https://github.com/banzaicloud/hpa-operator
Horizontal Pod Autoscaler operator for Kubernetes. Annotate and let the HPA operator do the rest.
https://github.com/banzaicloud/hpa-operator
Last synced: about 1 month ago
JSON representation
Horizontal Pod Autoscaler operator for Kubernetes. Annotate and let the HPA operator do the rest.
- Host: GitHub
- URL: https://github.com/banzaicloud/hpa-operator
- Owner: banzaicloud
- License: apache-2.0
- Archived: true
- Created: 2018-07-20T15:22:31.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-06-28T14:04:11.000Z (almost 4 years ago)
- Last Synced: 2025-03-05T13:02:34.004Z (about 2 months ago)
- Language: Go
- Homepage:
- Size: 39.6 MB
- Stars: 238
- Watchers: 14
- Forks: 31
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-repositories - banzaicloud/hpa-operator - Horizontal Pod Autoscaler operator for Kubernetes. Annotate and let the HPA operator do the rest. (Go)
README
> # ⚠️ DEPRECATION NOTICE ⚠️
> This operator and the helm chart are deprecated and no longer actively maintained.
# Horizontal Pod Autoscaler operator
You may not want nor can edit a Helm chart just to add an autoscaling feature. Nearly all charts supports **custom annotations** so we believe that it would be a good idea to be able to setup autoscaling just by adding some simple annotations to your deployment.
We have open sourced a [Horizontal Pod Autoscaler operator](https://github.com/banzaicloud/hpa-operator). This operator watches for your `Deployment` or `StatefulSet` and automatically creates an *HorizontalPodAutoscaler* resource, should you provide the correct autoscale annotations.
- [Horizontal Pod Autoscaler operator](https://github.com/banzaicloud/hpa-operator)
- [Horizontal Pod Autoscaler operator Helm chart](https://github.com/banzaicloud/hpa-operator/tree/master/deploy/charts/hpa-operator)
## Autoscale by annotations
Autoscale annotations can be placed:
- directly on Deployment / StatefulSet:
```
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: example
labels:
annotations:
hpa.autoscaling.banzaicloud.io/minReplicas: "1"
hpa.autoscaling.banzaicloud.io/maxReplicas: "3"
cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "70"
```
- or on `spec.template.metadata.annotations`:
```
apiVersion: extensions/v1beta1
kind: Deployment
...
spec:
replicas: 3
template:
metadata:
labels:
...
annotations:
hpa.autoscaling.banzaicloud.io/minReplicas: "1"
hpa.autoscaling.banzaicloud.io/maxReplicas: "3"
cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "70"
```
The [Horizontal Pod Autoscaler operator](https://github.com/banzaicloud/hpa-operator) takes care of creating, deleting, updating HPA, with other words keeping in sync with your deployment annotations.
## Annotations explained
All annotations must contain the `autoscaling.banzaicloud.io` prefix. It is required to specify minReplicas/maxReplicas and at least one metric to be used for autoscale. You can add *Resource* type metrics for cpu & memory and *Pods* type metrics.
Let's see what kind of annotations can be used to specify metrics:
- ``cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "{targetAverageUtilizationPercentage}"`` - adds a Resource type metric for cpu with targetAverageUtilizationPercentage set as specified, where targetAverageUtilizationPercentage should be an int value between [1-100]
- ``cpu.hpa.autoscaling.banzaicloud.io/targetAverageValue: "{targetAverageValue}"`` - adds a Resource type metric for cpu with targetAverageValue set as specified, where targetAverageValue is a [Quantity](https://godoc.org/k8s.io/apimachinery/pkg/api/resource#Quantity).
- ``memory.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "{targetAverageUtilizationPercentage}"`` - adds a Resource type metric for memory with targetAverageUtilizationPercentage set as specified, where targetAverageUtilizationPercentage should be an int value between [1-100]
- ``memory.hpa.autoscaling.banzaicloud.io/targetAverageValue: "{targetAverageValue}"`` - adds a Resource type metric for memory with targetAverageValue set as specified, where targetAverageValue is a [Quantity](https://godoc.org/k8s.io/apimachinery/pkg/api/resource#Quantity).
- ``pod.hpa.autoscaling.banzaicloud.io/customMetricName: "{targetAverageValue}"`` - adds a Pods type metric with targetAverageValue set as specified, where targetAverageValue is a [Quantity](https://godoc.org/k8s.io/apimachinery/pkg/api/resource#Quantity).
> To use custom metrics from *Prometheus*, you have to deploy `Prometheus Adapter` and `Metrics Server`, explained in detail in our previous post about [using HPA with custom metrics](https://banzaicloud.com/blog/k8s-horizontal-pod-autoscaler/)
### Custom metrics from version 0.1.5
From version 0.1.5 we have removed support for *Pod* type custom metrics and added support for Prometheus backed custom metrics exposed by [Kube Metrics Adapter](https://github.com/zalando-incubator/kube-metrics-adapter).
To setup HPA based on Prometheus one has to setup the following deployment annotations:
``
prometheus.customMetricName.hpa.autoscaling.banzaicloud.io/query: "sum({kubernetes_pod_name=~"^YOUR_POD_NAME.*",__name__=~"YOUR_PROMETHUES_METRICNAME"})"
prometheus.customMetricName.hpa.autoscaling.banzaicloud.io/targetValue: "{targetValue}"
prometheus.customMetricName.hpa.autoscaling.banzaicloud.io/targetAverageValue: "{targetAverageValue}"
``
The query should be a syntactically correct Prometheus query. Pay attention to select only metrics related to your *Deployment* / *Pod* / *Service*.
You should specify either targetValue or targetAverageValue, in which case metric value is averaged with current replica count.
## Quick usage example
Let's pick **Kafka** as an example chart, from our curated list of [Banzai Cloud Helm charts](https://github.com/banzaicloud/banzai-charts/tree/master/kafka). The Kafka chart by default doesn't contains any HPA resources, however it allows specifying Pod annotations as params so it's a good example to start with. Now let's see how you can add a simple cpu based autoscale rule for Kafka brokers by adding some simple annotations:
1. Deploy operator
```
helm repo add banzaicloud-stable https://kubernetes-charts.banzaicloud.com
helm install hpa-operator banzaicloud-stable/hpa-operator
```
2. Deploy Kafka chart, with autoscale annotations
```
cat > values.yaml <