers are stored in an attribute called _tclCommands.
This brings us to the third question.
Task 3: Where is the leak?¶
_tclCommands stores the names of all commands linked to a widget. The base class for interior widgets (of which the menu is one), has a method called destroy which:
Delete all Tcl commands created for this widget in the Tcl interpreter.
as well as a method deletecommand which deletes a single Tcl command. Both remove commands as by there name. Among them, we find our CallWrappers’ __call__ used to wrap the wakeup of the Path Browser window.
So we should expect at least either one to be invoked when a window is closed (best would be the invocation of only deletecommand). This would also go in line with menu.add_command we identified above. And indeed, in idlelib/EditorWindow.py, menu.delete is called. So where is the problem?
We return to Tkinter.py and have a closer look at delete method:
def delete(self, index1, index2=None): """Delete menu items between INDEX1 and INDEX2 (not included).""" self.tk.call(self._w, 'delete', index1, index2)
Mh, it looks like the menu item is deleted, but what about the attached command? Let’s ask the Web for “tkinter deletecommand”. Turns out that somebody some years ago filed a bug (see bugreport) which states:
Tkinter.Menu.delete does not delete the commands defined for the entries it deletes. Those objects will be retained until the menu itself is deleted. [..] the command function will still be referenced and kept in memory - until the menu object itself is destroyed.
Well, this seems to be the root of our memory leak. Let’s adapt the delete method a bit, so that the associated commands are deleted as well:
def delete(self, index1, index2=None): """Delete menu items between INDEX1 and INDEX2 (not included).""" if index2 is None: index2 = index1 cmds =  (num_index1, num_index2) = (self.index(index1), self.index(index2)) if (num_index1 is not None) and (num_index2 is not None): for i in range(num_index1, num_index2 + 1): if 'command' in self.entryconfig(i): c = str(self.entrycget(i, 'command')) if c in self._tclCommands: cmds.append(c) self.tk.call(self._w, 'delete', index1, index2) for c in cmds: self.deletecommand(c)
Now we restart IDLE, calibrate our tracker and do another round of print_diff. After the first time the Path Browser is opened we get this:
types | # objects | total size ========== | =========== | ============ tuple | 146 | 13.17 KB dict | 13 | 12.01 KB list | 2 | 11.26 KB str | 92 | 7.59 KB code | 46 | 5.52 KB function | 45 | 5.40 KB classobj | 9 | 864 B module | 3 | 168 B
Okay, still some objects created, but no more instances and instance methods. Let’s do it again.:
types | # objects | total size ======= | =========== | ============
Yes, this looks definitely better. The memory leak is gone.
The problem is fixed for Python versions 2.5 and higher so updated installations will not face this leak.