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.
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:
gprof2dot -f pstats -o /temp/prof.xdot /temp/prof.profile
A tool called xdot will display the resulting graph:
The profiler can be also used without saving data in a file.
@profile @api.model def mymethod(...):
The statistics will be displayed into the logs once the method to be analysed is completely reviewed.
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
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
# 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.
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:
flamegraph.pl ./test.flame > ~/mycode.svg