How to Setup Prometheus Monitoring On Kubernetes Cluster

This Prometheus kubernetes tutorial will guide you through setting up Prometheus on a Kubernetes cluster for monitoring the Kubernetes cluster.

This setup collects node, pods, and service metrics automatically using Prometheus service discovery configurations.

About Prometheus

Prometheus is a high-scalable open-source monitoring framework. It provides out-of-the-box monitoring capabilities for the Kubernetes container orchestration platform. Also, In the observability space, it is gaining huge popularity as it helps with metrics and alerts.

Explaining Prometheus is out of the scope of this article. If you want to know more about Prometheus, You can watch all the Prometheus-related videos from here.

However, there are a few key points I would like to list for your reference.

1. Metric Collection: Prometheus uses the pull model to retrieve metrics over HTTP. There is an option to push metrics to Prometheus using Pushgateway for use cases where Prometheus cannot Scrape the metrics. One such example is collecting custom metrics from short-lived kubernetes jobs & Cronjobs
2. Metric Endpoint: The systems that you want to monitor using Prometheus should expose the metrics on an /metrics endpoint. Prometheus uses this endpoint to pull the metrics in regular intervals.
3. PromQL: Prometheus comes with PromQL, a very flexible query language that can be used to query the metrics in the Prometheus dashboard. Also, the PromQL query will be used by Prometheus UI and Grafana to visualize metrics.
4. Prometheus Exporters: Exporters are libraries that convert existing metrics from third-party apps to Prometheus metrics format. There are many official and community Prometheus exporters. One example is, the Prometheus node exporter. It exposes all Linux system-level metrics in Prometheus format.
5. TSDB (time-series database): Prometheus uses TSDB for storing all the data efficiently. By default, all the data gets stored locally. However, to avoid a single point of failure, there are options to integrate remote storage for Prometheus TSDB.

If you would like to install Prometheus on a Linux VM, please see the Prometheus on Linux guide.

Prometheus Architecture

Here is the high-level architecture of Prometheus. If you want to understand prometheus components in detail, please read the detailed Prometheus Architecture blog.

The Kubernetes Prometheus monitoring stack has the following components.

1. Prometheus Server
2. Alert Manager
3. Grafana

In a nutshell, the following image depicts the high-level Prometheus kubernetes architecture that we are going to build. We have separate blogs for each component setup.

Note: The Linux Foundation has a Prometheus Certified Associate (PCA) certification exam. PCA focuses on showcasing skills related to observability, open-source monitoring, and alerting toolkit. Use code DCUBE20 Today to get instance discount on the certificatication.

Prometheus Monitoring Setup on Kubernetes

I assume that you have a Kubernetes cluster up and running with kubectl setup on your workstation.

Note: If you don’t have a Kubernetes setup, you can set up a cluster on google cloud or use minikube setup, or a vagrant automated setup or EKS cluster setup

The latest Prometheus is available as a docker image in its official docker hub account. We will use that image for the setup.

Connect to the Kubernetes Cluster

Connect to your Kubernetes cluster and make sure you have admin privileges to create cluster roles.

Only for GKE: If you are using Google cloud GKE, you need to run the following commands as you need privileges to create cluster roles for this Prometheus setup.

ACCOUNT=$(gcloud info --format='value(config.account)')
kubectl create clusterrolebinding owner-cluster-admin-binding \
    --clusterrole cluster-admin \
    --user $ACCOUNT

Prometheus Kubernetes Manifest Files

All the configuration files I mentioned in this guide are hosted on Github. You can clone the repo using the following command.

git clone https://github.com/techiescamp/kubernetes-prometheus

Thanks to James for contributing to this repo. Please don’t hesitate to contribute to the repo for adding features.

You can use the GitHub repo config files or create the files on the go for a better understanding, as mentioned in the steps.

Let’s get started with the setup.

Create a Namespace & ClusterRole

First, we will create a Kubernetes namespace for all our monitoring components. If you don’t create a dedicated namespace, all the Prometheus kubernetes deployment objects get deployed on the default namespace.

Execute the following command to create a new namespace named monitoring.

kubectl create namespace monitoring

Prometheus uses Kubernetes APIs to read all the available metrics from Nodes, Pods, Deployments, etc. For this reason, we need to create an RBAC policy with read access to required API groups and bind the policy to the monitoring namespace.

Step 1: Create a file named clusterRole.yaml and copy the following RBAC role.

Note: In the role, given below, you can see that we have added get, list, and watch permissions to nodes, services endpoints, pods, and ingresses. The role binding is bound to the monitoring namespace. If you have any use case to retrieve metrics from any other object, you need to add that in this cluster role.

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
- apiGroups: [""]
  resources:
  - nodes
  - nodes/proxy
  - services
  - endpoints
  - pods
  verbs: ["get", "list", "watch"]
- apiGroups:
  - extensions
  resources:
  - ingresses
  verbs: ["get", "list", "watch"]
- nonResourceURLs: ["/metrics"]
  verbs: ["get"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: prometheus
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: default
  namespace: monitoring

Step 2: Create the role using the following command.

kubectl create -f clusterRole.yaml

Create a Config Map To Externalize Prometheus Configurations

All configurations for Prometheus are part of prometheus.yaml file and all the alert rules for Alertmanager are configured in prometheus.rules.

1. prometheus.yaml: This is the main Prometheus configuration which holds all the scrape configs, service discovery details, storage locations, data retention configs, etc)
2. prometheus.rules: This file contains all the Prometheus alerting rules

By externalizing Prometheus configs to a Kubernetes config map, you don’t have to build the Prometheus image whenever you need to add or remove a configuration. You need to update the config map and restart the Prometheus pods to apply the new configuration.

The config map with all the Prometheus scrape config and alerting rules gets mounted to the Prometheus container in /etc/prometheus location as prometheus.yaml and prometheus.rules files.

Step 1: Create a file called config-map.yaml and copy the file contents from this link –> Prometheus Config File.

Step 2: Execute the following command to create the config map in Kubernetes.

kubectl create -f config-map.yaml

It creates two files inside the container.

Note: In Prometheus terms, the config for collecting metrics from a collection of endpoints is called a job.

The prometheus.yaml contains all the configurations to discover pods and services running in the Kubernetes cluster dynamically. We have the following scrape jobs in our Prometheus scrape configuration.

1. kubernetes-apiservers: It gets all the metrics from the API servers.
2. kubernetes-nodes: It collects all the kubernetes node metrics.
3. kubernetes-pods: All the pod metrics get discovered if the pod metadata is annotated with prometheus.io/scrape and prometheus.io/port annotations.
4. kubernetes-cadvisor: Collects all cAdvisor metrics.
5. kubernetes-service-endpoints: All the Service endpoints are scrapped if the service metadata is annotated with prometheus.io/scrape and prometheus.io/port annotations. It can be used for black-box monitoring.

prometheus.rules contains all the alert rules for sending alerts to the Alertmanager.

Create a Prometheus Deployment

Step 1: Create a file named prometheus-deployment.yaml and copy the following contents onto the file. In this configuration, we are mounting the Prometheus config map as a file inside /etc/prometheus as explained in the previous section.

Note: This deployment uses the latest official Prometheus image from the docker hub. Also, we are not using any persistent storage volumes for Prometheus storage as it is a basic setup. When setting up Prometheus for production uses cases, make sure you add persistent storage to the deployment.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus-deployment
  namespace: monitoring
  labels:
    app: prometheus-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prometheus-server
  template:
    metadata:
      labels:
        app: prometheus-server
    spec:
      containers:
        - name: prometheus
          image: prom/prometheus
          args:
            - "--storage.tsdb.retention.time=12h"
            - "--config.file=/etc/prometheus/prometheus.yml"
            - "--storage.tsdb.path=/prometheus/"
          ports:
            - containerPort: 9090
          resources:
            requests:
              cpu: 500m
              memory: 500M
            limits:
              cpu: 1
              memory: 1Gi
          volumeMounts:
            - name: prometheus-config-volume
              mountPath: /etc/prometheus/
            - name: prometheus-storage-volume
              mountPath: /prometheus/
      volumes:
        - name: prometheus-config-volume
          configMap:
            defaultMode: 420
            name: prometheus-server-conf
  
        - name: prometheus-storage-volume
          emptyDir: {}

Step 2: Create a deployment on monitoring namespace using the above file.

kubectl create  -f prometheus-deployment.yaml 

Step 3: You can check the created deployment using the following command.

kubectl get deployments --namespace=monitoring

You can also get details from the kubernetes dashboard as shown below.

Connecting To Prometheus Dashboard

You can view the deployed Prometheus dashboard in three different ways.

1. Using Kubectl port forwarding
2. Exposing the Prometheus deployment as a service with NodePort or a Load Balancer.
3. Adding an Ingress object if you have an Ingress controller deployed.

Let’s have a look at all three options.

Method 1: Using Kubectl port forwarding

Using kubectl port forwarding, you can access a pod from your local workstation using a selected port on your localhost. This method is primarily used for debugging purposes.

Step 1: First, get the Prometheus pod name.

kubectl get pods --namespace=monitoring

The output will look like the following.

➜  kubectl get pods --namespace=monitoring
NAME                                     READY     STATUS    RESTARTS   AGE
prometheus-monitoring-3331088907-hm5n1   1/1       Running   0          5m

Step 2: Execute the following command with your pod name to access Prometheus from localhost port 8080.

Note: Replace prometheus-monitoring-3331088907-hm5n1 with your pod name.

kubectl port-forward prometheus-monitoring-3331088907-hm5n1 8080:9090 -n monitoring

Step 3: Now, if you access http://localhost:8080 on your browser, you will get the Prometheus home page.

Method 2: Exposing Prometheus as a Service [NodePort & LoadBalancer]

To access the Prometheus dashboard over a IP or a DNS name, you need to expose it as a Kubernetes service.

Step 1: Create a file named prometheus-service.yaml and copy the following contents. We will expose Prometheus on all kubernetes node IP’s on port 30000.

Note: If you are on AWS, Azure, or Google Cloud, You can use Loadbalancer type, which will create a load balancer and automatically points it to the Kubernetes service endpoint.

apiVersion: v1
kind: Service
metadata:
  name: prometheus-service
  namespace: monitoring
  annotations:
      prometheus.io/scrape: 'true'
      prometheus.io/port:   '9090'
spec:
  selector: 
    app: prometheus-server
  type: NodePort  
  ports:
    - port: 8080
      targetPort: 9090 
      nodePort: 30000

The annotations in the above service YAML makes sure that the service endpoint is scrapped by Prometheus. The prometheus.io/port should always be the target port mentioned in service YAML

Step 2: Create the service using the following command.

kubectl create -f prometheus-service.yaml --namespace=monitoring

Step 3: Once created, you can access the Prometheus dashboard using any of the Kubernetes node’s IP on port 30000. If you are on the cloud, make sure you have the right firewall rules to access port 30000 from your workstation.

Step 4: Now, if you browse to status --> Targets, you will see all the Kubernetes endpoints connected to Prometheus automatically using service discovery as shown below.

The kube-state-metrics down is expected and I’ll discuss it shortly.

Step 5: You can head over to the homepage and select the metrics you need from the drop-down, and get the graph for the time range you mention. An example graph for container_cpu_usage_seconds_total is shown below.

Method 3: Exposing Prometheus Using Ingress

If you have an existing ingress controller setup, you can create an ingress object to route the Prometheus DNS to the Prometheus backend service.

Also, you can add SSL for Prometheus in the ingress layer. You can refer to the Kubernetes ingress TLS/SSL Certificate guide for more details.

Here is a sample ingress object. Please refer to this GitHub link for a sample ingress object with SSL

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: prometheus-ui
  namespace: monitoring
  annotations:
    kubernetes.io/ingress.class: nginx
spec:
  rules:
  # Use the host you used in your kubernetes Ingress Configurations
  - host: prometheus.example.com
    http:
      paths:
      - backend:
          serviceName: prometheus-service
          servicePort: 8080

Setting Up Kube State Metrics

Kube state metrics service will provide many metrics that are not available by default. Please make sure you deploy Kube state metrics to monitor all your Kubernetes API objects like deployments, pods, jobs, cronjobs etc.

Please follow this article to setup Kube state metrics on Kubernetes ==> How To Setup Kube State Metrics on Kubernetes

Setting Up Alertmanager

Alertmanager handles all the alerting mechanisms for Prometheus metrics. There are many integrations available to receive alerts from the Alertmanager (Slack, email, API endpoints, etc)

I have covered the Alert Manager setup in a separate article. Please follow ==> Alert Manager Setup on Kubernetes

Setting Up Grafana

Using Grafana, you can create dashboards from Prometheus metrics to monitor the Kubernetes cluster.

The best part is, you don’t have to write all the PromQL queries for the dashboards.

There are many community dashboard templates available for Kubernetes. You can import it and modify it as per your needs. I have covered it in the article.

Please follow this article for the Grafana setup ==> How To Setup Grafana On Kubernetes

Setting Up Node Exporter

Node Exporter will provide all the Linux system-level metrics of all Kubernetes nodes.

I have written a separate step-by-step guide on node-exporter daemonset deployment. Please follow Setting up Node Exporter on Kubernetes

The scrape config for node-exporter is part of the Prometheus config map. Once you deploy the node-exporter, you should see node-exporter targets and metrics in Prometheus.

Prometheus Production Setup Considerations

For the production Prometheus setup, there are more configurations and parameters that need to be considered for scaling, high availability, and storage. It all depends on your environment and data volume.

For example, Prometheus Operator project makes it easy to automate Prometheus setup and its configurations.

If you have multiple production clusters, you can use the CNCF project Thanos to aggregate metrics from multiple Kubernetes Prometheus sources.

Thanos provides features like multi-tenancy, horizontal scalability, and disaster recovery, making it possible to operate Prometheus at scale with high availability.

With Thanos, you can query data from multiple Prometheus instances running in different kubernetes clusters in a single place, making it easier to aggregate metrics and run complex queries.

Additionally, Thanos can store Prometheus data in an object storage backend, such as Amazon S3 or Google Cloud Storage, which provides an efficient and cost-effective way to retain long-term metric data.

Conclusion

In this comprehensive Prometheus kubernetes tutorial, I have covered the setup of important monitoring components to understand Kubernetes monitoring.

In the next blog, I will cover the Prometheus setup using helm charts. We will have the entire monitoring stack under one helm chart.

Also, If you are learning Kubernetes, you can check out my Kubernetes beginner tutorials where I have 40+ comprehensive guides.

Let me know what you think about the Prometheus monitoring setup by leaving a comment.

You can also use this setup to prepare for the Prometheus Certified Associate Certification.