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HPA Specificity Improvements (#8757)

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Updated the HPA docs to reference the `autoscaling/v2beta2` API version,
and added documentation about the new fields.
This commit is contained in:
Solly Ross
2018-09-04 18:43:53 -04:00
committed by k8s-ci-robot
parent 183d214c84
commit 9d84a497b4
2 changed files with 102 additions and 45 deletions
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content/en/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough.md
+94 -40
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@@ -167,18 +167,18 @@ Here CPU utilization dropped to 0, and so HPA autoscaled the number of replicas
## Autoscaling on multiple metrics and custom metrics ## Autoscaling on multiple metrics and custom metrics
You can introduce additional metrics to use when autoscaling the `php-apache` Deployment You can introduce additional metrics to use when autoscaling the `php-apache` Deployment
by making use of the `autoscaling/v2beta1` API version. by making use of the `autoscaling/v2beta2` API version.
First, get the YAML of your HorizontalPodAutoscaler in the `autoscaling/v2beta1` form: First, get the YAML of your HorizontalPodAutoscaler in the `autoscaling/v2beta2` form:
```shell ```shell
$ kubectl get hpa.v2beta1.autoscaling -o yaml > /tmp/hpa-v2.yaml $ kubectl get hpa.v2beta2.autoscaling -o yaml > /tmp/hpa-v2.yaml
``` ```
Open the `/tmp/hpa-v2.yaml` file in an editor, and you should see YAML which looks like this: Open the `/tmp/hpa-v2.yaml` file in an editor, and you should see YAML which looks like this:
```yaml ```yaml
apiVersion: autoscaling/v2beta1 apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler kind: HorizontalPodAutoscaler
metadata: metadata:
name: php-apache name: php-apache
@@ -194,7 +194,9 @@ spec:
- type: Resource - type: Resource
resource: resource:
name: cpu name: cpu
targetAverageUtilization: 50 target:
type: Utilization
averageUtilization: 50
status: status:
observedGeneration: 1 observedGeneration: 1
lastScaleTime: <some-time> lastScaleTime: <some-time>
@@ -204,8 +206,9 @@ status:
- type: Resource - type: Resource
resource: resource:
name: cpu name: cpu
currentAverageUtilization: 0 current:
currentAverageValue: 0 averageUtilization: 0
averageValue: 0
``` ```
Notice that the `targetCPUUtilizationPercentage` field has been replaced with an array called `metrics`. Notice that the `targetCPUUtilizationPercentage` field has been replaced with an array called `metrics`.
@@ -215,8 +218,8 @@ the only other supported resource metric is memory. These resources do not chan
to cluster, and should always be available, as long as the `metrics.k8s.io` API is available. to cluster, and should always be available, as long as the `metrics.k8s.io` API is available.
You can also specify resource metrics in terms of direct values, instead of as percentages of the You can also specify resource metrics in terms of direct values, instead of as percentages of the
requested value. To do so, use the `targetAverageValue` field instead of the `targetAverageUtilization` requested value, by using a `target` type of `AverageValue` instead of `AverageUtilization`, and
field. setting the corresponding `target.averageValue` field instead of the `target.averageUtilization`.
There are two other types of metrics, both of which are considered *custom metrics*: pod metrics and There are two other types of metrics, both of which are considered *custom metrics*: pod metrics and
object metrics. These metrics may have names which are cluster specific, and require a more object metrics. These metrics may have names which are cluster specific, and require a more
@@ -224,31 +227,40 @@ advanced cluster monitoring setup.
The first of these alternative metric types is *pod metrics*. These metrics describe pods, and The first of these alternative metric types is *pod metrics*. These metrics describe pods, and
are averaged together across pods and compared with a target value to determine the replica count. are averaged together across pods and compared with a target value to determine the replica count.
They work much like resource metrics, except that they *only* have the `targetAverageValue` field. They work much like resource metrics, except that they *only* support a `target` type of `AverageValue`.
Pod metrics are specified using a metric block like this: Pod metrics are specified using a metric block like this:
```yaml ```yaml
type: Pods type: Pods
pods: pods:
metricName: packets-per-second metric:
targetAverageValue: 1k name: packets-per-second
target:
type: AverageValue
averageValue: 1k
``` ```
The second alternative metric type is *object metrics*. These metrics describe a different The second alternative metric type is *object metrics*. These metrics describe a different
object in the same namespace, instead of describing pods. Note that the metrics are not object in the same namespace, instead of describing pods. The metrics are not necessarily
fetched from the object -- they simply describe it. Object metrics do not involve averaging, fetched from the object; they only describe it. Object metrics support `target` types of
and look like this: both `Value` and `AverageValue`. With `Value`, the target is compared directly to the returned
metric from the API. With `AverageValue`, the value returned from the custom metrics API is divided
by the number of pods before being compared to the target. The following example is the YAML
representation of the `requests-per-second` metric.
```yaml ```yaml
type: Object type: Object
object: object:
metricName: requests-per-second metric:
target: name: requests-per-second
describedObject:
apiVersion: extensions/v1beta1 apiVersion: extensions/v1beta1
kind: Ingress kind: Ingress
name: main-route name: main-route
targetValue: 2k target:
type: Value
value: 2k
``` ```
If you provide multiple such metric blocks, the HorizontalPodAutoscaler will consider each metric in turn. If you provide multiple such metric blocks, the HorizontalPodAutoscaler will consider each metric in turn.
@@ -275,19 +287,25 @@ spec:
- type: Resource - type: Resource
resource: resource:
name: cpu name: cpu
targetAverageUtilization: 50 target:
kind: AverageUtilization
averageUtilization: 50
- type: Pods - type: Pods
pods: pods:
metricName: packets-per-second metric:
name: packets-per-second
targetAverageValue: 1k targetAverageValue: 1k
- type: Object - type: Object
object: object:
metricName: requests-per-second metric:
target: name: requests-per-second
describedObject:
apiVersion: extensions/v1beta1 apiVersion: extensions/v1beta1
kind: Ingress kind: Ingress
name: main-route name: main-route
targetValue: 10k target:
kind: Value
value: 10k
status: status:
observedGeneration: 1 observedGeneration: 1
lastScaleTime: <some-time> lastScaleTime: <some-time>
@@ -297,14 +315,47 @@ status:
- type: Resource - type: Resource
resource: resource:
name: cpu name: cpu
currentAverageUtilization: 0 current:
currentAverageValue: 0 averageUtilization: 0
averageValue: 0
- type: Object
object:
metric:
name: requests-per-second
describedObject:
apiVersion: extensions/v1beta1
kind: Ingress
name: main-route
current:
value: 10k
``` ```
Then, your HorizontalPodAutoscaler would attempt to ensure that each pod was consuming roughly Then, your HorizontalPodAutoscaler would attempt to ensure that each pod was consuming roughly
50% of its requested CPU, serving 1000 packets per second, and that all pods behind the main-route 50% of its requested CPU, serving 1000 packets per second, and that all pods behind the main-route
Ingress were serving a total of 10000 requests per second. Ingress were serving a total of 10000 requests per second.
### Autoscaling on more specific metrics
Many metrics pipelines allow you to describe metrics either by name or by a set of additional
descriptors called _labels_. For all non-resource metric types (pod, object, and external,
described below), you can specify an additional label selector which is passed to your metric
pipeline. For instance, if you collect a metric `http_requests` with the `verb`
label, you can specify the following metric block to scale only on GET requests:
```yaml
type: Object
object:
metric:
name: `http_requests`
selector: `verb=GET`
```
This selector uses the same syntax as the full Kubernetes label selectors. The monitoring pipeline
determines how to collapse multiple series into a single value, if the name and selector
match multiple series. The selector is additive, and cannot select metrics
that describe objects that are **not** the target object (the target pods in the case of the `Pods`
type, and the described object in the case of the `Object` type).
### Autoscaling on metrics not related to Kubernetes objects ### Autoscaling on metrics not related to Kubernetes objects
Applications running on Kubernetes may need to autoscale based on metrics that don't have an obvious Applications running on Kubernetes may need to autoscale based on metrics that don't have an obvious
@@ -312,12 +363,14 @@ relationship to any object in the Kubernetes cluster, such as metrics describing
no direct correlation to Kubernetes namespaces. In Kubernetes 1.10 and later, you can address this use case no direct correlation to Kubernetes namespaces. In Kubernetes 1.10 and later, you can address this use case
with *external metrics*. with *external metrics*.
Using external metrics requires a certain level of knowledge of your monitoring system, and it requires a cluster Using external metrics requires knowledge of your monitoring system; the setup is
monitoring setup similar to one required for using custom metrics. With external metrics, you can autoscale similar to that required when using custom metrics. External metrics allow you to autoscale your cluster
based on any metric available in your monitoring system by providing a `metricName` field in your based on any metric available in your monitoring system. Just provide a `metric` block with a
HorizontalPodAutoscaler manifest. Additionally you can use a `metricSelector` field to limit which `name` and `selector`, as above, and use the `External` metric type instead of `Object`.
metrics' time series you want to use for autoscaling. If multiple time series are matched by `metricSelector`, If multiple time series are matched by the `metricSelector`,
the sum of their values is used by the HorizontalPodAutoscaler. the sum of their values is used by the HorizontalPodAutoscaler.
External metrics support both the `Value` and `AverageValue` target types, which function exactly the same
as when you use the `Object` type.
For example if your application processes tasks from a hosted queue service, you could add the following For example if your application processes tasks from a hosted queue service, you could add the following
section to your HorizontalPodAutoscaler manifest to specify that you need one worker per 30 outstanding tasks. section to your HorizontalPodAutoscaler manifest to specify that you need one worker per 30 outstanding tasks.
@@ -325,20 +378,21 @@ section to your HorizontalPodAutoscaler manifest to specify that you need one wo
```yaml ```yaml
- type: External - type: External
external: external:
metricName: queue_messages_ready metric:
metricSelector: name: queue_messages_ready
matchLabels: selector: "queue=worker_tasks"
queue: worker_tasks target:
targetAverageValue: 30 type: AverageValue
averageValue: 30
``` ```
If your metric describes work or resources that can be divided between autoscaled pods the `targetAverageValue` When possible, it's preferrable to use the custom metric target types instead of external metrics, since it's
field describes how much of that work each pod can handle. Instead of using the `targetAverageValue` field, you could use the easier for cluster administrators to secure the custom metrics API. The external metrics API potentially allows
`targetValue` to define a desired value of your external metric. access to any metric, so cluster administrators should take care when exposing it.
## Appendix: Horizontal Pod Autoscaler Status Conditions ## Appendix: Horizontal Pod Autoscaler Status Conditions
When using the `autoscaling/v2beta1` form of the HorizontalPodAutoscaler, you will be able to see When using the `autoscaling/v2beta2` form of the HorizontalPodAutoscaler, you will be able to see
*status conditions* set by Kubernetes on the HorizontalPodAutoscaler. These status conditions indicate *status conditions* set by Kubernetes on the HorizontalPodAutoscaler. These status conditions indicate
whether or not the HorizontalPodAutoscaler is able to scale, and whether or not it is currently restricted whether or not the HorizontalPodAutoscaler is able to scale, and whether or not it is currently restricted
in any way. in any way.
content/en/docs/tasks/run-application/horizontal-pod-autoscale.md
+8 -5
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@@ -57,8 +57,11 @@ or the custom metrics API (for all other metrics).
* For per-pod custom metrics, the controller functions similarly to per-pod resource metrics, * For per-pod custom metrics, the controller functions similarly to per-pod resource metrics,
except that it works with raw values, not utilization values. except that it works with raw values, not utilization values.
* For object metrics, a single metric is fetched (which describes the object * For object metrics and external metrics, a single metric is fetched, which describes
in question), and compared to the target value, to produce a ratio as above. the object in question. This metric is compared compared to the target
value, to produce a ratio as above. In the `autoscaling/v2beta2` API
version, this value can optionally be divided by the number of pods before the
comparison is made.
The HorizontalPodAutoscaler normally fetches metrics from a series of aggregated APIs (`metrics.k8s.io`, The HorizontalPodAutoscaler normally fetches metrics from a series of aggregated APIs (`metrics.k8s.io`,
`custom.metrics.k8s.io`, and `external.metrics.k8s.io`). The `metrics.k8s.io` API is usually provided by `custom.metrics.k8s.io`, and `external.metrics.k8s.io`). The `metrics.k8s.io` API is usually provided by
@@ -85,7 +88,7 @@ The current stable version, which only includes support for CPU autoscaling,
can be found in the `autoscaling/v1` API version. can be found in the `autoscaling/v1` API version.
The beta version, which includes support for scaling on memory and custom metrics, The beta version, which includes support for scaling on memory and custom metrics,
can be found in `autoscaling/v2beta1`. The new fields introduced in `autoscaling/v2beta1` can be found in `autoscaling/v2beta2`. The new fields introduced in `autoscaling/v2beta2`
are preserved as annotations when working with `autoscaling/v1`. are preserved as annotations when working with `autoscaling/v1`.
More details about the API object can be found at More details about the API object can be found at
@@ -146,7 +149,7 @@ may keep thrashing as usual.
## Support for multiple metrics ## Support for multiple metrics
Kubernetes 1.6 adds support for scaling based on multiple metrics. You can use the `autoscaling/v2beta1` API Kubernetes 1.6 adds support for scaling based on multiple metrics. You can use the `autoscaling/v2beta2` API
version to specify multiple metrics for the Horizontal Pod Autoscaler to scale on. Then, the Horizontal Pod version to specify multiple metrics for the Horizontal Pod Autoscaler to scale on. Then, the Horizontal Pod
Autoscaler controller will evaluate each metric, and propose a new scale based on that metric. The largest of the Autoscaler controller will evaluate each metric, and propose a new scale based on that metric. The largest of the
proposed scales will be used as the new scale. proposed scales will be used as the new scale.
@@ -159,7 +162,7 @@ custom metrics is still available, these metrics will not be available for use b
annotations for specifying which custom metrics to scale on are no longer honored by the Horizontal Pod Autoscaler controller. annotations for specifying which custom metrics to scale on are no longer honored by the Horizontal Pod Autoscaler controller.
Kubernetes 1.6 adds support for making use of custom metrics in the Horizontal Pod Autoscaler. Kubernetes 1.6 adds support for making use of custom metrics in the Horizontal Pod Autoscaler.
You can add custom metrics for the Horizontal Pod Autoscaler to use in the `autoscaling/v2beta1` API. You can add custom metrics for the Horizontal Pod Autoscaler to use in the `autoscaling/v2beta2` API.
Kubernetes then queries the new custom metrics API to fetch the values of the appropriate custom metrics. Kubernetes then queries the new custom metrics API to fetch the values of the appropriate custom metrics.
See [Support for metrics APIs](#support-for-metrics-APIs) for the requirements. See [Support for metrics APIs](#support-for-metrics-APIs) for the requirements.