* Changing session cookie name and added a way for clients to know what the key name is
* Adding session information to docs
* Fixing how awxkit gets the session id header
Right now, without this, we end up with a different number for max_workers than max_forks. For example, on a control node with 16 Gi of RAM,
max_mem_capacity w/ 100 MB/fork = (16*1024)/100 --> 164
max_workers = 5 * 16 --> 80
This means we would allow that control node to control up to 164 jobs, but all jobs after the 80th job will be stuck in `waiting` waiting for a dispatch worker to free up to run the job.
Sharing the /etc/redhat-access-insights is no longer
required for EEs. Furthermore, this fixes a SELinux issue
when launching multiple jobs with concurrency and fact_caching enabled.
i.e:
lsetxattr /etc/redhat-access-insights: operation not permitted
fix memory and cpu settings to suport k8s resource request format
* fix conversion of memory setting to bytes
This setting has not been getting set by default, and needed some fixing
up to be compatible with setting the memory in the same way as we set it
in the operator, as well as with other changes from last year which
assume that ansible runner is returning memory in bytes.
This way we can start setting this setting in the operator, and get a
more accurate reflection of how much memory is available to the control
pod in k8s.
On platforms where services are all sharing memory, we deduct a
penalty from the memory available. On k8s we don't need to do this
because the web, redis, and task containers each have memory
allocated to them.
* Support CPU setting expressed in units used by k8s
This setting has not been getting set by default, and needed some fixing
up to be compatible with setting the CPU resource request/limits in the
same way as we set it in the resource requests/limits.
This way we can start setting this setting in the
operator, and get a more accurate reflection of how much cpu is
available to the control pod in k8s.
Because cpu on k8s can be partial cores, migrate cpu field to decimal.
k8s does not allow granularity of less than 100m (equivalent to 0.1 cores), so only
store up to 1 decimal place.
fix analytics to deal with decimal cpu
need to use DjangoJSONEncoder when Decimal fields in data passed to
json.dumps
There is no current need or use to keep a seperate dependency graph for
each instance group. In the interest of making it clearer what the
current code does, eliminate this superfluous complication.
We are no longer ever referencing any accounting of instance group
capacity, instead we only look
at capacity on intances.
* Select control node before start task
Consume capacity on control nodes for controlling tasks and consider
remainging capacity on control nodes before selecting them.
This depends on the requirement that control and hybrid nodes should all
be in the instance group named 'controlplane'. Many tests do not satisfy that
requirement. I'll update the tests in another commit.
* update tests to use controlplane
We don't start any tasks if we don't have a controlplane instance group
Due to updates to fixtures, update tests to set node type and capacity
explicitly so they get expected result.
* Fixes for accounting of control capacity consumed
Update method is used to account for currently consumed capacity for
instance groups in the in-memory capacity tracking data structure we initialize in
after_lock_init and then update via calculate_capacity_consumed (both in
task_manager.py)
Also update fit_task_to_instance to consider control impact on instances
Trust that these functions do the right thing looking for a
node with capacity, and cut out redundant check for the whole group's
capacity per Alan's reccomendation.
* Refactor now redundant code
Deal with control type tasks before we loop over the preferred instance
groups, which cuts out the need for some redundant logic.
Also, fix a bug where I was missing assigning the execution node in one case!
* set job explanation on tasks that need capacity
move the job explanation for jobs that need capacity to a function
so we can re-use it in the three places we need it.
* project updates always run on the controlplane
Instance group ordering makes no sense on project updates because they
always need to run on the control plane.
Also, since hybrid nodes should always run the control processes for the
jobs running on them as execution nodes, account for this when looking for a
execution node.
* fix misleading message
the variables and wording were both misleading, fix to be more accurate
description in the two different cases where this log may be emitted.
* use settings correctly
use settings.DEFAULT_CONTROL_PLANE_QUEUE_NAME instead of a hardcoded
name
cache the controlplane_ig object during the after lock init to avoid
an uneccesary query
eliminate mistakenly duplicated AWX_CONTROL_PLANE_TASK_IMPACT and use
only AWX_CONTROL_NODE_TASK_IMPACT
* add test for control capacity consumption
add test to verify that when there are 2 jobs and only capacity for one
that one will move into waiting and the other stays in pending
* add test for hybrid node capacity consumption
assert that the hybrid node is used for both control and execution and
capacity is deducted correctly
* add test for task.capacity_type = control
Test that control type tasks have the right capacity consumed and
get assigned to the right instance group
Also fix lint in the tests
* jobs_running not accurate for control nodes
We can either NOT use "idle instances" for control nodes, or we need
to update the jobs_running property on the Instance model to count
jobs where the node is the controller_node.
I didn't do that because it may be an expensive query, and it would be
hard to make it match with jobs_running on the InstanceGroup which
filters on tasks assigned to the instance group.
This change chooses to stop considering "idle" control nodes an option,
since we can't acurrately identify them.
The way things are without any change, is we are continuing to over consume capacity on control nodes
because this method sees all control nodes as "idle" at the beginning
of the task manager run, and then only counts jobs started in that run
in the in-memory tracking. So jobs which last over a number of task
manager runs build up consuming capacity, which is accurately reported
via Instance.consumed_capacity
* Reduce default task impact for control nodes
This is something we can experiment with as far as what users
want at install time, but start with just 1 for now.
* update capacity docs
Describe usage of the new setting and the concept of control impact.
Co-authored-by: Alan Rominger <arominge@redhat.com>
Co-authored-by: Rebeccah <rhunter@redhat.com>
