Updated April 15, 2023
Definition of Kubernetes HPA
Kubernetes HPA is defined as, the HPA stands for Horizontal Pod Autoscaler which can automatically convert the state of our Kubernetes loads by automatically converting the number of pods in duplication, implementation, the duplication set is based on CPU supporting metrics or in reply to custom metrics from Kubernetes or outside of metrics from origins which are from outside of the cluster, the Kubernetes is portable, extensible, and open-source system developed by Google and now supported by the Cloud-native computing foundation, and used for automatic implementation, scaling and supporting the stack applications, we can test HPA in Kubernetes through command.
What is Kubernetes HPA?
As we see above the HPA is the number of a pod in the workload of CPU that can automatically convert the shape of our Kubernetes workload, so when we utilize our task to a Kubernetes cluster then we may not be assured about the assets essentials, and also not aware about how those assets are converted, the HPA aid to make sure that the workload of the functions is constant in the various state which allows managing the extra capacity as per the requirement.
The HPA can automatically convert the number of pods in our Kubernetes workload and that is based on some metrics that are the actual resource usage which can provide the detailed value of request which can be given to the pod’s CPU, custom metrics is can describe by cluster object which is in Kubernetes, and external metrics provides the service to the outside of our cluster.
When there are many requests in the pipeline then our workload may require extra CPU, at that time we have to create the extra metric as per the size of the queue, and then configure the HPA to expand the pods automatically but when the size decreases then queue size also may reduce.
How to use kubernetes HPA?
The Kubernetes has an auto-scaling feature so that it can scale the pods and also it can auto-scale the infrastructure horizontally which can be carried off by the assets and HPA is the assets or resource of a Kubernetes, for implementation of the basic unit the Kubernetes, make use of pods and HPA cannot take any help to convert the pods in duplication, implementation as that all depends on the metrics.
The HPA is applied to manage the loop in a periodic manner, so in every period the manager can manage the queries while using the metrics and that also acquire the metrics either from the origin metric or from other metrics.
For every pod, the origin metrics such as CPU, the controller bring the metrics from the API of origin metrics for every pod which is given by HPA, if the presence exertion value is set then the controller can evaluate the request value for every pod. And if the target raw value is set then raw metrics can use it directly, then the controller can search out the mean value of targeted pod will get and the ratio of it can be used to convert the number of preferred duplication.
Getting started kubernetes HPA
Before we started with Kubernetes HPA first we have to make sure that we have Google Kubernetes engine API, and then we also have to install the Cloud SDK and for setting up it we have to follow some steps that are, we have to set ‘gcloud’ as a default, and then we can use ‘gcloud init’ if we wanted to go through the settings and then we can use ‘gcloud config’ for setting the id, zone, and region.
When we are trying to use the console of Google cloud then the ‘HorizontalAutoScaler’ objects are created with the help of API, when we use ‘kubect1’ to see the information about HPA and we can also define the API of auto-scaling version, in which the ‘apiversion1’ is default version which allows us to auto-scale the metrics as per the utilization in CPU and the ‘apiversion2’ been generated by an object of an HPA.
Best practices kubernetes HPA
- Ensure all pods have resource requests specified:
The CPU utilization is a part of the Kubernetes controller and due to that the HPA can take scaling decision, in which the utilization value can be deliberated in the percentage of the resource request to the individual pods, hence the best practice is to make sure that the request value is been determined for all containers of every pod which is the part of the Kubernetes controller that has been scaled by HPA.
- To install metrics server:
For every pod, the resource metrics can take a decision separately and it restores the API metrics which can be provided by the metrics server so the best practice is to begin the metrics server in our Kubernetes cluster.
- To configure the external metrics:
With the help of the external metrics they can also take the conclusion of scaling, it has two class that are pod metrics and object metrics, the pod metrics which can help to assist the target value which can be determined by target average value, the best practice is to make sure the right target value type is to be used for pod metrics and object metrics.
- Custom metrics over external metrics:
There may have two options to select the best practice is to select the custom metric, because the API of the external metrics can gain many attempts to fix it in contrast with custom metrics.
- Configure cool-down period:
The HPA can also measure the vital complexion of the metrics, which leads to beating where the number of duplication differ and it is not advantageous.
Conclusion
In this article, we conclude that the Kubernetes has HPA to which it can make sure that every pod has resource request and we need to install the metrics, we can have a choice for selecting metrics which have discussed above, and also we have seen how to get started the Kubernetes HPA.
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