source: main/waeup.kofa/branches/uli-rm-bootstrap/docs/source/developerdocs/profiling.rst @ 17947

Profiling
*********

*or: What Did My Application Do in the Afternoon?*

This how-to explains how you can profile your Grok application or
Grok/Zope itself. This way you get an idea of the bottlenecks of your
application or in other words: where it spends its time.

.. note:: This text is an updated version of my how-to as it can seen
          on the old Grok site (no, I won't give a link to that
          slightly outdated stuff).

Profiling with Grok
===================

When it comes to a web framework like Grok, profiling is not as easy
as with commandline or desktop applications. You normally want to
trigger certain requests and see, in which parts of your code how much
time or memory was spent.

Here is how you can do this.

Prerequisites
=============

We assume you know how to create a Grok project (your application) and
have one already at hand.

Installing a profiler
=====================

There are some profiling tools available with every Python
installation. Usually, they are started together with some desktop- or
commandline application, wrapping it in a way to measure internal calls
and other things interesting for developers.

With web-frameworks, however, we often only want to check certain
requests. Luckily, with `Paste`_ and `WSGI`_ we have a mechanism, that
can serve in a similar way: we can define a (WSGI_) pipeline of apps
and filters that wrap each other, similar to the approach of regular
non-web applications. We also have a profiler already available, that
*is* some WSGI_ application itself and can profile things when some
HTTP request comes in: `repoze.profile`_ is even configurable over a
web frontend.

Install repoze.profile_
=======================

In the ``buildout.cfg`` of your project add the repoze.profile_ egg to list
of eggs of your application. Look out for a section named ``[app]``, which
could read like this or similar::


  ...
  [app]
  recipe = zc.recipe.egg
  eggs = cptapp
         z3c.evalexception>=2.0
         Paste
         PasteScript
         PasteDeploy
         repoze.profile
  interpreter = python-console
  ...

Here the added ``repoze.profile`` stanza is important.

Now run::

  $ bin/buildout

to fetch the egg from the net if it is not already available and to
make it known to the generated scripts.

Create a ``profiler.ini``
=========================

To make use of the new egg we must tell paster about it. This is done
by an appropriate initialization file we create now::

  # file: profiler.ini
  [app:myapp]
  use = egg:sample

  [filter:myprofiler]
  use = egg:repoze.profile#profile

  [pipeline:main]
  pipeline = myprofiler myapp

  [server:main]
  use = egg:Paste#http
  host = 127.0.0.1
  port = 8080

  [DEFAULT]
  # set the name of the zope.conf file
  zope_conf = %(here)s/zope.conf

Here we tell paster (the HTTP server) in the ``pipeline`` to filter
each web request passing it first to repoze.profile_ which then calls
our own ``sample`` application. This way the profiler can start its
job and measure all calls done by the following application call.

It is crucial, that you use the name of *your* project egg here in the
``[app:myapp]`` section. In the sample config above we assume that the
project (application) is called ``sample``, so that the egg with our
application is called the same.

.. note:: modern Grok projects put their paster configuration files as
          *templates* into the local ``etc/`` directory and do not
          call them ``debug.ini``, ``profile.ini``, etc. but
          ``debug.ini.in``, ``profile.ini.in``, etc (note the trailing
          ``.in``). This is not covered here.

Put this new file in the same directory as where your ``zope.conf``
lives (*not* ``zope.conf.in``). For newer projects this is
``parts/etc/``.

Start Profiling
===============

With the given setup we can start profiling by::

  $ bin/paster serve parts/etc/profiler.ini

If your ``profiler.ini`` file resides elsewhere, you must use a
different location, of course.

.. note:: you dont't *have* to create a separate profiler config. You
          can, of course, update an existing ``deploy.ini`` or
          ``debug.ini``. But as profiling takes more time than calls
          without profiling, it is a good idea to enable profiling
          only on request, i.e. when starting paster with a
          respective configuration.

The server will start as usual and you can do everything you like with
it.

Browsing the Profiler
=====================

To get to the profiler, enter the following URL:

    http://localhost:8080/__profile__

This brings us to the profiler web frontend. If you have browsed your
instance before, you will get some values about the timings of last
requests. If not, then browse a bit over your application to collect
some data. The data is collected 'in background' during each request
and added to the values already collected.

The result might look like this:

.. image:: screenshot01.png

As you can see, there are a few options you can play with. You can
filter the results by name, sort it by time or other stats, enable
display of full paths, etc.

When you stop paster, all collected data is removed. On the next start
you can generate new one.

Profiling a certain view
========================

Say we want to profile the performance of the ``index`` view created
by the our application. To do this, we first have to install an
instance of our application. Let's call it simply ``app``.

So, create an instance of your application under the name ``app`` in
the grok UI admin interface.

Now we can access

    http://localhost:8080/app

and the usual index page will (hopefully) appear.

If we go back to the profiler, however, we will see the summed up
values of *all* requests we did up to now - including all the actions
in the admin interface etc. we are not interested in.

We therefore clear the current data by clicking on ``clear`` on the
profiler page.

Now we access the page we want to examine directly and go to the above
URL directly.

When we now go back to the profiler, we only see the values of the
last request. That's the data we are interested in.

Profiling mass requests
=======================

Very often a single request to a view does not give us reliable data:
too many factors can influence the request to make its values not very
representative. What we often want are *many* requests and the average
values appearing here.

This means for our view: we want to do several hundreds requests to
the same view. But as we are lazy, we don't want to press the reload
button several hundred or even thousand times. Luckily there are tools
available, which can do that for us.

One of this tools is the apache benchmarking tool ``ab`` from the
Apache project. On Ubuntu systems it is automatically installed, if
you have the apache webserver installed.

With ``ab`` (apache benchmarking) we can trigger 1,000 requests to our
index page with one command::

  $ ab -n1000 -c4 http://127.0.0.1/app/@@index

This will give us 1,000 requests, of which at most four are triggered
concurrently, to the URL http://127.0.0.1/app/@@index. Please don't do
this on foreign machines!

The result might look like this::

  Benchmarking 127.0.0.1 (be patient)
  Completed 100 requests
  Completed 200 requests
  Completed 300 requests
  Completed 400 requests
  Completed 500 requests
  Completed 600 requests
  Completed 700 requests
  Completed 800 requests
  Completed 900 requests
  Finished 1000 requests
  
  
  Server Software:        PasteWSGIServer/0.5
  Server Hostname:        127.0.0.1
  Server Port:            8080
  
  Document Path:          /app/@@index
  Document Length:        198 bytes
  
  Concurrency Level:      4
  Time taken for tests:   38.297797 seconds
  Complete requests:      1000
  Failed requests:        0
  Write errors:           0
  Total transferred:      448000 bytes
  HTML transferred:       198000 bytes
  Requests per second:    26.11 [#/sec] (mean)
  Time per request:       153.191 [ms] (mean)
  Time per request:       38.298 [ms] (mean, across all concurrent requests)
  Transfer rate:          11.41 [Kbytes/sec] received

  Connection Times (ms)
                min  mean[+/-sd] median   max
  Connect:        0    0   0.0      0       0
  Processing:    94  152  17.3    151     232
  Waiting:       86  151  17.3    150     231
  Total:         94  152  17.3    151     232

  Percentage of the requests served within a certain time (ms)
    50%    151
    66%    153
    75%    156
    80%    158
    90%    176
    95%    189
    98%    203
    99%    215
   100%    232 (longest request)

Also this benchmarking results can be interesting. But we want to know
more about the functions called during this mass request and how much
time they spent each. This can be seen, if we now go back to the
browser and open

    http://localhost:8080/__profile__

again.

.. note:: ``ab``, of course, is a simple, rude method for stress
          testing. Although it provides many options, you might want
          to look for more sophisticated methods to generate tons of
          requests. ``multi-mechanize`` is such a tool that can kill
          your server with the same power as ``ab`` but does it
          smarter.

Turning the Data into a Graph
=============================

All this is very nice, but sometimes a picture tells more than
thousand words. So let's turn all this data into some graph.

As repoze.profile_ cannot do this for us, we have to 'export' the
collected data first.

Exporting Profiling Data
========================

The web frontend provided by repoze.profile_ is very nice for
analyzing ad-hoc. But sometimes we want to have the data 'exported' to
process it further with other tools or simply archiving the results.

Luckily we can do so by grabbing the file ``wsgi.prof`` which contains
all the data presented in the web interface. This file is created by
repoze.profile_ while working and in the top of the project
directory. In fact the profiler stores all collected data in that
``wsgi.prof`` file also for own usage.

Be careful: when you click ``clear`` in the webinterface, then the
file will vanish. Also stopping ``paster`` will make it disappear. So
copy it to some secure location where we can process the data further
while the web server is still running (and after you did all requests
you want to analyze).

Because repoze.profile_ makes use of the standard Python profiler in
the :mod:`profile` or :mod:`cProfile` module, the data in the
``wsgi.prof`` file conforms to output generated by these profilers.

Converting the Data into dot-format
===================================

One of the more advanced tools to create graphs from profiling
information is ``dot`` from graphviz_. To make use of it, we first
have to convert the data in ``wsgi.prof`` file into something
``dot``-compatible.

There is a tool available, which can do the job for us, a Python
script named ``GProf2Dot`` which is available at:

    http://code.google.com/p/jrfonseca/wiki/Gprof2Dot

Download the script from:

    http://jrfonseca.googlecode.com/svn/trunk/gprof2dot/gprof2dot.py

We can now turn our profiling data into a ``dot`` graph by doing::

  $ python grprof2dot.py -f pstats -o wsgi.prof.dot wsgi.prof

This will turn our input file ``wsgi.prof`` of format `pstats`
(=Python stats) into a ``dot``-file named ``wsgi.prof.dot``.

Converting the dot file into Graphics
=====================================

Now we can do the last step and turn our dot file into a nice graphics
file. For this we need of course the dot programme, which on Ubuntu
systems can be easily installed doing::

  $ sudo apt-get install dot

Afterwards we do the final transformation by::

  $ dot -Tpng -omygraph.png wsgi.prof.dot

This will generate a PNG file where we can see in terms of graphs
where a request (or thousands thereof) spends most of the
time. ``dot`` can generate **huge** graphs. A tiny snippet of a sample
is shown below.

.. image:: screenshot02.png

All the used tools (``ab``, ``dot``, ``gprof2dot``) provide a huge
bunch of options you might want to explore further. This way we can
generate more or less complete graphs (leaving out functions of little
impact), coulours etc.

In the end you hopefully know more about your application and where it
spent its time.

.. _`repoze.profile`: http://pypi.python.org/pypi/repoze.profile
.. _`Paste`: http://pythonpaste.org/
.. _`WSGI`: http://www.python.org/peps/pep-0333.html
.. _`graphviz`: http://www.graphviz.org/
.. _`GProf2Dot`: http://code.google.com/p/jrfonseca/wiki/Gprof2Dot