Tracking class instances


The ClassTracker is a facility delivering insight into the memory distribution of a Python program. It can introspect memory consumption of certain classes and objects. Facilities are provided to track and size individual objects or all instances of certain classes. Tracked objects are sized recursively to provide an overview of memory distribution between the different tracked objects.


Let’s start with a simple example. Suppose you have this module:

>>> class Employee:
...    pass
>>> class Factory:
...    pass
>>> def create_factory():
...    factory = Factory()
... = "Assembly Line Unlimited"
...    factory.employees = []
...    return factory
>>> def populate_factory(factory):
...    for x in xrange(1000):
...        worker = Employee()
...        worker.assigned =
...        factory.employees.append(worker)
>>> factory = create_factory()
>>> populate_factory(factory)

The basic tools of the ClassTracker are tracking objects or classes, taking snapshots, and printing or dumping statistics. The first step is to decide what to track. Then spots of interest for snapshot creation have to be identified. Finally, the gathered data can be printed or saved:

>>> factory = create_factory()
>>> from pympler.classtracker import ClassTracker
>>> tracker = ClassTracker()
>>> tracker.track_object(factory)
>>> tracker.track_class(Employee)
>>> tracker.create_snapshot()
>>> populate_factory(factory)
>>> tracker.create_snapshot()
>>> tracker.stats.print_summary()
---- SUMMARY ------------------------------------------------------------------
                                         active      1.22 MB      average   pct
  Factory                                     1    344     B    344     B    0%
  __main__.Employee                           0      0     B      0     B    0%
                                         active      1.42 MB      average   pct
  Factory                                     1      4.75 KB      4.75 KB    0%
  __main__.Employee                        1000    195.38 KB    200     B   13%

Basic Functionality

Instance Tracking

The purpose of instance tracking is to observe the size and lifetime of an object of interest. Creation and destruction timestamps are recorded and the size of the object is sampled when taking a snapshot.

To track the size of an individual object:

from pympler.classtracker import ClassTracker
tracker = ClassTracker()
obj = MyClass()

Class Tracking

Most of the time it’s cumbersome to track individual instances manually. Instead, all instances of a class can automatically be tracked with track_class:


All instances of MyClass (or a class that inherits from MyClass) created hereafter are tracked.

Tracked Object Snapshot

Tracking alone will not reveal the size of an object. The idea of the ClassTracker is to sample the sizes of all tracked objects at configurable instants in time. The create_snapshot function computes the size of all tracked objects:

tracker.create_snapshot('Before juggling with tracked objects')
tracker.create_snapshot('Juggling aftermath')

With this information, the distribution of the allocated memory can be apportioned to tracked classes and instances.

Advanced Functionality

Per-referent Sizing

It may not be enough to know the total memory consumption of an object. Detailed per-referent statistics can be gathered recursively up to a given resolution level. Resolution level 1 means that all direct referents of an object will be sized. Level 2 also include the referents of the direct referents, and so forth. Note that the member variables of an instance are typically stored in a dictionary and are therefore second order referents.

tracker.track_object(obj, resolution_level=2)

The resolution level can be changed if the object is already tracked:

tracker.track_change(obj, resolution_level=2)

The new setting will become effective for the next snapshot. This can help to raise the level of detail for a specific instance of a tracked class without logging all the class’ instances with a high verbosity level. Nevertheless, the resolution level can also be set for all instances of a class:

tracker.track_class(MyClass, resolution_level=1)


Please note the per-referent sizing is very memory and computationally intensive. The recorded meta-data must be stored for each referent of a tracked object which might easily quadruplicate the memory footprint of the build. Handle with care and don’t use too high resolution levels, especially if set via track_class.

Instantiation traces

Sometimes it is not trivial to observe where an object was instantiated. The ClassTracker can record the instantiation stack trace for later evaluation.

tracker.track_class(MyClass, trace=1)

This only works with tracked classes, and not with individual objects.

Background Monitoring

The ClassTracker can be configured to take periodic snapshots automatically. The following example will take 10 snapshots a second (approximately) until the program has exited or the periodic snapshots are stopped with stop_periodic_snapshots. Background monitoring also works if no object is tracked. In this mode, the ClassTracker will only record the total virtual memory associated with the program. This can be useful in combination with background monitoring to detect memory usage which is transient or not associated with any tracked object.



Take care if you use automatic snapshots with tracked objects. The sizing of individual objects might be inconsistent when memory is allocated or freed while the snapshot is being taken.

Off-line Analysis

The more data is gathered by the ClassTracker the more noise is produced on the console. The acquired ClassTracker log data can also be saved to a file for off-line analysis:


The Stats class of the ClassTracker provides means to evaluate the collected data. The API is inspired by the Stats class of the Python profiler. It is possible to sort the data based on user preferences, filter by class and limit the output noise to a manageable magnitude.

The following example reads the dumped data and prints the ten largest Node objects to the standard output:

from pympler.classtracker_stats import ConsoleStats

stats = ConsoleStats()
stats.sort_stats('size').print_stats(limit=10, clsname='Node')

HTML Statistics

The ClassTracker data can also be emitted in HTML format together with a number of charts (needs python-matplotlib). HTML statistics can be emitted using the HtmlStats class:

from pympler.classtracker_stats import HtmlStats

However, you can also reprocess a previously generated dump:

from pympler.classtracker_stats import HtmlStats

stats = HtmlStats(filename='profile.dat')

Limitations and Corner Cases


Class tracking allows to observe multiple classes that might have an inheritance relationship. An object is only tracked once. The tracking parameters of the most specialized tracked class control the actual tracking of an instance.

Shared Data

Data shared between multiple tracked objects won’t lead to overestimations. Shared data will be assigned to the first (evaluated) tracked object it is referenced from, but is only counted once. Tracked objects are evaluated in the order they were announced to the ClassTracker. This should make the assignment deterministic from one run to the next, but has two known problems. If the ClassTracker is used concurrently from multiple threads, the announcement order will likely change and may lead to random assignment of shared data to different objects. Shared data might also be assigned to different objects during its lifetime, see the following example:

class A():

from pympler.classtracker import ClassTracker
tracker = ClassTracker()

a = A()
b = A()
b.content = range(100000)
a.notmine = b.content

In the snapshot #1, b‘s size will include the size of the large list. Then the list is shared with a. The snapshot #2 will assign the list’s footprint to a because it was registered before b.

If a tracked object A is referenced from another tracked object B, A‘s size is not added to B‘s size, regardless of the order in which they are sized.


ClassTracker uses the sizer module to gather size informations. Asizeof makes assumptions about the memory footprint of the various data types. As it is implemented in pure Python, there is no way to know how the actual Python implementation allocates data and lays it out in memory. Thus, the size numbers are not really accurate and there will always be a divergence between the virtual size of the Python process as reported by the OS and the sizes asizeof estimates.

Most recent C/Python versions contain a facility to report accurate size informations of Python objects. If available, asizeof uses it to improve the accuracy.

Morphing objects

Some programs instate the (anti-)pattern of changing an instance’ class at runtime, for example to morph abstract objects into specific derivations during runtime. The pattern looks like the following in the code:

obj.__class__ = OtherClass

If the instance which is morphed is already tracked, the instance will continue to be tracked by the ClassTracker. If the target class is tracked but the instance is not, the instance will only be tracked if the constructor of the target class is called as part of the morphing process. The object will not be re-registered to the new class in the tracked object index. However, the new class is stored in the representation of the object as soon as the object is sized.