Data Persistent Prometheus-Grafana Intergration with Jenkins

Prometheus is an open-source application monitoring and alerting software solution. It is a web application which can be deployed anywhere — in a PC, virtual machine, or even in a container. It scrapes data from the exporters (small programs convert system data to Prometheus metrics) periodically and records the real-time metrics in a time series database.

On the other hand: Grafana is an open-source metric analytics & visualization application. It is used for visualizing time series data for infrastructure and application analytics.

It is also a web application which can be deployed anywhere that users want. It can target a data source from Prometheus and use its customizable panels to give users powerful visualization of the data from any infrastructure under management.

Why Prometheus + Grafana?

The relationship between Prometheus and Grafana is like a backend and frontend for a data monitoring system. Prometheus acts as the data collection and storage backend and Grafana as the interface for analysis and visualization.

One of the significant advantages of Prometheus is the exporter feature. The official Prometheus website has already provided many useful exporters for users, and the Prometheus communities also have many customized exporters built by users themselves. By using different kinds of the exporters, Prometheus is able to monitor applications in all types of environments. This is especially efficient for docker & Kubernetes container environments.

One of the significant advantages of Grafana is its customization features. It’s effortless to customize the visualization for massive amounts of data. Users can choose a linear graph, a single number panel, a gauge, a table, or a heatmap to display their data. They can also sort all their data with various labels; data with different labels will go to different panels. The dashboard can be rearranged and resized simply by dragging panels around. These customization features make Grafana useful and powerful for many different types of users, such as DevOps, Security, Operations, and Networking.

Problem Statement:

Integrate Prometheus and Grafana and perform in following way:
1. Deploy them as pods on top of Kubernetes by creating resources Deployment, Replica Set, Pods or Services.
2. And make their data to be remain persistent.
3. And both of them should be exposed to outside world.

Prerequisite:

Minikube and kubectl configured if not follow this link: https://kubernetes.io/docs/setup/learning-environment/minikube/One Node for matrices.

General Concepts:

Procedure:

Step 1:Here We need to make a deployment of Prometheus and Grafana.

Open your text-editor and type the following code > save it > grafana.yaml.

apiVersion: v1
kind: Service
metadata:
name: grafana
labels:
app: graf
spec:
ports:
- port: 3000
selector:
app: graf
type: NodePort
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: graf-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 2Gi


---


apiVersion: apps/v1
kind: Deployment
metadata:
name: grafana-deploy
labels:
app: graf
spec:
replicas: 1
selector:
matchLabels:
app: graf
strategy:
type: Recreate
template:
metadata:
labels:
app: graf
spec:
containers:
- image: vimal13/grafana
name: graf
ports:
- containerPort: 3000
name: graf
volumeMounts:
- name: graf-vol
mountPath: /var/lib/grafana
volumes:
- name: graf-vol
persistentVolumeClaim:
claimName: graf-pvc

Explanation: Here you need to expose the port i.e. 3000 , because Grafana runs on port 3000, in k8s to expose the port we need to use services i,e NodePort, for making data permanent here we use kind PersistentVolumeClaim , and gave access ReadWriteOnce, After that we have to make deployment of grafana in k8s kind is Deployment, spec for specification, gave image name i,e(vimal13/grafana), continerport (3000), for making data permanent here we have to mount the dir i.e. (var/lib/grafana) in the PersistentVolumeClaim.

Step 2: Configure file of prometheus.

Open your text-editor and type the following code > save it > prometheus.yaml.

apiVersion: v1

kind: Service

metadata:

name: prometheus

labels:

app: prom

spec:

ports:

- port: 9090

selector:

app: prom

type: NodePort

---

apiVersion: v1

kind: ConfigMap

metadata:

name: p-config

labels:

app: prom

data:

prometheus.yml: |

global:

scrape_interval: 15s

evaluation_interval: 15s

scrape_configs:

- job_name: 'prometheus'

static_configs:

- targets: ['localhost:9090']

- job_name: 'node1'

static_configs:

- targets: ['192.168.43.98:9100']

---

apiVersion: v1

kind: PersistentVolumeClaim

metadata:

name: prom-vol

spec:

accessModes:

- ReadWriteOnce

resources:

requests:

storage: 2Gi

---

apiVersion: apps/v1

kind: Deployment

metadata:

name: prometh-deploy

labels:

app: prom

spec:

replicas: 1

selector:

matchLabels:

app: prom

strategy:

type: Recreate

template:

metadata:

labels:

app: prom

spec:

containers:

- image: vimal13/prometheus

name: prom

ports:

- containerPort: 9090

name: prom

volumeMounts:

- name: p-config

mountPath: /etc/prometheus/prometheus.yml

subPath: prometheus.yml

- name: prom-vol

mountPath: /prometheus/data

volumes:

- name: p-config

configMap:

name: p-config

- name: prom-vol

persistentVolumeClaim:

claimName: prom-vol

Explanation: Here you need to expose port no. 9090, prometheus runs on port no. 9090, in k8s for exposing the port, kind is NodePort, to make the config file, Here we need to configure he kind ConfigMap, in the config map set your node ipi,e (IP:9100) port no. 9100 for node_exporter, for making data permanent here we use kind PersistentVolumeClaim, and gave access ReadWriteOnce, After that we have to make deployment in k8s, kind is Deployment , spec for specification, gave image name i.e.(vimal13/prometheus), conatinerPort(9090), Now here we have to mount to things first ConfigMap location (/etc prometheus/prometheus.yaml) and second PersistentVolumeClaim location (/prometheus/data) for making our data permament.

Step 3: Configure kustomization file.

Open your text-editor and type the following code > save it > kustomization.yaml.

apiVersion: kustomize.config.k8s.io/v1beta1

kind: Kustomization

resources:

- grafana.yaml

-

- prometheus.yaml

Explanation: This specifies the common resources and common customizations to those resources, Here where we need to use kink Kustomization in k8s, gave resources and it executes the resources, Here resources are yaml files .

To run the setup type > kubectl apply -k.

Step 4: Output of prometheus:

open your command prompt type > kubectl get services.

Now open your browser > type > IP of minikube: port no given by the NodePort i.e. 31555.

Step 5: Setup of Grafana.

open your command prompt type > kubectl get services.Now open your browser > type > IP of minikube:port no given by the NodePort i.e. 32106.To login> type> username(admin)> password(admin).Type your password to login > submit.Click on Data Sources for setup prometheus.Select prometheus.Type name , Gave the IP:port of prometheus.Now Here our prometheus configured in the Grafana.

Step 6: To create Dashboard in Grafana:

Goto Grafana Homepage.Click on the + icon to create Dashboard , Here you need to configure the Panel for Dashboard.Click on Add panel .Select Myprometheus.Give the query (node_memory_MemFree_bytes) for usage of RAM > give Visualization i.e (Graph).Give the name and save it.Add new Panel > Configure using following image:Add query node_cpu_seconds_total(for CPU usage)> select Gauge Visualization.After that add new panel > configure using following image:Add query node_disk_read_bytes_total(For Disk Usage), Select Bar gauge Visualization.Add One more New Panel > configure using following image:Add query sum by (job)(prometheus_http_requests_total{code=”200"}) , it means we need to see how many http_request on prometheus, sum if for sum the values , code=200 means valid request.Output after setup.After that we have to save our Dashboard.Click on the save symbol.Provide the name and save it.

Step 7: Now let's test whether our data is permanent or not.

Open command prompt and delete the pod of grafana.

Explanation: Here you can see our pod has been deleted, and after that it starts new pod this is because we already use deployment, if pod is deleted deployment start new ones, here you can see for some second our data , dashboard has gone.

Hence we complete the project.

You can also reach out on my Twitter, Instagram, or Facebook in case you need more help, I would be delighted to solve queries.

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Happy Coding!

#DevOps #Jenkins #Automation #Github #Git #Docker #Prometheus #Grafana #Metrics #PersistentVolume #Data #Developer #Kubernetes