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 written entirely in Python, with no dependencies to external libraries. It integrates `Bottle<http://bottlepy.org>`_ and `Highcharts<http://www.highcharts.com>`_. Pympler has been tested with Python 2.4, 2.5, 2.6, 2.7, 3.1 and 3.2. However, the Pympler web frontend does not work with Python 2.4.
Pympler is platform independent and has been tested on various Linux distributions (32bit and 64bit), Windows XP, Windows 7 and MacOS X.
If you have SetupTools installed, the easiest way to get Pympler is to use easy_install:
Every Python developer interested in analyzing the memory consumption of his or her Python program should find a suitable, readily usable facility in Pympler.
Aaron is curious how much memory certain Python objects consume. He uses one of the asizeof functions to get the size of these objects and all associated referents:
>>> from pympler.asizeof import asizeof >>> obj = [1, 2, (3, 4), 'text'] >>> asizeof(obj) 176
Peter is trying to compare different implementations of a new parser module. For each implementation, he uses the asizeof module to print simple statistics like size and number of objects summarized by type.
Graham has been notified that his Python script leaks memory. Looking at the garbage collector debug output does not reveal where the leaks come from. Thus he decides to use the muppy module to see which actions result in an increased memory usage. Graham discovers that whenever his script iterates over the input set, a new dict object is created. With the help of the muppy module he can identify where these new dicts are referenced and eliminates the leak.
Helen maintains a complex application that is taking up a large amount of memory. She would like to reduce the memory footprint of her program by optimizing or restructuring her code. She has a number of optimization candidates and she would like to know if optimizing one of them would likely reduce the total memory footprint. Helen uses the Class Tracker to track and profile her candidate classes. The results tell her which class instances take up the largest shares of memory and are therefore best suited for optimization attempts. After trying to optimize her code she runs the program again and compares the profiling results to quantify the improvements.
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.
- 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 - Changes in Pympler.
- Copyright - Last but not least ..
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