grafana via prometheus.
This metric is a good indicator of how far behind the callback receiver
is. The higher the load the further behind/the greater the number of
seconds the metric will display.
This number being high may indicate the need for horizontal scaling in
the control plane or vertically scaling the number of callback
receivers.
* Track host_status_counts and use that to process notifications
* Remove now unused setting
* Back out changes to callback class not needed after all
* Skirt the need for duck typing by leaning on the cached field
* Delete tests for deleted task
* Revert "Back out changes to callback class not needed after all"
This reverts commit 3b8ae350d218991d42bffd65ce4baac6f41926b2.
* Directly hardcode stats_event_type for callback class
* Fire notifications if stats event was never sent
* Remove test content for deleted methods
* Add placeholder for when no hosts matched
* Make field default be None, denote events processed with empty dict
* Make UI process null value for host_status_counts
* Fix tracking of EOF dispatch for system jobs
* Reorganize EVENT_MAP into class properties
* Consolidate conditional I missed from EVENT_MAP refactor
* Give up on the null condition, also applies for empty hosts
* Remove cls position argument not being used
* Move wrapup method out of class, add tests
--- Added 3 new sub-package : awx.main.tasks.system , awx.main.tasks.jobs , awx.main.tasks.receptor
--- Modified the functional tests and unit tests accordingly
- Adds a Metrics() class that can track data such as number of
events the callback receiver inserted into database
- Exposes this metric data at the api/v2/metrics/ endpoint.
This data is prometheus-friendly
- Metric data is stored in memory, then periodically saved to Redis.
- Metric data is periodically broadcast to other nodes in the cluster,
so that each node has a copy of the most recent metric data collected.
the bigint migration removed the foreign key constraints for:
- host_id
- job_id (and projectupdate_id, etc...)
because of this, we don't really need to check explicitly for a host_id
IntegrityError anymore (because it won't occur)
additionally, while it's possible to insert an event with a mismatched
job_id now (for example, you can totally start a long-running job, and
delete the job record in the background using the ORM or psql), doing
so results in DoesNotExist errors in the code that handles the
playbook_on_stats events
instead, just have each worker connect directly to redis
this has a few benefits:
- it's simpler to explain and debug
- back pressure on the queue keeps messages around in redis (which is
observable, and survives the restart of Python processes)
- it's likely notably more performant at high loads
success/failure notifications for *playbooks* include summary data about
the hosts in based on the contents of the playbook_on_stats event
the current implementation suffers from a number of race conditions that
sometimes can cause that data to be missing or incomplete; this change
makes it so that for *playbooks* we build (and send) the notification in
response to the playbook_on_stats event, not the EOF event
additionaly, optimize away several per-event host lookups and
changed/failed propagation lookups
we've always performed these (fairly expensive) queries *on every event
save* - if you're processing tens of thousands of events in short
bursts, this is way too slow
this commit also introduces a new command for profiling the insertion
rate of events, `awx-manage callback_stats`
see: https://github.com/ansible/awx/issues/5514
this commit implements the bulk of `awx-manage run_dispatcher`, a new
command that binds to RabbitMQ via kombu and balances messages across
a pool of workers that are similar to celeryd workers in spirit.
Specifically, this includes:
- a new decorator, `awx.main.dispatch.task`, which can be used to
decorate functions or classes so that they can be designated as
"Tasks"
- support for fanout/broadcast tasks (at this point in time, only
`conf.Setting` memcached flushes use this functionality)
- support for job reaping
- support for success/failure hooks for job runs (i.e.,
`handle_work_success` and `handle_work_error`)
- support for auto scaling worker pool that scale processes up and down
on demand
- minimal support for RPC, such as status checks and pool recycle/reload
