This is a very large refactoring which aims at making Tutor both more
extendable and more generic. Historically, the Tutor plugin system was
designed as an ad-hoc solution to allow developers to modify their own
Open edX platforms without having to fork Tutor. The plugin API was
simple, but limited, because of its ad-hoc nature. As a consequence,
there were many things that plugin developers could not do, such as
extending different parts of the CLI or adding custom template filters.
Here, we refactor the whole codebase to make use of a generic plugin
system. This system was inspired by the Wordpress plugin API and the
Open edX "hooks and filters" API. The various components are added to a
small core thanks to a set of actions and filters. Actions are callback
functions that can be triggered at different points of the application
lifecycle. Filters are functions that modify some data. Both actions and
filters are collectively named as "hooks". Hooks can optionally be
created within a certain context, which makes it easier to keep track of
which application created which callback.
This new hooks system allows us to provide a Python API that developers
can use to extend their applications. The API reference is added to the
documentation, along with a new plugin development tutorial.
The plugin v0 API remains supported for backward compatibility of
existing plugins.
Done:
- Do not load commands from plugins which are not enabled.
- Load enabled plugins once on start.
- Implement contexts for actions and filters, which allow us to keep track of
the source of every hook.
- Migrate patches
- Migrate commands
- Migrate plugin detection
- Migrate templates_root
- Migrate config
- Migrate template environment globals and filters
- Migrate hooks to tasks
- Generate hook documentation
- Generate patch reference documentation
- Add the concept of action priority
Close #499.
I found the existing docs a bit light on the particulars
of how the YAML and Python plugin APIs relate. I was
able to figure it out (there's a nice congruence
between them) but I think these tweaks should it make
it more immediately obvious to readers how the Python
API is a essentially a superset of the YAML API that
allows for dynamic behavior.
Forum is an optional feature, and as such it deserves its own plugin. Starting
from Maple, users will be able to install the forum from
https://github.com/overhangio/tutor-forum/
Close #450.
Previously, it was not possible to override the docker registry for just
one or a few services. Setting the DOCKER_REGISTRY configuration
parameter would apply to all images. This was inconvenient. To resolve
this, we include the docker registry value in the DOCKER_IMAGE_*
configuration parameters. This allows users to override the docker
registry individually by defining the DOCKER_IMAGE_SERVICENAME
configuration parameter.
See https://discuss.overhang.io/t/kubernetes-ci-cd-pipeline/765/3
Running jobs was previously done with "exec". This was because it
allowed us to avoid copying too much container specification information
from the docker-compose/deployments files to the jobs files. However,
this was limiting:
- In order to run a job, the corresponding container had to be running.
This was particularly painful in Kubernetes, where containers are
crashing as long as migrations are not correctly run.
- Containers in which we need to run jobs needed to be present in the
docker-compose/deployments files. This is unnecessary, for example when
mysql is disabled, or in the case of the certbot container.
Now, we create dedicated jobs files, both for local and k8s deployment.
This introduces a little redundancy, but not too much. Note that
dependent containers are not listed in the docker-compose.jobs.yml file,
so an actual platform is still supposed to be running when we launch the
jobs.
This also introduces a subtle change: now, jobs go through the container
entrypoint prior to running. This is probably a good thing, as it will
avoid forgetting about incorrect environment variables.
In k8s, we find ourselves interacting way too much with the kubectl
utility. Parsing output from the CLI is a pain. So we need to switch to
the native kubernetes client library.
