On Tuesday, October 18, 2016 at 9:29:37 AM UTC-4, Mike Bayer wrote:
This approach can also be made extremely performant by making sure you
> load up that profile datafile just once, and cache it in memory in a
> dictionary.
>
What's also nice about the decorator approach in the aspect, is that you
can use it as a factory and disable/enable it on startup to eliminate
runtime performance issues when not needed:
def monitor_sqlcalls(wrapped):
if DO_NOT_DECORATE:
return wrapped
def wrapper():
result = wrapped()
return result
return wrapper
@monitor_sqlcalls
def foo():
return True
If you toggle it off, you'll only have a performance hit on the application
startup, while Python compiles the functions and returns the underlying
function, essentially leaving it undecorated. This can let you decorate
profiling code on everything you want, then only run it on a few select
deployments / processes.
If you really want to shave off some milliseconds and are using cPython,
you can set the env variable `PYTHONOPTIMIZE` to change the `__debug__`
constant from True to False (or invoke Python with `-o/-oo`), and nest any
random debugging work below it. Anything in an `if __debug__` block will
only be compiled if PYTHONOPTIMIZE is unset. The compiler only handles a
very simple `if __debug__:` construct. 'if not __debug__` needs to be
written as `if __debug__: pass` + `else:` If you want to run something as
and/or `__debug__`, you'll need to split that into two if statements.
Another trick is to log stuff using `statsd` from Etsy. It's an external
daemon written in node.js, and clients send data to it via UDP. We have a
handful of functions wrapped in statsd decorators that track timing; some
track average results or number of postgres interactions. The uwsgi
containers also report into statsd, allowing us to easily correlate
everything to server loads across the network at the time. The upside is
that there is almost no overhead, the downside is that this performance
stems from UDP transmissions not being guaranteed of receipt.
--
SQLAlchemy -
The Python SQL Toolkit and Object Relational Mapper
http://www.sqlalchemy.org/
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