Pympler A development tool to measure, monitor and analyze the memory behavior of Python objects.
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Python memory objects Python objects measure memory behavior analyze memory behavior

Pympler Description

A development tool to measure, monitor and analyze the memory behavior of Python objects. 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 3 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 heapmonitor provides off-line analysis of the lifetime of selected Python objects.Pympler is written entirely in Python, with no dependencies to external libraries or projects. Both the heapmonitor and the muppy module will work with Python 2.4, 2.5, and 2.6. The asizeof module has been tested with Python 2.2.3, 2.3.7, 2.4.5, 2.5.1, 2.5.2, 2.6 or 3.0rc3 on CentOS 4.6, SuSE 9.3, Panther 10.3.9 (PPC) and MacOS X 10.4.11 Tiger (Intel), Solaris 10 and Windows XP all 32-bit Python and on RHEL 3u7 and Solaris 10 both 64-bit Python.Target Audience:Every Python developer interested in analyzing the memory consumption of his or her Python program should find a suitable, readily usable facility in Pympler.Usage Examples: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.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 heapmonitor 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. Requirements: · Python

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