Understanding Kubernetes Autoscaling Custom Metrics

Kubernetes is a popular open-source container orchestration tool that helps in managing containerized applications. It provides several features to automate and simplify application deployment, scaling, and management.

One of the essential features of Kubernetes is autoscaling, which automatically adjusts the number of replicas of a deployment based on the application's resource utilization. Kubernetes supports several types of autoscaling, including horizontal pod autoscaling (HPA), vertical pod autoscaling (VPA), and cluster autoscaling.

In this article, we will focus on Kubernetes autoscaling with custom metrics.

Custom Metrics in Kubernetes

Kubernetes allows users to define custom metrics based on their application's specific needs. Custom metrics are useful when the default metrics provided by Kubernetes are not sufficient for monitoring the application's resource utilization. Custom metrics can be defined using various data sources such as Prometheus, Stackdriver, Datadog, and others.

Kubernetes supports custom metrics-based autoscaling, which allows users to scale their deployments based on custom metrics. The custom metrics-based autoscaling is implemented using the Kubernetes Metrics API, which provides a standard interface for collecting and querying custom metrics.

Configuring Kubernetes Autoscaling with Custom Metrics

In this section, we will see how to configure Kubernetes autoscaling with custom metrics. We will use Prometheus as the data source for custom metrics and create a custom metric called "request_per_second" that measures the number of requests per second received by the application.

Step 1: Deploy Prometheus

The first step is to deploy Prometheus, a popular open-source monitoring and alerting system that can scrape metrics from different data sources. Prometheus can be deployed as a Kubernetes deployment using the following command:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/nginx-0.27.1/deploy/prometheus/00-namespace.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/nginx-0.27.1/deploy/prometheus/configmap.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/nginx-0.27.1/deploy/prometheus/prometheus.yaml

Step 2: Create a Custom Metrics API Server

The next step is to create a Custom Metrics API Server, which exposes the custom metrics to Kubernetes. The Custom Metrics API Server can be created using the following command:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/nginx-0.27.1/deploy/prometheus/custom-metrics-apiserver.yaml

Step 3: Create a Custom Metrics Adapter

The Custom Metrics Adapter is responsible for retrieving the custom metrics from the data source and exposing them to the Custom Metrics API Server. In this case, we will use the Prometheus Custom Metrics Adapter, which can be deployed using the following command:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/nginx-0.27.1/deploy/prometheus/custom-metrics-adapter.yaml

Step 4: Define the Custom Metric

Now that we have set up the Custom Metrics API Server and the Custom Metrics Adapter, we can define our custom metric. We will create a custom metric called "request_per_second" that measures the number of requests per second received by the application. The custom metric can be defined using the following command:

apiVersion: custom.metrics.k8s.io/v1beta1
kind: MetricValueList
metadata:
  name: request-per-second
spec:
  metricName: request_per_second
  timestamp: "2023-04-12T12:00:00Z"
  value: "100"

Step 5: Configure Autoscaling

Finally, we can configure autoscaling based on the custom metric.

Related Searches and Questions asked:

Kubernetes Autoscaling Commands

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Understanding Kubernetes Autoscaling Nodes

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