Harness and benchmarks for evaluating Django's performance over time
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Latest commit 487460d Nov 23, 2016 @adamchainz adamchainz committed with timgraham Fix for Django 1.10 loaddata
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djangobench Fix for Django 1.10 loaddata Nov 23, 2016
.gitignore Finished refactoring everything into a module. Jun 22, 2010
MANIFEST.in setup.py: include benchmarks directory in install Jan 24, 2011
README.rst Compare performance between Python versions Apr 13, 2014
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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

    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!