During a Deployment update there may be more Pods in the scale target
ref status than in the spec. This test verifies that we do not scale
to the status value. Instead we should stay at the spec value.
Fails before #79035 and passes after.
Add support for scaling to zero pods
minReplicas is allowed to be zero
condition is set once
Based on https://github.com/kubernetes/kubernetes/pull/61423
set original valid condition
add scale to/from zero and invalid metric tests
Scaling up from zero pods ignores tolerance
validate metrics when minReplicas is 0
Document HPA behaviour when minReplicas is 0
Documented minReplicas field in autoscaling APIs
This change adds pending pods to the ignored set first before
selecting pods missing metrics. Pending pods are always ignored when
calculating scale.
When the HPA decides which pods and metric values to take into account
when scaling, it divides the pods into three disjoint subsets: 1)
ready 2) missing metrics and 3) ignored. First the HPA selects pods
which are missing metrics. Then it selects pods should be ignored
because they are not ready yet, or are still consuming CPU during
initialization. All the remaining pods go into the ready set. After
the HPA has decided what direction it wants to scale based on the
ready pods, it considers what might have happened if it had the
missing metrics. It makes a conservative guess about what the missing
metrics might have been, 0% if it wants to scale up--100% if it wants
to scale down. This is a good thing when scaling up, because newly
added pods will likely help reduce the usage ratio, even though their
metrics are missing at the moment. The HPA should wait to see the
results of its previous scale decision before it makes another
one. However when scaling down, it means that many missing metrics can
pin the HPA at high scale, even when load is completely removed. In
particular, when there are many unschedulable pods due to insufficient
cluster capacity, the many missing metrics (assumed to be 100%) can
cause the HPA to avoid scaling down indefinitely.
current scale. Two important ones are when missing metrics might
change the direction of scaling, and when the recommended scale is
within tolerance of the current scale.
The way that ReplicaCalculator signals it's desire to not change the
current scale is by returning the current scale. However the current
scale is from scale.Status.Replicas and can be larger than
scale.Spec.Replicas (e.g. during Deployment rollout with configured
surge). This causes a positive feedback loop because
scale.Status.Replicas is written back into scale.Spec.Replicas,
further increasing the current scale.
This PR fixes the feedback loop by plumbing the replica count from
spec through horizontal.go and replica_calculator.go so the calculator
can punt with the right value.
Handle a case in the Horizontal Pod Autoscaler Controller when scaling
on multiple metrics and one or more is missing or invalid.
If all metrics are missing - return an error and leave the isScalingActive
condition as that for the last invalid metric.
If some metrics are missing/invalid and some are valid and found -
if a scale up would be triggered by the valid metrics ignore the missing
metrics and scale up, if a scale down would be triggered, return an error
and leave the isScalingActive condition as that for the last invalid metric.
Add three tests for handling invalid metrics when scaling on
multiple metrics - one for scaling up successfully (new behaviour)
and two for ensuring we don't scale down (existing behaviour).
The reactor in runTest is set to catch all actions, but eventually it
only handles CreateAction without checking action type which might fail
sometimes when Patch arrives. This fix ensures we handle only the
CreateAction.
