util for profiling python code mainly in django projects, but can be used also on ordinary python code
Project description
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. @profilehook decorator uses profilehooks python package to gather code execution stats. Except it logs via standard python logging it also outputs code execution stats directly to sys.stdout.
Requirements
python 2.7+
For more information see debian_requirements.txt and requirements.txt files.
Important notice
Due to packaging problem of python-profiler linux package on newest linux distributions, profilehooks in not an integral part of django-profiler package. profilehooks and python-profiler are not required to be installed. Without installing them you won’t be able to use @profilehook decorator, but you will be able to use django-profiler and it’s decorator nevertheless. To use profilehooks package you must install profilehooks python package and python-profiler linux package manually.
Installation
Install via pip or copy this module into your project or into your PYTHON_PATH.
Configuration
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. profilehooks stats are outputted directly into sys.stdout.
from profiling import profilehook
@profilehook
def complex_computations():
#some complex computations
Example 8
Using decorator approach. Output will be logged to profiling.ComplexClass.complex_computations logger. profilehooks stats are outputted directly into sys.stdout.
from profiling import profilehook
class ComplexClass(object)
@profilehook
def complex_computations():
#some complex computations
Tests
Tested on evnironment
Xubuntu Linux 11.04 natty 64-bit
python 2.7.1+
python unittest
Running tests
To run the test run command:
$ python test.py $ python setup.py test
References
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
