How to configure Kubernetes HorizontalPodAutoscaler based on different metrics?

Introduction

Kubernetes, the popular container orchestration platform, provides a powerful feature called HorizontalPodAutoscaler (HPA) that allows you to automatically scale your application's pods based on various metrics. In this tutorial, we will explore how to configure the Kubernetes HPA to scale your pods based on CPU utilization and custom metrics, ensuring your application can handle varying workloads efficiently.

Skills Graph

Understanding Kubernetes HorizontalPodAutoscaler

Kubernetes HorizontalPodAutoscaler (HPA) is a built-in feature that automatically scales the number of pods in a deployment or replica set based on observed CPU utilization or other custom metrics. This allows your application to handle fluctuating workloads without the need for manual intervention.

What is Kubernetes HorizontalPodAutoscaler?

Kubernetes HorizontalPodAutoscaler is a control loop that monitors the performance metrics of your application and automatically adjusts the number of replicas to maintain a target level of resource utilization. This helps ensure that your application can handle increased traffic or workload without overloading the available resources.

Key Features of HorizontalPodAutoscaler

• CPU Utilization Scaling: HPA can scale your application based on the average CPU utilization of the pods in your deployment or replica set.
• Custom Metric Scaling: HPA can also scale your application based on custom metrics, such as the number of requests per second or the length of a message queue.
• Scalability: HPA can automatically scale your application up or down based on the current workload, ensuring that your application can handle fluctuations in demand.
• Ease of Use: HPA is a built-in feature of Kubernetes, making it easy to configure and use without the need for additional tools or services.

Configuring HorizontalPodAutoscaler

To configure HorizontalPodAutoscaler, you can use the kubectl command-line tool or the Kubernetes API. The configuration includes setting the target metric, the desired utilization level, and the minimum and maximum number of replicas.

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: example-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: example-deployment
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50

In this example, the HorizontalPodAutoscaler is configured to scale the example-deployment deployment based on the average CPU utilization of the pods. The target average CPU utilization is set to 50%, and the minimum and maximum number of replicas are set to 2 and 10, respectively.

Scaling Pods Based on CPU Utilization

Scaling pods based on CPU utilization is one of the most common use cases for Kubernetes HorizontalPodAutoscaler (HPA). This approach allows your application to automatically scale up or down based on the current CPU usage of your pods.

Configuring HPA for CPU Utilization Scaling

To configure HPA for CPU utilization scaling, you need to specify the Resource metric type and the cpu resource name in the HPA configuration. Here's an example:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: example-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: example-deployment
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50

In this example, the HPA is configured to scale the example-deployment deployment based on the average CPU utilization of the pods. The target average CPU utilization is set to 50%, and the minimum and maximum number of replicas are set to 2 and 10, respectively.

How HPA Scales Pods Based on CPU Utilization

The Kubernetes HPA controller periodically checks the average CPU utilization of the pods in the target deployment or replica set. If the average CPU utilization exceeds the target value (in this case, 50%), the HPA will scale up the number of replicas to bring the CPU utilization back to the target level. Conversely, if the average CPU utilization drops below the target value, the HPA will scale down the number of replicas.

The scaling process is based on the following formula:

desired_replicas = ceil(current_replicas * (current_cpu_utilization / target_cpu_utilization))

This formula ensures that the number of replicas is adjusted proportionally to the current CPU utilization, with the goal of maintaining the target CPU utilization level.

Monitoring and Troubleshooting HPA

You can monitor the status of your HPA using the kubectl get hpa command. This will show you the current number of replicas, the target CPU utilization, and the actual CPU utilization.

If you encounter any issues with your HPA, you can check the logs of the Kubernetes controller manager to investigate the root cause. You can also use Kubernetes events to get more information about the scaling actions performed by the HPA.

Scaling Pods Based on Custom Metrics

While scaling based on CPU utilization is a common use case, Kubernetes HorizontalPodAutoscaler (HPA) also supports scaling based on custom metrics. This allows you to scale your application based on metrics that are specific to your use case, such as the number of requests per second or the length of a message queue.

Configuring HPA for Custom Metric Scaling

To configure HPA for custom metric scaling, you need to specify the Metric type and provide the details of the custom metric you want to use. Here's an example:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: example-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: example-deployment
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metricName: requests-per-second
      targetAverageValue: 100

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

Implementing Custom Metrics

To use custom metrics with HPA, you need to have a way to expose these metrics to the Kubernetes monitoring system. This can be done by using a custom metrics adapter, such as the Prometheus Adapter or the LabEx Metrics Server.

Here's an example of how you can use the LabEx Metrics Server to expose a custom metric:

apiVersion: labex.io/v1alpha1
kind: MetricServer
metadata:
  name: example-metric-server
spec:
  containers:
  - name: metric-server
    image: labex/metric-server:latest
    command:
    - /metric-server
    - --metric-name=requests-per-second
    - --metric-value-function=get_requests_per_second

In this example, the LabEx Metrics Server is configured to expose a custom metric called requests-per-second. The get_requests_per_second function is responsible for calculating the metric value, which can be based on any data source or business logic.

Once the custom metric is exposed, you can use it in the HPA configuration as shown in the previous example.

Monitoring and Troubleshooting Custom Metric Scaling

You can monitor the status of your HPA using the kubectl get hpa command, which will show you the current number of replicas and the values of the custom metrics being used for scaling.

If you encounter any issues with your custom metric scaling, you can check the logs of the Kubernetes controller manager and the custom metrics adapter (e.g., LabEx Metrics Server) to investigate the root cause. You can also use Kubernetes events to get more information about the scaling actions performed by the HPA.

Summary

By the end of this tutorial, you will have a comprehensive understanding of how to configure the Kubernetes HorizontalPodAutoscaler to scale your application's pods based on different metrics, such as CPU utilization and custom metrics. This knowledge will help you optimize your Kubernetes deployments and ensure your application can handle varying workloads effectively.