Kubernetes / Kubernetes CI/CD Integration

Integrating Kubernetes with Jenkins CI/CD

This tutorial will guide you through the steps to integrate Kubernetes with Jenkins CI/CD. Jenkins is an open-source tool that provides continuous integration services for softwar…

Tutorial 1 of 5 5 resources in this section

Section overview

5 resources

Explains how to integrate Kubernetes with CI/CD pipelines.

1. Introduction

In this tutorial, we will learn how to integrate Kubernetes with Jenkins CI/CD. Continuous Integration (CI) and Continuous Delivery (CD) embody a culture, set of operating principles, and collection of practices that enable application development teams to deliver code changes more frequently and reliably. Jenkins is a self-contained, open-source automation server which can be used to automate all sorts of tasks related to building, testing, and delivering or deploying software. Kubernetes, on the other hand, is an open-source system for automating deployment, scaling, and management of containerized applications.

You will learn how to set up a Jenkins CI/CD pipeline to build Docker images and deploy them into a Kubernetes cluster.

Prerequisites

  • Basic understanding of Jenkins and Kubernetes.
  • A working Jenkins and Kubernetes installation.
  • Docker installed on the Jenkins server.

2. Step-by-Step Guide

Jenkins Configuration

Before we start, we need to make sure that Jenkins has the Kubernetes Continuous Deploy plugin installed. This will allow Jenkins to deploy updates to Kubernetes directly after the build process.

  1. Go to Manage Jenkins > Manage Plugins > Available.
  2. Search for the 'Kubernetes Continuous Deploy' plugin and install it.

Jenkinsfile

Jenkins uses a 'Jenkinsfile' to define a pipeline. We will use this file to define the steps of our CI/CD process. Here's a basic example of what this file might look like:

pipeline {
  agent any
  stages {
    stage('Build') {
      steps {
        echo 'Building...'
      }
    }
    stage('Test') {
      steps {
        echo 'Testing...'
      }
    }
    stage('Deploy') {
      steps {
        echo 'Deploying...'
      }
    }
  }
}

Each stage in this pipeline represents a part of the software's life cycle. Our job is to fill these stages with life.

Building Docker Images

The first stage of our pipeline will be used to build Docker images. For this, we will use the Docker Pipeline plugin.

  1. Install the Docker Pipeline plugin via Manage Jenkins > Manage Plugins > Available.
  2. Add the following to the 'Build' stage in your Jenkinsfile:
stage('Build') {
  steps {
    script {
      dockerImage = docker.build("my-image:${env.BUILD_ID}")
    }
  }
}

This will build a Docker image using the Dockerfile found in the root directory of your project and tag it with the current build ID.

3. Code Examples

Deploying to Kubernetes

Now that we have our Docker image, we can deploy it to our Kubernetes cluster. For this, we will use the Kubernetes CLI.

Add the following to the 'Deploy' stage in your Jenkinsfile:

stage('Deploy') {
  steps {
    script {
      kubernetesDeploy(configs: 'k8s/*.yaml', kubeconfigId: 'my-kubeconfig', enableConfigSubstitution: true)
    }
  }
}

This configuration will deploy all Kubernetes configurations found in the 'k8s' directory of your project. It uses a kubeconfig file with the ID 'my-kubeconfig', which should be added to Jenkins as a secret file.

The 'enableConfigSubstitution' option allows us to use environment variables in our Kubernetes configurations.

4. Summary

In this tutorial, we've learned how to set up a Jenkins CI/CD pipeline that builds Docker images and deploys them into a Kubernetes cluster. This allows for a smooth, automated deployment process that ensures your software is always up to date.

For further learning, consider exploring more advanced Jenkins and Kubernetes concepts, such as Jenkins shared libraries and Kubernetes deployments.

5. Practice Exercises

  1. Modify the pipeline to include a 'Staging' step, which deploys the application to a staging environment before deploying to production.
  2. Add a rollback mechanism to the pipeline, which reverts the application to a previous state if the deployment fails.
  3. Implement automated testing in the pipeline, which prevents the application from being deployed if any tests fail.

Remember, practice is key when learning new concepts. Don't be afraid to experiment and make mistakes, as they are a crucial part of the learning process.

Need Help Implementing This?

We build custom systems, plugins, and scalable infrastructure.

Discuss Your Project

Related topics

Keep learning with adjacent tracks.

View category

HTML

Learn the fundamental building blocks of the web using HTML.

Explore

CSS

Master CSS to style and format web pages effectively.

Explore

JavaScript

Learn JavaScript to add interactivity and dynamic behavior to web pages.

Explore

Python

Explore Python for web development, data analysis, and automation.

Explore

SQL

Learn SQL to manage and query relational databases.

Explore

PHP

Master PHP to build dynamic and secure web applications.

Explore

Popular tools

Helpful utilities for quick tasks.

Browse tools

JSON Formatter & Validator

Beautify, minify, and validate JSON data.

Use tool

Age Calculator

Calculate age from date of birth.

Use tool

XML Sitemap Generator

Generate XML sitemaps for search engines.

Use tool

Time Zone Converter

Convert time between different time zones.

Use tool

Countdown Timer Generator

Create customizable countdown timers for websites.

Use tool

Latest articles

Fresh insights from the CodiWiki team.

Visit blog

AI in Drug Discovery: Accelerating Medical Breakthroughs

In the rapidly evolving landscape of healthcare and pharmaceuticals, Artificial Intelligence (AI) in drug dis…

Read article

AI in Retail: Personalized Shopping and Inventory Management

In the rapidly evolving retail landscape, the integration of Artificial Intelligence (AI) is revolutionizing …

Read article

AI in Public Safety: Predictive Policing and Crime Prevention

In the realm of public safety, the integration of Artificial Intelligence (AI) stands as a beacon of innovati…

Read article

AI in Mental Health: Assisting with Therapy and Diagnostics

In the realm of mental health, the integration of Artificial Intelligence (AI) stands as a beacon of hope and…

Read article

AI in Legal Compliance: Ensuring Regulatory Adherence

In an era where technology continually reshapes the boundaries of industries, Artificial Intelligence (AI) in…

Read article

Need help implementing this?

Get senior engineering support to ship it cleanly and on time.

Get Implementation Help