Development

Basic Docker usage

Edit files as usual on your host machine; the current directory is mounted via Docker host mounting at /app within various Kuma containers. Useful docker sub-commands:

docker-compose exec web bash     # Start an interactive shell
docker-compose logs web          # View logs from the web container
docker-compose logs -f           # Continuously view logs from all containers
docker-compose restart web       # Force a container to reload
docker-compose stop              # Shutdown the containers
docker-compose up -d             # Start the containers
docker-compose rm                # Destroy the containers

There are make shortcuts on the host for frequent commands, such as:

make up         # docker-compose up -d
make bash       # docker-compose exec web bash
make shell_plus # docker-compose exec web ./manage.py shell_plus

Run all commands in this doc in the web service container after make bash.

Running Kuma

When the Docker container environment is started (make up or similar), all of the services are also started. The development instance is available at http://localhost:8000.

Running the tests

One way to confirm that everything is working, or to pinpoint what is broken, is to run the test suite.

Django tests

Run the Django test suite:

make test

For more information, see the test documentation.

Functional tests

To run the functional tests, see Client-side Testing.

Database migrations

Apps are migrated using Django’s migration system. To run the migrations:

./manage.py migrate

Coding conventions

See CONTRIBUTING.md for details of the coding style on Kuma.

New code is expected to have test coverage. See the Test Suite docs for tips on writing tests.

Managing dependencies

Python dependencies

Kuma uses Poetry for dependency management. Poetry is configured in the pyproject.toml file at the root of the repository, and exact versions of dependencies (along with hashes) are stored in the poetry.lock file.

Please refer to the Poetry docs on adding and updating dependencies.

A few examples:

  • Use poetry update to update and re-lock all dependencies to their latest compatible versions, according to constraints in pyproject.toml.

  • Use poetry update <name> to update only a single dependency to its latest compatible version, according to constraints in pyproject.toml. For example poetry update pytz.

  • To update a package to the very latest and not just what matches what’s currently in pyproject.toml, add @latest. For example poetry update pytz@latest.

  • Use poetry add <name> to modify or add new entries inside of pyproject.toml, for example, poetry add django~2.2 or poetry add flake8@latest.

  • Use poetry lock to regenerate the poetry.lock, for example, after manually editing pyproject.toml.

  • Use poetry show to report on the project’s dependencies.

In brief, update alters the lockfile, but does not modify entries within pyproject.toml. The add command changes both.

You may wish to run these commands inside of Docker:

docker-compose exec web poetry update --dry-run

Using Poetry directly on your host computer is also fine; the resulting pyproject.toml and poetry.lock files should be the same either way.

Customizing with environment variables

Environment variables are used to change the way different components work. There are a few ways to change an environment variables:

  • Exporting in the shell, such as:

    export DEBUG=True;
    ./manage.py runserver
    
  • A one-time override, such as:

    DEBUG=True ./manage.py runserver
    
  • Changing the environment list in docker-compose.yml.

  • Creating a .env file in the repository root directory.

One variable you may wish to alter for local development is DEBUG_TOOLBAR, which, when set to True, will enable the Django Debug Toolbar:

DEBUG_TOOLBAR=True

Note that enabling the Debug Toolbar can severely impact response time, adding around 4 seconds to page load time.

Customizing number of workers

The docker-compose.yml in git comes with a default setting of 4 celery workers and 4 gunicorn workers. That’s pretty resource intensive since they prefork. To change the number of gunicorn and celery workers, consider setting this in your .env file:

CELERY_WORKERS=2
GUNICORN_WORKERS=3

In that example, it will only start 2 celery workers and 3 gunicorn workers just for your environment.

Customizing the Docker environment

Running docker-compose will create and run several containers, and each container’s environment and settings are configured in docker-compose.yml. The settings are “baked” into the containers created by docker-compose up.

To override a container’s settings for development, use a local override file. For example, the web service runs in a container with the default command “gunicorn -w 4 --bind 0.0.0.0:8000 --timeout=120 kuma.wsgi:application”. (The container has a name that begins with kuma_web_1_ and ends with a string of random hex digits. You can look up the name of your particular container with docker ps | grep kuma_web. You’ll need this container name for some of the commands described below.) A useful alternative for debugging is to run a single-threaded process that loads the Werkzeug debugger on exceptions (see docs for runserver_plus), and that allows for stepping through the code with a debugger. To use this alternative, create an override file docker-compose.override.yml:

version: "2.1"
services:
  web:
    command: ./manage.py runserver_plus 0.0.0.0:8000
    stdin_open: true
    tty: true

This is similar to “docker run -it <container> ./manage.py runserver_plus”, using all the other configuration items in docker-compose.yml. Apply the custom setting with:

docker-compose up -d

You can then add pdb breakpoints to the code (import pdb; pdb.set_trace) and connect to the debugger with:

docker attach <container>

A similar method can be used to override environment variables in containers, run additional services, or make other changes. See the docker-compose documentation for more ideas on customizing the Docker environment.

Customizing the database

The database connection is defined by the environment variable DATABASE_URL, with this default:

DATABASE_URL=postgresql://kuma:kuma@postgres:5432/developer_mozilla_org

The format is defined by the dj-database-url project:

DATABASE_URL=mysql://user:password@host:port/database

If you configure a new database, override DATABASE_URL to connect to it. To add an empty schema to a freshly created database:

./manage.py migrate

To connect to the database specified in DATABASE_URL, use:

./manage.py dbshell

Using secure cookies

To prevent error messages like “Forbidden (CSRF cookie not set.):”, set the environment variable:

CSRF_COOKIE_SECURE = false

This is the default in Docker, which does not support local development with HTTPS.

Maintenance mode

Maintenance mode is a special configuration for running Kuma in read-only mode, where all operations that would write to the database are blocked. As the name suggests, it’s intended for those times when we’d like to continue to serve documents from a read-only copy of the database, while performing maintenance on the master database.

For local Docker-based development in maintenance mode:

  1. If you haven’t already, create a read-only user for your local MySQL database:

    docker-compose up -d
    docker-compose exec web mysql -h mysql -u root -p
    (when prompted for the password, enter "kuma")
    mysql> source ./scripts/create_read_only_user.sql
    mysql> quit
    
  2. Create a .env file in the repository root directory, and add these settings:

    MAINTENANCE_MODE=True
    DATABASE_USER=kuma_ro
    

    Using a read-only database user is not required in maintenance mode. You can run in maintenance mode just fine with only this setting:

    MAINTENANCE_MODE=True
    

    and going with a database user that has write privileges. The read-only database user simply provides a level of safety as well as notification (for example, an exception will be raised if an attempt to write the database slips through).

  3. Update your local Docker instance:

    docker-compose up -d
    
  4. You may need to recompile your static assets and then restart:

    docker-compose exec web make build-static
    docker-compose restart web
    

You should be good to go!

There is a set of integration tests for maintenance mode. If you’d like to run them against your local Docker instance, first do the following:

  1. Load the latest sample database (see Visit the homepage).

  2. Ensure that the test document “en-US/docs/User:anonymous:uitest” has been rendered (all of its macros have been executed). You can check this by browsing to http://localhost:8000/en-US/docs/User:anonymous:uitest. If there is no message about un-rendered content, you are good to go. If there is a message about un-rendered content, you will have to put your local Docker instance back into non-maintenance mode, and render the document:

    and then put your local Docker instance back in maintenance mode:

    • Configure your .env file for maintenance mode:

      MAINTENANCE_MODE=True
      DATABASE_USER=kuma_ro
      
    • docker-compose up -d

  3. Configure your environment with DEBUG=False because the maintenance-mode integration tests check for the non-debug version of the not-found page:

    DEBUG=False
    MAINTENANCE_MODE=True
    DATABASE_USER=kuma_ro
    

    This, in turn, will also require you to recompile your static assets:

    docker-compose up -d
    docker-compose exec web ./manage.py compilejsi18n
    docker-compose exec web ./manage.py collectstatic
    docker-compose restart web
    

Now you should be ready for a successful test run:

py.test --maintenance-mode -m "not search" tests/functional --base-url http://localhost:8000 --driver Chrome --driver-path /path/to/chromedriver

Note that the “search” tests are excluded. This is because the tests marked “search” are not currently designed to run against the sample database.

Enabling PYTHONWARNINGS

Python ignores some warnings by default, including DeprecationWarning. To see these warnings, you can set the PYTHONWARNINGS environment variable in your .env file. For example:

# Show every warning, every time it occurs
PYTHONWARNINGS=always

Or alternatively:

# Show every warning, but ignore repeats
PYTHONWARNINGS=default

Note: Explicitly setting PYTHONWARNINGS=default will not do what you expect. It actually disables the default filters, ensuring that every warning gets displayed, but only the first time it occurs on a given line.

See the PYTHONWARNINGS docs for more information on possible values.