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.
- python 2.7+
Install via pip or copy this module into your project or into your PYTHON_PATH.
django settings.py constants
PROFILING_LOGGER_NAME
PROFILING_SQL_QUERIESIt 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.
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 computationsExample 2
Using context manager approach. Output will be logged to profiling.Computation logger.
from profiling import Profiler
with Profiler('Computation'):
# code with some complex computationsExample 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 computationsExample 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 computationsExample 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 computationsExample 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 computationsExample 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 computationsExample 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 computationsTested 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