In the context of the Certified Kubernetes Administrator (CKA) exam, configuring workload autoscaling primarily revolves on the Horizontal Pod Autoscaler (HPA). The HPA automatically scales the number of Pods in a Deployment, ReplicaSet, or StatefulSet based on observed CPU utilization or memory us…In the context of the Certified Kubernetes Administrator (CKA) exam, configuring workload autoscaling primarily revolves on the Horizontal Pod Autoscaler (HPA). The HPA automatically scales the number of Pods in a Deployment, ReplicaSet, or StatefulSet based on observed CPU utilization or memory usage.
For autoscaling to function, the **Metrics Server** must be installed in the cluster. This component aggregates resource usage data; without it, the HPA cannot retrieve current metrics and will display targets as `<unknown>`.
Configuring HPA involves three essential requirements:
1. **Pod Specifications:** Containers within your Pods must have resource **requests** and **limits** defined. The HPA uses the request value to calculate utilization percentages.
2. **HPA Resource:** You must create an HPA object that targets a specific workload. This defines the minimum and maximum number of replicas and the target metric (e.g., maintain 50% CPU utilization).
3. **Control Loop:** The HPA controller periodically queries the Metrics Server. It calculates the desired replica count using the ratio of current usage to the target usage. If the load exceeds the target, it scales out (adds pods); if the load drops, it scales in (removes pods).
You can configure this imperatively using: `kubectl autoscale deployment <name> --cpu-percent=50 --min=1 --max=10`. Alternatively, using a YAML manifest (typically `apiVersion: autoscaling/v2`) allows for granular control, such as defining stabilization windows to prevent 'thrashing' (rapid fluctuation of replica counts). Mastery of HPA troubleshooting—specifically ensuring metrics are available and resource requests are set—is critical for the CKA.
Concept: Configure Workload Autoscaling (HPA)
What is Workload Autoscaling? In the context of the Certified Kubernetes Administrator (CKA) exam, workload autoscaling refers to the Horizontal Pod Autoscaler (HPA). It automatically updates a workload resource (such as a Deployment or StatefulSet), with the aim of automatically scaling the workload to match demand. It adjusts the number of replicas based on observed CPU utilization or other select metrics.
Why is it Important? Autoscaling is vital for self-healing and efficiency. It ensures your application can handle traffic spikes (high availability) without manual intervention and saves resources/money by scaling down during periods of low activity.
How it Works The HPA controller, running within the Kubernetes Control Plane, periodically monitors the metrics of target Pods. 1. Metrics Retrieval: It queries the Metrics Server (which must be installed) for resource usage (like CPU or Memory). 2. Calculation: It compares the current metric value against the desired target value specified in the HPA configuration. 3. Action: It calculates the required number of replicas to meet the target and updates the replicas field in the Deployment or ReplicaSet.
How to Configure and Answer Exam Questions Step 1: Check Prerequisites Before creating an HPA, ensure the Metrics Server is running by checking if kubectl top pods returns data. Also, crucially, ensure the target Deployment's Pods have resources.requests defined in their YAML. Without CPU requests, the HPA cannot determine utilization percentage.
Step 2: Use Imperative Commands The fastest way to answer CKA questions is using the CLI. If asked to scale a deployment named 'web-app' based on 50% CPU usage: kubectl autoscale deployment web-app --cpu-percent=50 --min=1 --max=10
Step 3: Verification Run kubectl get hpa. You will see columns for TARGETS, MINPODS, MAXPODS, and REPLICAS.
Exam Tips: Answering Questions on Configure Workload Autoscaling Tip 1: Troubleshooting <unknown> Targets. If you run kubectl get hpa and see <unknown>/50% under the TARGETS column, do not panic. First, wait 15-30 seconds for the cycle to run. If it persists, the issue is almost always that the Pods in the Deployment do not have resource requests defined for CPU. Tip 2: Editing Existing HPA. If a question asks you to update the maximum number of replicas for an existing HPA, simply use: kubectl scale creates a static scale, but for HPA settings, use kubectl edit hpa <hpa-name> and modify the spec. Tip 3: Do not write YAML from scratch. Always use kubectl autoscale to generate the resource, or use kubectl create deployment ... --dry-run=client -o yaml to ensure requests are set on the application before applying the autoscaler.