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Introduction
============
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
<tracker> provides off-line analysis of the lifetime of
selected Python objects.
Pympler is written entirely in Python, can be used with Python
version 2.2.3 or later up to the most recent Python 2.6 and 3.0
releases without other dependencies.
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> function 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 <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.
History
=======
Pympler was founded in August 2008 by Jean Brouwers, Ludwig Hähne, and Robert
Schuppenies with the goal of providing a complete and stand-alone
memory profiling solution for Python.
===================================================================
<some more details about each module>
<simple usage examples for each module>
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