HPA not scaling down - kubernetes

Question

I hope you can shed some light on this.
I am facing the same issue as described here: Kubernetes deployment not scaling down even though usage is below threshold
My configuration is almost identical.
I have checked the hpa algorithm, but I cannot find an explanation for the fact that I am having only one replica of my-app3.
Any hints?
kubectl get hpa -A
NAMESPACE NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
my-ns1 my-app1 Deployment/my-app1 49%/75%, 2%/75% 1 10 2 20h
my-ns2 my-app2 Deployment/my-app2 50%/75%, 10%/75% 1 10 2 22h
my-ns2 my-app3 Deployment/my-app3 47%/75%, 10%/75% 1 10 1 22h
kubectl top po -A
NAMESPACE NAME CPU(cores) MEMORY(bytes)
my-ns1 pod-app1-8d694bc8f-mkbrh 1m 76Mi
my-ns1 pod-app1-8d694bc8f-qmlnw 1m 72Mi
my-ns2 pod-app2-59d895d96d-86fgm 1m 77Mi
my-ns2 pod-app2-59d895d96d-zr67g 1m 73Mi
my-ns2 pod-app3-6f8cbb68bf-vdhsd 1m 47Mi

Answer 1

Posting this answer as it could be beneficiary for the community members on why exactly Horizontal Pod Autoscaler decided not to scale the amount of replicas in this particular setup.
The formula for amount of replicas workload will have is:
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
Following on the describe of HPA:
NAMESPACE NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
my-ns1 my-app1 Deployment/my-app1 49%/75%, 2%/75% 1 10 2 20h
my-ns2 my-app2 Deployment/my-app2 50%/75%, 10%/75% 1 10 2 22h
my-ns2 my-app3 Deployment/my-app3 47%/75%, 10%/75% 1 10 1 22h
HPA decides on the amount of replicas on the premise of their current amount.
A side note: In the setup that uses multiple metrics (for example CPU and RAM) it will use the higher metric and act accordingly.
Also please consider that downscaling has a cooldown.
Calculation on each of the Deployments
ceil[] - round a number up:
ceil(4,55) = 5
ceil(4,01) = 5
app1:
Replicas = ceil[2 * (49 / 75)]
Replicas = ceil[2 * 0,6533..]
Replicas = ceil[1,3066..]
Replicas = 2
This example shows that there will be no changes to be amount of replicas.
Amount of replicas would go:
Up when the currentMetricValue (49) would exceed the desiredMetricValue (75)
Down when the currentMetricValue (49) would be less than half of the desiredMetricValue (75)
app2 is in the same situation as app1 so it can be skipped
app3:
Replicas = ceil[1 * (49 / 75)]
Replicas = ceil[1 * 0,6266..]
Replicas = ceil[0,6266..]
Replicas = 1
This example also shows that there will be no changes to be amount of replicas.
Amount of replicas would go:
Up when the currentMetricValue (47) would exceed the desiredMetricValue (75)
Additional resources:
Kubernetes.io: Docs: Tasks: Run application: Horizontal pod autoscale

Answer 2

Indeed from my research it seems that the HPA algorithm works in this way:
https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details
Do not know the reason why my-app3 was assigned one replica and the other two apps two replicas, but according to the algorithm it is not needed to scale out at this time.