Pympler is a development tool to measure, monitor and analyze the memory behavior of Python objects in a running Python application.
By pympling a Python application, detailed insight in the size and the lifetime of Python objects can be obtained. Undesirable or unexpected runtime behavior like memory bloat and other “pymples” can easily be identified.
Pympler integrates three previously separate modules into a single, comprehensive profiling tool. The asizeof module provides basic size information for one or several Python objects, module muppy is used for on-line monitoring of a Python application and module Class Tracker provides off-line analysis of the lifetime of selected Python objects.
A web profiling frontend exposes process statistics, garbage visualisation and class tracker statistics.
Pympler is platform independent and has been tested on various Linux distributions (32bit and 64bit), Windows 7 and MacOS X.
If you have pip installed, the easiest way to get Pympler is:
pip install pympler
Every Python developer interested in analyzing the memory consumption of their Python program should find a suitable, readily usable facility in Pympler.
pympler.asizeof can be used to investigate how much memory certain Python
objects consume. In contrast to
asizeof sizes objects
recursively. You can use one of the asizeof functions to get
the size of these objects and all associated referents:
>>> from pympler import asizeof >>> obj = [1, 2, (3, 4), 'text'] >>> asizeof.asizeof(obj) 176 >>> print(asizeof.asized(obj, detail=1).format()) [1, 2, (3, 4), 'text'] size=176 flat=48 (3, 4) size=64 flat=32 'text' size=32 flat=32 1 size=16 flat=16 2 size=16 flat=16
Memory leaks can be detected by using muppy. While the garbage collector debug output can report circular references this does not easily reveal where the leaks come from. Muppy can identify if objects are leaked out of a scope between two reference points:
>>> from pympler import tracker >>> tr = tracker.SummaryTracker() >>> function_without_side_effects() >>> tr.print_diff() types | # objects | total size ======= | =========== | ============ dict | 1 | 280 B list | 1 | 192 B
Tracking the lifetime of objects of certain classes can be achieved with the Class Tracker. This gives insight into instantiation patterns and helps to understand how specific objects contribute to the memory footprint over time:
>>> from pympler import classtracker >>> tr = classtracker.ClassTracker() >>> tr.track_class(Document) >>> tr.create_snapshot() >>> create_documents() >>> tr.create_snapshot() >>> tr.stats.print_summary() active 1.42 MB average pct Document 1000 195.38 KB 200 B 13%
Pympler was founded in August 2008 by Jean Brouwers, Ludwig Haehne, and Robert Schuppenies with the goal of providing a complete and stand-alone memory profiling solution for Python.
Table of Content¶
- Sizing individual objects - A description of the asizeof module.
- Tracking class instances - A description of the ClassTracker facility.
- Identifying memory leaks - A description of the muppy modules.
- Tracking memory in Django - How to use the Django debug toolbar memory panel.
- Library - The library reference guide.
- Pympler Tutorials - Pympler tutorials and usage examples.
- Related Work - Other projects which deal which memory profiling in Python are mentioned in the this section.
- Glossary - A few basic terms used throughout the documentation.
- Changes in Pympler
- Copyright - Last but not least ..
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