> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Monitoring
> This article teaches how to monitor Kubernetes clusters, focusing on resource consumption and performance using the Metrics Server.
Welcome to this lesson on monitoring Kubernetes clusters. In this article, you will learn how to track resource consumption and performance in your Kubernetes environment.
## Why Monitor Kubernetes?
Monitoring in Kubernetes is essential for maintaining your cluster’s health and performance. Key metrics to watch include:
* **Node-Level Metrics:** Total nodes, their health status, and overall performance.
* **Performance Metrics:** CPU, memory, network, and disk usage.
* **Pod-Level Metrics:** Number of pods and individual pod performance (e.g., CPU and memory consumption).
Implementing a monitoring solution enables you to collect, aggregate, and analyze these metrics efficiently. Although Kubernetes does not include a full-featured built-in monitoring system, several open-source options—such as Metrics Server, Prometheus, and Elastic Stack—as well as proprietary solutions like Datadog and Dynatrace are available. In this lesson, we'll focus on the Metrics Server. For more advanced monitoring techniques, see the [Kubernetes Administration: Package Management with Glasskube](https://learn.kodekloud.com/user/courses/k8s-administration-package-management-with-glasskube) course.
The Metrics Server is a lightweight, in-memory monitoring solution, ideal for real-time metrics collection. However, it does not persist historical data. For long-term data analysis, consider advanced monitoring systems.
## Historical Perspective
Originally, the Hipster project provided monitoring and analysis features for Kubernetes. Even though you might encounter references to Hipster in online resources, it has been deprecated in favor of its streamlined counterpart, the Metrics Server. Remember, you can deploy only one Metrics Server per cluster.
## How Metrics are Collected
Each node in your Kubernetes cluster runs a kubelet, which manages pods and communicates with the Kubernetes API master server. A subcomponent of the kubelet, known as cAdvisor (container advisor), collects performance data from pods and makes it available to the Metrics Server via the kubelet API.
## Deploying the Metrics Server
### For Minikube Users
If you’re running a local cluster with Minikube, you can enable the Metrics Server with this simple command:
```bash theme={null}
minikube addons enable metrics-server
```
### For Other Environments
To deploy the Metrics Server in other environments, first clone the repository, then apply the deployment files as shown below:
```bash theme={null}
git clone https://github.com/kubernetes-incubator/metrics-server.git
```
```bash theme={null}
kubectl create -f deploy/1.8+/
```
After running these commands, you should see output similar to the following:
```text theme={null}
clusterrolebinding "metrics-server:system:auth-delegator" created
rolebinding "metrics-server-auth-reader" created
apiservice "v1beta1.metrics.k8s.io" created
serviceaccount "metrics-server" created
deployment "metrics-server" created
service "metrics-server" created
clusterrole "system:metrics-server" created
clusterrolebinding "system:metrics-server" created
```
These steps deploy the necessary pods, services, and roles for the Metrics Server to gather real-time performance metrics from your cluster nodes. Allow a few moments for the Metrics Server to start collecting data.
## Viewing Cluster Metrics
After deployment, you can view your cluster's performance metrics using the following commands:
### Node Metrics
To see CPU and memory usage for each node, run:
```bash theme={null}
kubectl top node
```
Example output:
```text theme={null}
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
kubemaster 166 8% 1337Mi 70%
kubnode1 36 1% 1046Mi 55%
kubnode2 39 1% 1048Mi 55%
```
### Pod Metrics
To display pod-level metrics, execute:
```bash theme={null}
kubectl top pod
```
Example output:
```text theme={null}
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
nginx 166 8% 1337Mi 70%
redis 36 1% 1046Mi 55%
```
## Conclusion
This article provided an overview of monitoring Kubernetes clusters using the Metrics Server. By deploying this lightweight solution, you can efficiently monitor node and pod performance, ensuring your cluster runs smoothly. For further practice, explore the provided coding exercises to reinforce your monitoring skills in Kubernetes.
Happy monitoring!