Kubernetes Kapsule cluster monitoring with Prometheus & Grafana

Reviewed on 17 June 2024 • Published on 18 March 2020

kubernetes
kapsule
Prometheus
monitoring
Grafana

Kubernetes Kapsule provides a managed Kubernetes environment to create, configure, and run a cluster of preconfigured machines for containerized applications.

This tutorial will explain how to monitor your Kubernetes Kapsule cluster. The stack that we are going to deploy is based on Prometheus, Grafana, kube-state-metrics, and node-exporter. We will use Helm to deploy the whole stack. All applications used for this how-to are well-known and widely-used open-source software that fit well in a Kubernetes environment.

Prometheus: Prometheus is an application used for monitoring and alerting. It records real-time metrics in a time series database. It is based on a pull model and relies on HTTP for scraping the metrics.
Grafana: Grafana is used for visualizing the metrics scraped by Prometheus and stored in the time series database.
kube-state-metrics: kube-state-metrics listens to the Kubernetes API server and generates metrics about the state of the objects. The list of the exported metrics is available here. For instance, kube-state-metrics can report the number of pods ready (kube_pod_status_ready), or the number of unschedulable pods (kube_pod_status_unschedulable).
node-exporter: The node-exporter is a Prometheus exporter for hardware and OS metrics exposed by the Linux Kernel. It allows you to get metrics about CPU, memory, file system for each Kubernetes node.

Before you start

To complete the actions presented below, you must have:

A Scaleway account logged into the console
Owner status or IAM permissions allowing you to perform actions in the intended Organization
Created a Kapsule cluster
A working kubectl on your machine
Installed helm (version 3.2+), the Kubernetes package manager, on your local machine

Preparing the Kubernetes Kapsule cluster

Ensure you are connected to your cluster and kubectl and helm are installed on your local machine.

Add the Prometheus Community Helm repo and the stable Kubernetes repo and update them:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo add stable https://kubernetes-charts.storage.googleapis.com/
helm repo update

Deploying Prometheus

We are first going to deploy the Prometheus stack in a dedicated Kubernetes namespace called ”monitoring”. We will set the retention time for 30 days and create a persistent volume (based on Scaleway Block Storage) to store the Prometheus data.

Use the helm package manager to install the stable release of Prometheus. Set the following parameters to helm for both of these:

server.persistentVolume: 100Gi
server.retention: 30d

helm install prometheus prometheus-community/prometheus --create-namespace --namespace monitoring  --set server.persistentVolume.size=100Gi,server.retention=30d
NAME:   prometheus
LAST DEPLOYED: Fri Oct  9 16:35:50 2020
NAMESPACE: monitoring
STATUS: DEPLOYED
[..]

Once the stack is deployed, verify that the created pods are all running. It is also possible to check if the 100 Gi block volume was created:

kubectl get pods,pv,pvc -n monitoring
NAME                                                READY   STATUS    RESTARTS   AGE
pod/prometheus-alertmanager-6565668c85-5vdxc        2/2     Running   0          67s
pod/prometheus-kube-state-metrics-6756bbbb8-6qs9r   1/1     Running   0          67s
pod/prometheus-node-exporter-fbg6s                  1/1     Running   0          67s
pod/prometheus-pushgateway-6d75c59b7b-6knfd         1/1     Running   0          67s
pod/prometheus-server-556dbfdfb5-rx6nl              1/2     Running   0          67s
NAME                                                        CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM                                STORAGECLASS   REASON   AGE
persistentvolume/pvc-5a9def3b-22a1-4545-9adb-72823b899c36   100Gi      RWO            Delete           Bound    monitoring/prometheus-server         scw-bssd                67s
persistentvolume/pvc-c5e24d9b-3a69-46c1-9120-b16b7adf73e9   2Gi        RWO            Delete           Bound    monitoring/prometheus-alertmanager   scw-bssd                67s
NAME                                            STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS   AGE
persistentvolumeclaim/prometheus-alertmanager   Bound    pvc-c5e24d9b-3a69-46c1-9120-b16b7adf73e9   2Gi        RWO            scw-bssd       68s
persistentvolumeclaim/prometheus-server         Bound    pvc-5a9def3b-22a1-4545-9adb-72823b899c36   100Gi      RWO            scw-bssd       68s

To access Prometheus use the Kubernetes port forwarding feature:
```
export POD_NAME=$(kubectl get pods --namespace monitoring -l "app=prometheus,component=server" -o jsonpath="{.items[0].metadata.name}")
kubectl --namespace monitoring port-forward $POD_NAME 9090
```
Access the Prometheus dashboard using the following URL: http://localhost:9090.
Verify that both node-exporter and kube-state-metrics metrics are correctly scrapped by Prometheus:
- The node-exporter metrics begin with “node_”
- The kube-state-metrics begin with “kube_”
Prometheus can generate graphs on its own. You can test the feature by choosing metrics to analyze directly in the application:

Deploying Grafana

We are going to use and deploy Grafana to display the Prometheus metrics in some pre-defined dashboards. To do so, we are -as always- using helm. Once again we deploy it in the monitoring namespace and enable the persistence:

persistence.enable : true
persistence.type : pvc
persistence.size : 10Gi

Refer to the Loki tutorial to have additional information about Grafana.

Add the Helm repo and install Grafana using helm with the following commands:

helm repo add grafana https://grafana.github.io/helm-charts
helm install grafana grafana/grafana \
                              --set persistence.enabled=true,persistence.type=pvc,persistence.size=10Gi \
                              --namespace=monitoring

Once Grafana is installed retrieve the admin password:

kubectl get secret --namespace monitoring grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo

Configure the port forwarding to access the Grafana Web interface at this address: http://localhost:3000:
```
kubectl port-forward --namespace monitoring service/grafana 3000:80
Forwarding from 127.0.0.1:3000 -> 3000
Forwarding from [::1]:3000 -> 3000
```
Open the Grafana Web Interface in a web browser at http://localhost:3000. The login screen displays. Enter the user admin and the password recovered in step 2:
The welcome screen displays and invites you to complete the configuration of Grafana. Click Add data source to configure a new data source:
Choose Prometheus as data source from the list of available options.
Enter the details of the data source. You can leave the default settings and add the data source: http://prometheus-server. Click Test & Save to validate the connection to Prometheus and to save the settings:
Click the + sign and then Import to import a ready-to-use dashboard from the Grafana website. To create a dashboard that uses kube-state-metrics, import the dashboard number 8588 and get information about your Deployment, Statefulset, and Daemonset:
Choose Prometheus as data source and click Import.
Access the dashboard with metrics for Deployment, Statefulset, and Daemonset:
You can also configure additional dashboards, for example, the node exporter full dashboard (1860) to display a dashboard with system metrics for each Kubernetes node:

You now have basic monitoring for your Kubernetes Kapsule cluster. For more information on how to configure your cluster, refer to the official Kubernetes documentation.

Kubernetes Kapsule cluster monitoring with Prometheus & Grafana

Before you start

Preparing the Kubernetes Kapsule cluster

Deploying Prometheus

Deploying Grafana

External links