Currently, we use both Python's built-in datetime library and Pendulum
to do datetime operations. For the sake of consistency, we should try to
stick to one library for datetimes. We could have used either, but
Pendulum has a more ergonomic API, so I decided to go with it when
possible.
The places where were we didn't / couldn't replace datetime are:
- checking instances of datetimes. Pendulum's objects are subclasses of
python native datetime objects, so it's still useful to import
datetime in those cases of using is_instance()
- WTForms date validators expect datetime style string formats --
Pendulum has its own format for formatting/ parsing strings. As such,
our custom validator DateRange needs to use datetime.stptime() to
account for this format.
AT-AT needs to be able to track which user tasks failed and why. To
accomplish this we:
- Enabled Celery's results backend, which logs task results to a data
store; a Postgres table, in our case.
(https://docs.celeryproject.org/en/latest/userguide/tasks.html#result-backends)
- Created tables to track the relationships between the relevant models
(Environment, EnvironmentRole) and their task failures.
- Added an `on_failure` hook that tasks can use. The hook will add
records to the job failure tables.
Now a resource like an `Environment` has access to it task failures
through the corresponding failure table.
Notes:
- It might prove useful to use a real foreign key to the Celery results
table eventually. I did not do it here because it requires that we
explicitly store the Celery results table schema as a migration and
add a model for it. In the current implementation, AT-AT can be
agnostic about where the results live.
- We store the task results indefinitely, so it is important to specify
tasks for which we do not care about the results (like `send_mail`)
via the `ignore_result` kwarg.
Debug mode allows route integration tests to raise explicit exceptions on
errors, instead of returning error pages. Some portions of the test
suite need to be able to ignore exceptions (the response is not under
test) so they use a separate pytest fixture version of the app and
client that are configured with debug disabled, as it would be in
production.
Celery provides a more robust set of queueing options for both tasks and
worker processes. Updates include:
- infrastructure necessary to run Celery, including celery entrypoint
- backgrounded functions are now imported directly from atst.jobs
- update tests as-needed
- update kubernetes worker pod command
