Wednesday, January 19, 2022

Deploy Python App into Kubernetes Cluster using kubectl Jenkins Pipeline | Containerize Python App and Deploy into EKS Cluster | Kubectl Deployment using Jenkins

We will learn how to automate Docker builds using Jenkins and Deploy into Kubernetes Cluster in AWS Cloud. We will use kubectl command to deploy Docker images into EKS cluster. We will use Python based application. I have already created a repo with source code + Dockerfile. The repo also have Jenkinsfile for automating the following:

- Automating builds using Jenkins
- Automating Docker image creation
- Automating Docker image upload into Elastic container registry
- Automating Deployments to Kubernetes Cluster using kubectl CLI plug-in



Pre-requistes:
1. EKS Cluster is setup and running. Click here to learn how to create EKS cluster.
2. Jenkins Master is up and running.
3. Install Docker in Jenkins.
4. Docker, Docker pipeline and Kubectl CLI plug-ins are installed in Jenkins





5. ECR repo created to store docker images.

The Code for this video is here:
and make necessary changes in eks-deploy-from-ecr.yaml file after you fork into your account.

Step #1 - Create Credentials for connecting to EKS cluster using Kubeconfig
Go to Jenkins UI, click on Credentials -->


Click on Global credentials
Click on Add Credentials

use secret file from drop down.

execute the below command to login as jenkins user.
sudo su - jenkins

you should see the nodes running in EKS cluster.

kubectl get nodes


Execute the below command to get kubeconfig info, copy the entire content of the file:
cat /var/lib/jenkins/.kube/config




Open your text editor or notepad, copy and paste the entire content and save in a file.
We will upload this file.

Enter ID as K8S and choose File and upload the file and save.


Step # 2 - Create a pipeline in Jenkins
Create a new pipeline job.


Step # 3 - Copy the pipeline code from below
Make sure you change values as per your settings highlighted in yellow below:

pipeline {
    agent any

    environment {
        registry = "account_id.dkr.ecr.us-east-2.amazonaws.com/my-docker-repo"
    }
    stages {
        stage('checkout') {
            steps {
                checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[url: 'https://github.com/akannan1087/myPythonDockerRepo']]])
            }
        }
        
        stage ("build image") 
        {
            steps {
                script {
                    dockerImage = docker.build registry
                    }
                }
        }
        
        stage ("upload ECR") {
            steps {
                script {
                    sh "aws ecr get-login-password --region us-east-2 | docker login --username AWS --password-stdin account_id.dkr.ecr.us-east-2.amazonaws.com"
                    sh "docker push account_id.dkr.ecr.us-east-2.amazonaws.com/my-docker-repo:latest"
                }
            }
        }
        
        stage ("Deploy to K8S") {
            steps {
                withKubeConfig(caCertificate: '', clusterName: '', contextName: '', credentialsId: 'K8S', namespace: '', serverUrl: '') {
                      sh 'kubectl apply -f eks-deploy-from-ecr.yaml'
                }
            }
        }
    }    
}

Step # 4 - Build the pipeline



Step # 5 - Verify deployments to EKS

kubectl get pods


kubectl get deployments
kubectl get services


Steps # 6 - Access Python App in K8S cluster
Once deployment is successful, go to browser and enter above load balancer URL along with port number 5000 mentioned above

You should see page like below:

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