* Introduces the idea of content errors into the learning_sequences
public API, accessible using get_content_errors().
* Makes course outline generation much more resilient to unusual
structures (e.g. Section -> Unit with no Sequence in between),
with the understanding that anything that doesn't conform to the
standard structure will simply be skipped.
* Improves the Django Admin for learning_sequences to display
content errors and improve sequence data browsing within a course.
* Switches the main table viewed in the Django admin from
LearningContext to CourseContext, which is appropriate since only
course runs generate outlines.
This was done as part of TNL-8057, with the end goal of making
course outline generation resilient enough to switch over apps
to using the learning_sequences outline API. The types of course
structure errors that this PR addresses cause display issues even
in the current Outline Page experience, but would break the outline
generation for learning_sequences altogether.
The approach for error messages here is very generic, to keep
modulestore concepts from seeping into learning_sequences (which is
not aware of the modulestore/contentstore). We may need to address
this later, with a more normalized content error data model.
While the Django admin page is backwards compatible with the old
versions of the models, we should run the backfill_course_outlines
management command after deploying this change, to get the full
benefits.
The Learning MFE had a bug where instructors selecting
'View As: Audit' would find themselves redirected to the
'Enroll Now' page. This was because the Courseware API,
which powers the MFE, checked courseware access *after*
setting up masquerading--so, instead of checking whether
the instructor had access to view the course, we checked
whether some arbitrary un-enrolled learner would have
access to the course (the answer is generally No).
We fix this by doing what the Legacy courseware index
does: calculate courseware access *before* setting up
masquerading.
TNL-7989
This will make it possible to make a New Relic dashboard for the
learning_sequences API calls that tracks call performance across
different transactions (by querying Spans). Our goal would be to
offer SLAs around 99th percentile performance.
Course IDs and User ID metrics are also added, so that we can see
outliers in Span reporting, for later investigation.
* Adds the backfill_course_outlines management command to contentstore
* Adds a read-only Django admin interface to learning_sequences for the
support team and debugging.
* Adds two new functions to the learning_sequences public API:
key_supports_outlines and get_course_keys_with_outlines
The learning_sequences app isn't supposed to know about contentstore or
modulestore, as it's intended to be extracted out of edx-platform in the
long term. Therefore, the backfill_course_outlines command is in
contentstore, and not learning_sequences.
This work was tracked in TNL-7983, but it also fixes a bug where we were
trying to generate course outlines for libraries (TNL-7981).
All Open edX instances upgrading to Lilac should run the
backfill_course_outlines command as part of their upgrade process.
Centralize the logic for choosing between
MFE and Legacy-frontend courseware within
three new functions:
* courseware_mfe_is_active
* courseware_mfe_is_visible
* courseware_legacy_is_visible
This allows us to create another new function:
* get_courseware_url
which can be called anywhere in LMS/Studio
to get the canonical URL to courseware
content (whether it be MFE or Legacy).
In future commits we we begin using
get_courseware_url throughout the platform.
TNL-7796
django-not-configured is an error raised by pylint (with
the pylint-django plugin) when it's not correctly configured.
We should not be applying lint amnesty for such a violation.
COURSE_CERT_DATE_CHANGE was being called before saving the new data in
the course overview. The listeners were expecting to pull the data out
of the course overview, and thus were only right about half the time.
This moves the signal to trigger after the course publish signals are
handled.
