Pyramid views for serving collections of compiled static assets (eg. bundles of JavaScript and CSS).
Compared to Pyramid's builtin static asset functionality , this provides a convenient way to serve assets based on certain assumptions about how assets are generated and opinions about how they should be served:
- The assets are assumed to be compiled artefacts in an output directory
populated by frontend build tooling, rather than source files inside the
Python package. Typically Hypothesis applications use a
build
directory in the root of the repository. - Cache busting is always enabled and is done via query strings. These query strings are checked, if present, when serving a request to avoid responses being stored under the wrong keys in downstream caches.
- It is assumed that compressing bytes (eg. with gzip or Brotli) will be handled by a service like Cloudflare, not the Python application.
Additionally h-assets provides a way to define collections (bundles) of
assets and methods to generate cache-busted URLs for all assets in the bundle.
This is useful for example to render all the <script>
or <style>
tags that
are needed by a certain part of a site.
Using h-assets in a Pyramid project involves three steps:
- Prepare the compiled assets for use with h-assets
- During Pyramid application configuration, create an asset
Environment
to handle asset URL generation and register a view to serve assets from that environment - Expose the URL-generation methods from the asset
Environment
to your templating system so that templates can generate asset URLs
-
Set up a process to compile or copy assets from source files into an output directory. Conventionally Hypothesis projects use a folder called
build
in the repository root. -
In the output directory generate a JSON manifest file (eg.
manifest.json
) that maps asset paths to URLs with cache-busting query strings. Example content:{ "scripts/app.bundle.js": "scripts/app.bundle.js?abcdef", "scripts/vendor.bundle.js": "scripts/vendor.bundle.js?xyz123" }
Any format is allowed for the cache-buster. Hypothesis projects typically use the first few characters of a hash (eg. SHA-1) of the file's contents.
-
Create an INI file (eg.
assets.ini
) that defines collections ("bundles") of assets that are used together. Example content:[bundles] frontend_apps_js = scripts/browser_check.bundle.js scripts/frontend_apps.bundle.js frontend_apps_css = styles/frontend_apps.css
To serve assets using h-assets, a Pyramid view needs to be created using the
assets_view
function.
In the Pyramid app configuration, define a route where the URL is a base URL
followed by a *subpath
:
def includeme(config):
config.add_route("assets", "/assets/*subpath")
To register the view associated with this route, first create an Environment
to handle generation of asset URLs. Then use assets_view
to create the view
callable for use with config.add_view
:
import os.path
from h_assets import Environment, assets_view
def includeme(config):
# This assumes the following repository structure:
# build/ - Compiled frontend assets
# manifest.json
# projectname/
# assets.py - This module
# routes.py - Route definitions
# assets.ini
root_dir = os.path.dirname(__file__)
assets_env = Environment(
assets_base_url="/assets",
bundle_config_path="{root_dir}/assets.ini",
manifest_path=f"{root_dir}/../build/manifest.json",
)
# Store asset environment in registry for use in registering `asset_urls`
# Jinja2 helper in `app.py`.
config.registry["assets_env"] = assets_env
config.add_view(route_name="assets", view=assets_view(assets_env))
To get a list of asset URLs for assets in a bundle, use the urls
method of the
asset Environment
. A common pattern is to expose these methods as global helpers
in the templating environment being used to generate HTML responses. For example,
in a project using pyramid_jinja2
:
jinja2_env = config.get_jinja2_environment()
jinja2_env.globals["asset_url"] = config.registry["assets_env"].url
jinja2_env.globals["asset_urls"] = config.registry["assets_env"].urls
Then a template can generate URLs using:
{% for url in asset_urls("frontend_apps_js") %}
<script async defer src="{{ url }}"></script>
{% endfor %}
First you'll need to install:
- Git.
On Ubuntu:
sudo apt install git
, on macOS:brew install git
. - GNU Make.
This is probably already installed, run
make --version
to check. - pyenv. Follow the instructions in pyenv's README to install it. The Homebrew method works best on macOS. The Basic GitHub Checkout method works best on Ubuntu. You don't need to set up pyenv's shell integration ("shims"), you can use pyenv without shims.
Then to set up your development environment:
git clone https://github.com/hypothesis/h-assets.git
cd h-assets
make help
-
First, to get PyPI publishing working you need to go to: https://github.com/organizations/hypothesis/settings/secrets/actions/PYPI_TOKEN and add h-assets to the
PYPI_TOKEN
secret's selected repositories. -
Now that the h-assets project has access to the
PYPI_TOKEN
secret you can release a new version by just creating a new GitHub release. Publishing a new GitHub release will automatically trigger a GitHub Actions workflow that will build the new version of your Python package and upload it to https://pypi.org/project/h-assets.
To change what versions of Python the project uses:
-
Change the Python versions in the cookiecutter.json file. For example:
"python_versions": "3.10.4, 3.9.12",
-
Re-run the cookiecutter template:
make template
-
Commit everything to git and send a pull request
To change the production dependencies in the setup.cfg
file:
-
Change the dependencies in the
.cookiecutter/includes/setuptools/install_requires
file. If this file doesn't exist yet create it and add some dependencies to it. For example:pyramid sqlalchemy celery
-
Re-run the cookiecutter template:
make template
-
Commit everything to git and send a pull request
To change the project's formatting, linting and test dependencies:
-
Change the dependencies in the
.cookiecutter/includes/tox/deps
file. If this file doesn't exist yet create it and add some dependencies to it. Use tox's factor-conditional settings to limit which environment(s) each dependency is used in. For example:lint: flake8, format: autopep8, lint,tests: pytest-faker,
-
Re-run the cookiecutter template:
make template
-
Commit everything to git and send a pull request