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Understanding Horizontal Pod Autoscaler Custom Metrics

Understanding Horizontal Pod Autoscaler Custom Metrics

Horizontal Pod Autoscaler (HPA) is a Kubernetes feature that allows for the automatic scaling of pods based on metrics like CPU utilization or memory usage. However, sometimes you might need to scale based on other custom metrics specific to your application. That's where HPA custom metrics come in. In this article, we will explore what HPA custom metrics are and how to use them effectively.

Understanding HPA Custom Metrics

HPA custom metrics are metrics that are specific to your application and are not provided by default in Kubernetes. These metrics can be anything that you want to use to scale your pods, such as the number of requests per second or the length of a queue.

In order to use HPA custom metrics, you will need to have a metric server set up in your Kubernetes cluster. This server collects metrics from various sources and makes them available to the HPA controller. Once you have your metric server set up, you can start using HPA custom metrics.

Using HPA Custom Metrics

To use HPA custom metrics, you will need to follow these steps:

  1. Define your custom metric: The first step is to define your custom metric. This can be done using Prometheus or any other monitoring tool that supports custom metrics in Kubernetes.

  2. Expose the metric to Kubernetes: Once you have defined your custom metric, you will need to expose it to Kubernetes. This is typically done by creating a service that exposes the metric using the Prometheus format.

  3. Configure the HPA: Now that your custom metric is exposed to Kubernetes, you can configure the HPA to use it for scaling. This is done by specifying the metric name and target value in the HPA manifest.

For example, let's say you want to scale your pods based on the number of requests per second. You would define this metric in Prometheus, expose it using a service, and then configure the HPA to use it like this:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: my-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: my-app
metrics:
- type: Object
object:
metricName: requests-per-second
target:
type: Value
value: "100"

In this example, the HPA is configured to scale the deployment named 'my-app' based on the custom metric named 'requests-per-second'. The target value is set to 100 requests per second.

More Examples

Here are a few more examples of custom metrics that you might use with HPA:

  • Queue length: If your application uses a queue, you might want to scale based on the length of the queue. You can define this metric in Prometheus and then expose it to Kubernetes using a service.
  • Error rate: If your application has a high error rate, you might want to scale up to handle the increased load. You can define this metric in Prometheus and then expose it to Kubernetes using a service.
  • Custom business metric: If you have a custom metric that is specific to your business, you can define it in Prometheus and then expose it to Kubernetes using a service.

HPA custom metrics allow you to scale your pods based on metrics that are specific to your application. By defining custom metrics, exposing them to Kubernetes, and configuring the HPA to use them, you can ensure that your application is always scaled to handle the load. So, the next time you need to scale based on a custom metric, be sure to give HPA custom metrics a try!

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  • That's it for this post. Keep practicing and have fun. Leave your comments if any.

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