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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.)

References