django-profiler 1.0b2

util for profiling python code mainly in django projects, but can be used also on ordinary python code

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.
`@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.


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


Author
------

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


References
----------

- http://github.com/char0n/django-profiler
- http://www.codescale.net/en/community#django-profiler