Djangobench

A harness and a set of benchmarks for measuring Django's performance over time.

Running the benchmarks

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

Python 3

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 \

Writing new benchmarks
----------------------

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 have
  ``PYTHONPATH`` set up to point to the correct Django; it'll also have
  ``DJANGO_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!**