[](https://github.com/ietf-tools/datatracker/releases) [](https://github.com/ietf-tools/datatracker/blob/main/LICENSE) [](https://codecov.io/gh/ietf-tools/datatracker) [](#prerequisites) [](#prerequisites) [](#prerequisites) [](#prerequisites) ##### The day-to-day front-end to the IETF database for people who work on IETF standards.

• Production Website
• Changelog
• Contributing
• Getting Started - tl;dr
  • Creating a Fork
  • Git Cloning Tips
  • Docker Dev Environment
• Database & Assets
• Old Datatracker Branches
• Frontend Development
  • Intro
  • Vite
  • Parcel
  • Bootstrap
  • Serving Static Files via CDN
  • Handling of External Javascript and CSS Components
  • Handling of Internal Static Files
  • Changes to Template Files
  • Deployment
• Running Tests
  • Python
  • Frontend
  • Diff Tool

Getting Started

This project is following the standard Git Feature Workflow development model. Learn about all the various steps of the development workflow, from creating a fork to submitting a pull request, in the Contributing guide.

Make sure to read the Styleguides section to ensure a cohesive code format across the project.

You can submit bug reports, enhancement and new feature requests in the discussions area. Accepted tickets will be converted to issues.

Creating a Fork

Click the Fork button in the top-right corner of the repository to create a personal copy that you can work on.

Note that some GitHub Actions might be enabled by default in your fork. You should disable them by going to Settings > Actions > General and selecting Disable actions (then Save).

Git Cloning Tips

As outlined in the Contributing guide, you will first want to create a fork of the datatracker project in your personal GitHub account before cloning it.

Windows developers: Start with WSL2 from the beginning.

Because of the extensive history of this project, cloning the datatracker project locally can take a long time / disk space. You can speed up the cloning process by limiting the history depth, for example (replace USERNAME with your GitHub username):

• To fetch only up to the 10 latest commits: sh git clone --depth=10 https://github.com/USERNAME/datatracker.git
• To fetch only up to a specific date: sh git clone --shallow-since=DATE https://github.com/USERNAME/datatracker.git

The tl;dr to get going

Note that you will have to have cloned the datatracker code locally - please read the above sections.

Datatracker development is performed using Docker containers. You will need to be able to run docker (and docker-compose) on your machine to effectively develop. It is possible to get a purely native install working, but it is very complicated and typically takes a first time datatracker developer a full day of setup, where the docker setup completes in a small number of minutes.

Many developers are using VS Code and taking advantage of VS Code's ability to start a project in a set of containers. If you are using VS Code, simply start VS Code in your clone and inside VS Code choose Restart in container.

If VS Code is not available to you, in your clone, type cd docker; ./run

Once the containers are started, run the tests to make sure your checkout is a good place to start from (all tests should pass - if any fail, ask for help at tools-help@). Inside the app container's shell type:

ietf/manage.py test --settings=settings_test

Note that we recently moved the datatracker onto PostgreSQL - you may still find older documentation that suggests testing with settings_sqlitetest. That will no longer work.

For a more detailed description of getting going, see docker/README.md.

Overview of the datatracker models

A beginning of a walkthrough of the datatracker models was prepared for the IAB AID workshop.

Docker Dev Environment

In order to simplify and reduce the time required for setup, a preconfigured docker environment is available.

Read the Docker Dev Environment guide to get started.

Database & Assets

Nightly database dumps of the datatracker are available as Docker images: ghcr.io/ietf-tools/datatracker-db:latest

Note that to update the database in your dev environment to the latest version, you should run the docker/cleandb script.

Frontend Development

Intro

We now use yarn to manage assets for the Datatracker, and vite/parcel to package them. yarn maintains its node packages under the .yarn directory.

The datatracker uses 2 different build systems, depending on the use case: - Vite for Vue 3 pages / components - Parcel for legacy pages / jQuery

Vite (Vue 3)

Pages will gradually be updated to Vue 3 components. These components are located under the /client directory.

Each Vue 3 app has its own sub-directory. For example, the agenda app is located under /client/agenda.

The datatracker makes use of the Django-Vite plugin to point to either the Vite.js server or the precompiled production files. The DJANGO_VITE_DEV_MODE flag, found in the ietf/settings_local.py file determines whether the Vite.js server is used or not.

In development mode, you must start the Vite.js development server, in addition to the usual Datatracker server:

yarn dev

Any changes made to the files under /client will automatically trigger a hot-reload of the modified components.

To generate production assets, run the build command:

yarn build

This will create packages under ietf/static/dist-neue, which are then served by the Django development server, and which must be uploaded to the CDN.

Parcel (Legacy/jQuery)

The Datatracker includes these packages from the various Javascript and CSS files in ietf/static/js and ietf/static/css respectively, bundled using Parcel. Static images are likewise in ietf/static/images.

Whenever changes are made to the files under ietf/static, you must re-run the build command to package them:

yarn legacy:build

This will create packages under ietf/static/dist/ietf, which are then served by the Django development server, and which must be uploaded to the CDN.

Bootstrap

The "new" datatracker uses Twitter Bootstrap for the UI.

Get familiar with https://getbootstrap.com/getting-started/ and use those UI elements, CSS classes, etc. instead of cooking up your own.

Some ground rules:

• Think hard before tweaking the bootstrap CSS, it will make it harder to upgrade to future releases.
• No <style> tags in the HTML! Put CSS into the "morecss" block of a template instead.
• CSS that is used by multiple templates goes into static/css/ietf.css or a new CSS file.
• Javascript that is only used on one template goes into the "js" block of that template.
• Javascript that is used by multiple templates goes into static/js/ietf.js or a new js file.
• Avoid CSS, HTML styling or Javascript in the python code!

Serving Static Files via CDN

Production Mode

If resources served over a CDN and/or with a high max-age don't have different URLs for different versions, then any component upgrade which is accompanied by a change in template functionality will have a long transition time during which the new pages are served with old components, with possible breakage. We want to avoid this.

The intention is that after a release has been checked out, but before it is deployed, the standard django collectstatic management command will be run, resulting in all static files being collected from their working directory location and placed in an appropriate location for serving via CDN. This location will have the datatracker release version as part of its URL, so that after the deployment of a new release, the CDN will be forced to fetch the appropriate static files for that release.

An important part of this is to set up the STATIC_ROOT and STATIC_URL settings appropriately. In 6.4.0, the setting is as follows in production mode:

STATIC_URL = "https://www.ietf.org/lib/dt/%s/"%__version__
STATIC_ROOT = CDN_ROOT + "/a/www/www6s/lib/dt/%s/"%__version__

The result is that all static files collected via the collectstatic command will be placed in a location served via CDN, with the release version being part of the URL.

Development Mode

In development mode, STATIC_URL is set to /static/, and Django's staticfiles infrastructure makes the static files available under that local URL root (unless you set settings.SERVE_CDN_FILES_LOCALLY_IN_DEV_MODE to False). It is not necessary to actually populate the static/ directory by running collectstatic in order for static files to be served when running ietf/manage.py runserver -- the runserver command has extra support for finding and serving static files without running collectstatic.

In order to work backwards from a file served in development mode to the location from which it is served, the mapping is as follows:

Handling of External Javascript and CSS Components

In order to make it easy to keep track of and upgrade external components, these are now handled by a tool called yarn via the configuration in package.json.

To add a new package, simply run (replace <package-name> with the NPM module name):

yarn add <package-name>

Handling of Internal Static Files

Previous to this release, internal static files were located under static/, mixed together with the external components. They are now located under ietf/static/ietf/ and ietf/secr/static/secr, and will be collected for serving via CDN by the collectstatic command. Any static files associated with a particular app will be handled the same way (which means that all admin/ static files automatically will be handled correctly, too).

Changes to Template Files

In order to make the template files refer to the correct versioned CDN URL (as given by the STATIC_URL root) all references to static files in the templates have been updated to use the static template tag when referring to static files. This will automatically result in both serving static files from the right place in development mode, and referring to the correct versioned URL in production mode and the simpler /static/ URLs in development mode.

Deployment

During deployment, it is now necessary to run the management command:

ietf/manage.py collectstatic
````
before activating a new release.

## Running Tests

### Python Tests

From a datatracker container, run the command:
```sh
./ietf/manage.py test --settings=settings_test

You can limit the run to specific tests using the --pattern argument.

Frontend Tests

Frontend tests are done via Playwright. There're 2 different type of tests:

• Tests that test Vue pages / components and run natively without any external dependency.
• Tests that require a running datatracker instance to test against (usually legacy views).

Make sure you have Node.js 16.x or later installed on your machine.

Run Vue Tests

:warning: All commands below MUST be run from the ./playwright directory, unless noted otherwise.

1. Run once to install dependencies on your system: sh npm install npm run install-deps

2. Run in a separate process, from the project root directory: sh yarn preview

3. Run the tests, in of these 3 modes, from the ./playwright directory:

   3.1 To run the tests headlessly (command line mode): sh npm test 3.2 To run the tests visually (CANNOT run in docker): sh npm run test:visual

   3.3 To run the tests in debug mode (CANNOT run in docker): sh npm run test:debug

Run Legacy Views Tests

First, you need to start a datatracker instance (dev or prod), ideally from a docker container, exposing the 8000 port.

:warning: All commands below MUST be run from the ./playwright directory.

1. Run once to install dependencies on your system:

npm install
npm run install-deps

1. Run the tests headlessly (command line mode):

npm run test:legacy

Diff Tool

To compare 2 different datatracker instances and look for diff, read the diff tool instructions.