django-profiler

django-profiler is util for profiling python code mainly in django projects but can be used also on ordinary python code. It counts sql queries a measures time of code execution. It logs its output via standard python logging library and uses logger profiling. If your profiler name doesn't contain any empty spaces e.g. Profiler('Profiler1') django-profiler will log all the output to the profiling.Profiler logger.

Requirements

• python 2.7+

Installation

Install via pip or copy this module into your project or into your PYTHON_PATH.

Configuration

django settings.py constants

::

PROFILING_LOGGER_NAME PROFILING_SQL_QUERIES

It is possible to change default django-profiler logger name by defining PROFILING_LOGGER_NAME = 'logger_name' in your django settings.py.

To log also sql queries into profiler logger set PROFILING_SQL_QUERIES to True in your django settings.py module.

Examples

Example 1

Using context manager approach. Output will be logged to profiling logger.

::

from profiling import Profiler with Profiler('Complex Computation'): # code with some complex computations

Example 2

Using context manager approach. Output will be logged to profiling.Computation logger.

::

from profiling import Profiler with Profiler('Computation'): # code with some complex computations

Example 3

Using standard approach. Output will be logged to profiling logger.

::

from profiling import Profiler profiler = Profiler('Complex Computation') profiler.start()

code with some complex computations

profiler.stop()

Example 4

Using standard approach and starting directly in constructor. Output will be logged to profiling logger.

::

from profiling import Profiler profiler = Profiler('Complex Computation', start=True)

code with some complex computations

profiler.stop()

Example 5

Using decorator approach. Output will be logged to profiling.complex_computations logger.

::

from profiling import profile

@profile def complex_computations(): #some complex computations

Example 6

Using decorator approach. Output will be logged to profiling.ComplexClass.complex_computations logger.

::

from profiling import profile

class ComplexClass(object): @profile def complex_computations(): #some complex computations

Example 7

Using decorator approach. Output will be logged to profiling.complex_computations logger. profile execution stats are logged to profiling.complex_computations logger.

::

from profiling import profile

@profile(stats=True) def complex_computations(): #some complex computations

Example 8

Using decorator approach. Output will be logged to profiling.complex_computations logger. profile execution stats are printed to sys.stdout.

::

import sys

from profiling import profile

@profile(stats=True, stats_buffer=sys.stdout) def complex_computations(): #some complex computations

Example 9

Using decorator approach. Output will be logged to profiling.ComplexClass.complex_computations logger. profile stats will be logged to profiling.ComplexClass.complex_computations.

::

from profiling import profile

class ComplexClass(object) @profile(stats=True) def complex_computations(): #some complex computations

Example 10

Using decorator approach. Output will be stored in /tmp/stats and can be analyzed with pstats module. profile stats will be logged to profiling.ComplexClass.complex_computations.

::

from profiling import profile

class ComplexClass(object) @profile(stats=True, stats_filename='/tmp/stats') def complex_computations(): #some complex computations

Tests

Tested on evnironment

• Xubuntu Linux 11.10 oneiric 64-bit
• python 2.7.2+
• python unittest

Running tests

To run the test run command: ::

$ python test.py $ python setup.py test

Author

| char0n (Vladimír Gorej, CodeScale s.r.o.) | email: [email protected] | web: http://www.codescale.net

References

• http://github.com/CodeScaleInc/django-profiler
• http://pypi.python.org/pypi/django-profiler/
• http://www.codescale.net/en/community#django-profiler