A harness and a set of benchmarks for measuring Django's performance over time.
Here's the short version:
mkvirtualenv djangobench pip install -e git://github.com/django/djangobench.git#egg=djangobench git clone git://github.com/django/django.git cd django djangobench --control=1.2 --experiment=master
Okay, so what the heck's going on here?
First, djangobench
doesn't test a single Django version in isolation --
that wouldn't be very useful. Instead, it benchmarks an "experiment" Django
against a "control", reporting on the difference between the two and
measuring for statistical significance.
Because a Git clone can contain all the project development history, you can
test against a single repository specifying individual commit IDs, tag (as we've
done above) and even possibly branches names with the --control
and
--experiment
options.
Before djangobench
0.10 you had to use --vcs=git
to get this behavior.
Now it's the default. There is also support for Mercurial (--vcs=hg
).
Another way to use djangobench
, is to run it against two complete Django
source trees, you can specify this mode by using --vcs=none
. By default it
looks for directories named django-control
and django-experiment
in the
current working directory:
djangobench --vcs=none
but you can change that by using the --control
or --experiment
options:
djangobench --vcs=none --control pristine --experiment work
Now, it's impractical to install the Django source code trees under test (this
is particularly true in the two-trees scenario): djangobench
works its magic
by mucking with PYTHONPATH
.
However, the benchmarks themselves need access to the djangobench
module, so
you'll need to install it.
You can specify the benchmarks to run by passing their names on the command line.
This is an example of not-statistically-significant results:
Running 'startup' benchmark ... Min: 0.138701 -> 0.138900: 1.0014x slower Avg: 0.139009 -> 0.139378: 1.0027x slower Not significant Stddev: 0.00044 -> 0.00046: 1.0382x larger
Not only is djangobench
Python 3 compatible, but can also be used to
compare Python 2 vs Python 3 code paths. To do this, you need to provide the
full paths to the corresponding Python executables in --control-python
and
--experiment-python
. The short version (assuming you have also the
djangobench
environment setup like above):
mkvirtualenv djangobench-py3 -p python3 pip install -e git://github.com/django/djangobench.git#egg=djangobench cd django djangobench --vcs=none --control=. --experiment=. \ --control-python=~/.virtualenvs/djangobench/bin/python \ --experiment-python=~/.virtualenvs/djangobench-py3/bin/python \
Benchmarks are very simple: they're a Django app, along with a settings
file, and an executable benchmarks.py
that gets run by the harness. The
benchmark script needs to honor a simple contract:
It's an executable Python script, run as
__main__
(e.g.python path/to/benchmark.py
). The subshell environment will havePYTHONPATH
set up to point to the correct Django; it'll also haveDJANGO_SETTINGS_MODULE
set to<benchmark_dir>.settings
.The benchmark script needs to accept a
--trials
argument giving the number of trials to run.The output should be simple RFC 822-ish text -- a set of headers, followed by data points:
Title: some benchmark Description: whatever the benchmark does 1.002 1.003 ...
The list of headers is TBD.
There's a couple of utility functions in djangobench.utils
that assist
with honoring this contract; see those functions' docstrings for details.
The existing benchmarks should be pretty easy to read for inspiration. The
query_delete
benchmark is probably a good place to start.
Please write new benchmarks and send us pull requests on Github!