===================
Profiling Odoo code
===================

.. warning::

    This tutorial requires :ref:`having installed Odoo <setup/install>`
    and :doc:`writing Odoo code <backend>`

Graph a method
==============

Odoo embeds a profiler of code. This embeded profiler output can be used to
generate a graph of calls triggered by the method, number of queries, percentage
of time taken in the method itself as well as time taken in method and it's
sub-called methods.

.. code:: python

    from odoo.tools.profiler import profile
    [...]
    @profile('/temp/prof.profile')
    @api.multi
    def mymethod(...)

This produce a file called /temp/prof.profile

A tool called *gprof2dot* will produce a graph with this result:

.. code:: bash

    gprof2dot -f pstats -o /temp/prof.xdot /temp/prof.profile

A tool called *xdot* will display the resulting graph:

.. code:: bash
    
    xdot /temp/prof.xdot
    
The profiler can be also used without saving data in a file.

.. code:: python

    @profile
    @api.model
    def mymethod(...):

The statistics will be displayed into the logs once the method to be analysed is
completely reviewed.

.. code:: bash

    2018-03-28 06:18:23,196 22878 INFO openerp odoo.tools.profiler:
    calls     queries   ms
    project.task ------------------------ /home/odoo/src/odoo/addons/project/models/project.py, 638

    1         0         0.02          @profile
                                      @api.model
                                      def create(self, vals):
                                          # context: no_log, because subtype already handle this
    1         0         0.01              context = dict(self.env.context, mail_create_nolog=True)

                                          # for default stage
    1         0         0.01              if vals.get('project_id') and not context.get('default_project_id'):
                                              context['default_project_id'] = vals.get('project_id')
                                          # user_id change: update date_assign
    1         0         0.01              if vals.get('user_id'):
                                              vals['date_assign'] = fields.Datetime.now()
                                          # Stage change: Update date_end if folded stage
    1         0         0.0               if vals.get('stage_id'):
                                              vals.update(self.update_date_end(vals['stage_id']))
    1         108       631.8             task = super(Task, self.with_context(context)).create(vals)
    1         0         0.01              return task

    Total:
    1         108       631.85

Dump stack
==========

Sending the SIGQUIT signal to an odoo process (only available on POSIX) makes
this process output the current stack trace to log, with info level. When an
odoo process seems stucked, sending this signal to the process permit to know
what the process is doing, and letting the process continue his job.

Tracing code execution
======================

Instead of sending the SIGQUIT signal to an odoo process often enough, to check
where processes is performing worse than expected, we can use pyflame tool to
do it for us.

Install pyflame and flamegraph
------------------------------

.. code:: bash

    # These instructions are given for Debian/Ubuntu distributions
    sudo apt install autoconf automake autotools-dev g++ pkg-config python-dev python3-dev libtool make
    git clone https://github.com/uber/pyflame.git
    git clone https://github.com/brendangregg/FlameGraph.git
    cd pyflame
    ./autogen.sh
    ./configure
    make
    sudo make install

Record executed code
--------------------

As pyflame is installed, we now record the executed code lines with pyflame.
This tool will record, multiple times a second, the stacktrace of the process.
Once done, we'll display them as an execution graph.

.. code:: bash

    pyflame --exclude-idle -s 3600 -r 0.2 -p <PID> -o test.flame

where <PID> is the process ID of the odoo process you want to graph. This will
wait until the dead of the process, with a maximum of one hour, and and get 5
traces a second. With the output of pyflame, we can produce an svg graph with
the flamegraph tool:

.. code:: bash

    flamegraph.pl ./test.flame > ~/mycode.svg

.. image:: profile/flamegraph.svg