In certain conditions in which the set of metrics returned by Heapster
is completely disjoint from the set of pods returned by the API server,
we can have a request sum of zero, which can cause a panic (due to
division by zero). This checks for that condition.
Fixes#39680
Currently, the HPA considers unready pods the same as ready pods when
looking at their CPU and custom metric usage. However, pods frequently
use extra CPU during initialization, so we want to consider them
separately.
This commit causes the HPA to consider unready pods as having 0 CPU
usage when scaling up, and ignores them when scaling down. If, when
scaling up, factoring the unready pods as having 0 CPU would cause a
downscale instead, we simply choose not to scale. Otherwise, we simply
scale up at the reduced amount caculated by factoring the pods in at
zero CPU usage.
The effect is that unready pods cause the autoscaler to be a bit more
conservative -- large increases in CPU usage can still cause scales,
even with unready pods in the mix, but will not cause the scale factors
to be as large, in anticipation of the new pods later becoming ready and
handling load.
Similarly, if there are pods for which no metrics have been retrieved,
these pods are treated as having 100% of the requested metric when
scaling down, and 0% when scaling up. As above, this cannot change the
direction of the scale.
This commit also changes the HPA to ignore superfluous metrics -- as
long as metrics for all ready pods are present, the HPA we make scaling
decisions. Currently, this only works for CPU. For custom metrics, we
cannot identify which metrics go to which pods if we get superfluous
metrics, so we abort the scale.
Here are a list of changes along with an explanation of how they work:
1. Add a new string field called TargetSelector to the external version of
extensions Scale type (extensions/v1beta1.Scale). This is a serialized
version of either the map-based selector (in case of ReplicationControllers)
or the unversioned.LabelSelector struct (in case of Deployments and
ReplicaSets).
2. Change the selector field in the internal Scale type (extensions.Scale) to
unversioned.LabelSelector.
3. Add conversion functions to convert from two external selector fields to a
single internal selector field. The rules for conversion are as follows:
i. If the target resource that this scale targets supports LabelSelector
(Deployments and ReplicaSets), then serialize the LabelSelector and
store the string in the TargetSelector field in the external version
and leave the map-based Selector field as nil.
ii. If the target resource only supports a map-based selector
(ReplicationControllers), then still serialize that selector and
store the serialized string in the TargetSelector field. Also,
set the the Selector map field in the external Scale type.
iii. When converting from external to internal version, parse the
TargetSelector string into LabelSelector struct if the string isn't
empty. If it is empty, then check if the Selector map is set and just
assign that map to the MatchLabels component of the LabelSelector.
iv. When converting from internal to external version, serialize the
LabelSelector and store it in the TargetSelector field. If only
the MatchLabel component is set, then also copy that value to
the Selector map field in the external version.
4. HPA now just converts the LabelSelector field to a Selector interface
type to list the pods.
5. Scale Get and Update etcd methods for Deployments and ReplicaSets now
return extensions.Scale instead of autoscaling.Scale.
6. Consequently, SubresourceGroupVersion override and is "autoscaling"
enabled check is now removed from pkg/master/master.go
7. Other small changes to labels package, fuzzer and LabelSelector
helpers to piece this all together.
8. Add unit tests to HPA targeting Deployments and ReplicaSets.
9. Add an e2e test to HPA targeting ReplicaSets.
Dr. Stefan Schimanski