The current behavior is that the update is pushed into the scheduler,
and the scheduler cherry pick's what it needs. This leaves the task itself out
meaning any logging the scheduler did was not going to have the new task information in it.
When a task is told to execute it can be enqueued waiting for a worker.
This statistic will be superior to the existing delta based on scheduled for,
the current system can be effected by a user having slow queries or a long "delay" on the task.
This new way of measuring the same thing should allow us to accuratly measure when it is the task system's fault.
If we are caching run's in the kv storage system it is possible to get
the the cached version from the kv store and the recently completed run
from the analytical store. We just need to only show analytical results if
we find a duplicate.
fix(notification/check): include tags in check object in generated flux
Closes https://github.com/influxdata/influxdb/issues/14769
fix(notification/check): use selected field in threshold functions
Closes https://github.com/influxdata/influxdb/issues/14776
fix(testing): add selected field for check tests
fix(check): use real flux for threshold check
feat(notification/check): generate flux for deadman checks
chore(endpoint): rename webhook endpoint to http endpoint
fix(notification/rule): fetch url for flux script off of endpoint
fix(notification/rule): clean up slack and http rules
fix(notification/rule): change MessageTemp to MessageTemplate
fix(rules): pass endpoint in to rule during create
fix(ui): rename webhook to http
feat(notification/check): namespace deadman under alerts
fix(notification/check): nest tags under tags key in data object in flux
wip
feat(kv): log error if urm cannot be deleted for notification rule
fix(notification/rule): remove name from notify call in slack rule
chore(ui/cypress/e2e): skip rule create test
To have checks and notifications happen transactionally we need to be
able to alert the task system when a new task was created using the checks and notifications systems.
These two new middlewares allow us to inform the task system of a update
to a task that was created through the check or notification systems.
* feat(task): Remove token's from task structures
We had previously removed token's from the task api but left the token in place in several locations in the stack.
Now we can cleanly remove the extra tokens.
* feat(task): impersonate user on task execution
Passing tokens to tasks is cumbersome and we needed a way to more easily create tasks. With this change we no longer need a token on task create. We take the user that created the task and pass that in as the "owner". As far as the task is concerned the owner is the source of permissions.
This is done by adding an additional field on task create that is OwnerID. We will no longer respect the token passed in and it will be deprecated soon.
Things to do still:
Task updates need to allow for owners to be set.
Current behavior is that the first execution of a task happens based on the create time
of the task when using a 'every' schedule. If you create a task at 12:02 and want
the task to run every 15m. The first execution would happen at 12:17, and the 2nd would happen
at 12:30.
To fix this behavior I refactored the kv task to give a single source of knowledge.
We now have one function for finding exactly what the last scheduled task was.
We also now have a single method that calculates when the next schedule is due.
By unifying the logic it should always work the same way weather your asking when to run
or when creating a task.
* fix(tasks): Add a log message for run transition clairity
We on occasion will see a run in chronograf with missing run data.
We need to find out if we are submitting incomplete data or if we submit full data and somethinge else is happening
* Report errors found when iterating over flux query in task
* Add failing test for tasks executor result iterator exhaust failure
* Ensure errors exhausting tasks query result iterator are surfaced as task failure
* Update CHANGELOG with task result iteration error surfacing fix
docmerlin (j. Emrys Landivar)
Lyon Hill
Stuart Carnie
Michael Desa
Nathaniel Cook
Jonathan A. Sternberg
Adam
George
Alirie Gray
Sebastian Borza
Chris Goller