In the context of the Certified Kubernetes Administrator (CKA) exam and troubleshooting, monitoring resource usage is fundamental to maintaining cluster health and application performance. Kubernetes does not provide a native storage solution for historical metrics by default; instead, it relies on…In the context of the Certified Kubernetes Administrator (CKA) exam and troubleshooting, monitoring resource usage is fundamental to maintaining cluster health and application performance. Kubernetes does not provide a native storage solution for historical metrics by default; instead, it relies on the Metrics Server, a cluster-wide aggregator of resource usage data. The Metrics Server collects metrics such as CPU and memory usage efficiently from the Summary API exposed by the Kubelet on each node.
For an administrator, the primary interface for accessing this data is the `kubectl top` command. You use `kubectl top nodes` to analyze the capacity and utilization of the underlying infrastructure, identifying if nodes are overcommitted or under pressure. Similarly, `kubectl top pods` allows you to inspect application-level consumption. This command is versatile, supporting flags to sort results (e.g., `--sort-by=cpu` or `--sort-by=memory`), filter by selector labels, or check specific namespaces, which is vital for quickly identifying 'noisy neighbor' containers that are causing resource contention.
Crucially, this monitoring pipeline is not just for observation; it drives automation. The Horizontal Pod Autoscaler (HPA) queries the Metrics Server API to determine when to scale the number of pod replicas up or down based on defined utilization thresholds. Without a functional Metrics Server, HPA cannot operate.
From a troubleshooting perspective, monitoring usage is key to diagnosing issues like `OOMKilled` (Out of Memory) errors, where a container exceeds its memory limit, or CPU throttling, which degrades performance. While the CKA focuses on these immediate, in-memory metrics, production environments typically integrate robust tools like Prometheus and Grafana for long-term data retention, historical trend analysis, and complex alerting.
Monitor Cluster and Application Resource Usage
Why is it Important? Monitoring cluster and application resources is a cornerstone of Kubernetes administration. Without visibility into how much CPU and Memory your Nodes and Pods are consuming, you cannot effectively troubleshoot performance issues, diagnose application crashes (such as OOMKilled errors), or make informed decisions about capacity planning. Furthermore, features like the Horizontal Pod Autoscaler (HPA) rely entirely on these metrics to function correctly. In the CKA exam, demonstrating the ability to quickly retrieve these stats is proof of your ability to assess cluster health.
What is it? In the context of the CKA curriculum, monitoring refers primarily to the collection and retrieval of resource utilization metrics (CPU and RAM) from the cluster components. Unlike full-stack observability solutions (like Prometheus/Grafana) which store historical data, the native Kubernetes monitoring needed for the exam focuses on the Metrics Server. The Metrics Server aggregates resource usage data across the cluster and exposes it via the Kubernetes API server, allowing users to query current usage snapshot via the CLI.
How it works? The process relies on a pipeline of components: 1. cAdvisor: Embedded inside the Kubelet on every node, cAdvisor collects container metrics. 2. Metrics Server: This is a cluster-wide aggregator. It scrapes the Kubelets for resource metrics. 3. Metrics API: The Metrics Server registers itself with the main API Server, exposing the /apis/metrics.k8s.io/ endpoint. 4. kubectl top: When you run this command, the CLI queries the API server, which retrieves the data from the Metrics Server to display current usage.
How to Answer Questions on Resource Usage 1. Verify the Metrics Server: Before running commands, ensure the metrics API is available. In most exam environments, it is pre-installed. You can check by listing pods in the kube-system namespace. 2. Use kubectl top: This is your primary tool. You will be asked to identify nodes with high CPU load or pods consuming the most memory. - To check Nodes: kubectl top node - To check Pods: kubectl top pod 3. Identify the Resource: Read the question carefully to see if they want CPU (cores/millicores) or Memory (bytes/mebibytes). 4. Sort the Output: Often, you need to find the 'highest' consumer. Use the sort flag to avoid manual errors. - Example: kubectl top pod --sort-by=cpu - Example: kubectl top node --sort-by=memory 5. Store the Result: Questions often ask you to write the name of the resource to a specific file (e.g., /opt/high_cpu_pod.txt). Ensure you copy the exact name.
Exam Tips: Answering Questions on Monitor cluster and application resource usage Tip 1: Know your units. CPU is often measured in millicores (e.g., 100m) and memory in Mi (Mebibytes). Do not confuse these. Tip 2: Filtering by Namespace. If asked to find the heaviest pod in a specific namespace, verify you add -n <namespace>. If asked for all namespaces, use -A. Tip 3: Container Metrics. If a Pod has multiple containers (sidecars), a standard kubectl top pod gives the sum of all containers. If the question asks for usage of a specific container, append --containers to see the breakdown. Tip 4: Troubleshooting 'Metrics API not available'. If kubectl top fails, checking the logs of the Metrics Server pod in kube-system is a valid troubleshooting step, though rare in the standard CKA tasks unless explicitly asked to fix it.