Autoscaling Java apps on Kubernetes based on metrics

02 Oct 2023

In today’s dynamic and scalable world, it is essential to have an efficient autoscaling mechanism for your Java applications running on Kubernetes. Autoscaling allows you to automatically adjust the number of replicas of your application based on metrics like CPU usage or request latency, ensuring optimal performance and resource utilization.

In this blog post, we will explore how to implement autoscaling for your Java apps on Kubernetes, using the Horizontal Pod Autoscaler (HPA) feature.

What is Horizontal Pod Autoscaler (HPA)?

The Horizontal Pod Autoscaler is a Kubernetes feature that automatically scales the number of pods in a deployment based on observed CPU utilization or custom metrics. It helps you optimize resource allocation and maintain the desired performance level.

Pre-requisites

Before we dive into the implementation, make sure you have the following pre-requisites in place:

A Kubernetes cluster up and running.
A Java application containerized as a Docker image.
Kubernetes Metrics Server deployed on your cluster.

Step 1: Deploying Metrics Server

To enable autoscaling based on metrics, we need to deploy the Kubernetes Metrics Server on our cluster. The Metrics Server collects resource metrics from the cluster and makes them available to the Horizontal Pod Autoscaler.

Run the following command to deploy the Metrics Server:

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

Step 2: Creating Autoscaling Configuration

Now that we have the Metrics Server deployed, we can define the autoscaling configuration for our Java application.

Create a file named hpa.yaml and add the following contents:

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-autoscaler
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app-deployment
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

In the above configuration, we are defining an autoscaler named my-app-autoscaler that scales the my-app-deployment deployment. The minimum number of replicas is set to 1, and the maximum is set to 10.

The autoscaler is configured to scale based on CPU utilization. It maintains an average CPU utilization of 50%.

Adjust the configuration as per your application’s requirements.

Step 3: Deploying the Autoscaler

Apply the autoscaling configuration by running the following command:

kubectl apply -f hpa.yaml

The Horizontal Pod Autoscaler will now create and manage the required replicas based on the defined metrics.

Step 4: Testing Autoscaling

To verify the autoscaling functionality, you can generate load on your Java application and analyze the scaling behavior.

You can use tools like Apache JMeter or Kubernetes Horizontal Pod Autoscaler Scalerunner to simulate load and monitor the autoscaling in action.

Conclusion

Autoscaling Java applications on Kubernetes based on metrics provides an efficient way to ensure optimal resource utilization and performance. With the Horizontal Pod Autoscaler, you can easily scale your Java apps based on CPU utilization or custom metrics.

By following the steps outlined in this article, you will be able to implement autoscaling for your Java apps on Kubernetes and take advantage of the dynamic and scalable nature of the platform.

#autoscaling #Kubernetes #Java #metrics