This repository is a solution of the Kubernetes Resume Challenge.

This project highlights proficiency in Kubernetes and containerization, demonstrating the ability to deploy, scale, and manage web applications efficiently in a K8s environment, underscoring cloud-native deployment skills.

Prerequisites

• Docker & K8s CLI installed
• KodeKloud K8s Crash Course Completed
• AWS Account Setup
• Github Account Setup
• Ecommerce Application & DB Script Overview

Database Containerization & Web Application Containerization(Local Testing using Docker Desktop)

• For testing purposes locally, i pulled/ran the MariaDB image as the database container in a docker network

docker run -d —network some-network —name mysql-service DB_USER=ecomuser  —env DB_PASSWORD=ecompassword —env MARIADB_ROOT_PASSWORD=ecompassword -p 3306:3306 mariadb:latest

• I accessed the database container to run SQL statements--which creates a database in the database server

docker run -it --network some-network --rm mariadb mariadb -h mysql-service -u root -p

CREATE DATABASE ecomdb;

CREATE USER 'ecomuser'@'localhost' IDENTIFIED BY 'ecompassword';

GRANT ALL PRIVILEGES ON ecomdb.* TO 'ecomuser'@'localhost';

FLUSH PRIVILEGES;

exit

• Loaded data into the new database and queried the data

USE ecomdb;

CREATE TABLE products (id mediumint(8) unsigned NOT NULL auto_increment,Name varchar(255) default NULL,Price varchar(255) default NULL, ImageUrl varchar(255) default NULL,PRIMARY KEY (id)) AUTO_INCREMENT=1;

INSERT INTO products (Name,Price,ImageUrl) VALUES ("Laptop","100","c-1.png"),("Drone","200","c-2.png"),("VR","300","c-3.png"),("Tablet","50","c-5.png"),("Watch","90","c-6.png"),("Phone Covers","20","c-7.png"),("Phone","80","c-8.png"),("Laptop","150","c-4.png");

select * from ecomdb.products; 

• I wrote a Dockerfile at the root of the Web Application
• I built a Docker image for the application and started the image in the same network as the database container

docker build -t ecom-web:v1 .

docker run -d —network some-network —name ecom-web -p 8080:80 ecom-web:v1

Implement CI(Continuous Integration)

• Created a .github/workflows/deploy.yml file to build the Docker image and push it to Docker Hub

Setup Kubernetes Cluster on AWS

• Setup an AWS EKS cluster using Infrastructure As Code:Terraform

Deployed Website to Kubernetes and Exposed The Wesbite Using Kubernetes Deployment and Service Manifests

• Kubernetes Deployment and Service Manifests
• Outcome URL: http://a37a0662677084f048a9b0287d65371a-762690969.eu-north-1.elb.amazonaws.com/

Implement Configuration Management

• Added a dark theme CSS file and a simple feature toggle in the application code
• Created a ConfigMap named ecom-web-config with the data FEATURE_DARK_MODE=true.
• Outcome: Website Dark Mode

Autoscale the application; preparing for a marketing campaign

• Current number of running pods: kubectl get pods

• Using kubectl scale deployment/ecom-web --replicas=6 to handle the increased load

• Observing the deployment scaling up with kubectl get pods

Performing a Rolling Update

• Modified web application’s code to include promotional banner
• Built a updated Docker image--tomiwa97/ecom-web:v2 and pushed to DockerHub
• Updated ecom-web.yaml with the new image version
• Outcome: kubectl rollout status deployment/ecom-web

Rolling Back Deployment

• Executed kubectl rollout undo deployment/ecom-web to revert to the previous deployment state

Implement Liveness and Readiness Probes for the Application

• Added liveness and readiness probes to ecom-web.yml, targeting an endpoint/port in the application that confirms its operational status
• Updated deployment with the new configuration and tested probes

Autoscaling the application based on CPU usage to handle unpredictable traffic spikes

• Installed Kubernetes Metrics Server: kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

• Verify that the metrics-server deployment is running the desired number of pods:

• Created a HorizontalPodAutoscaler Manifest
• Apply HPA: kubectl apply -f ecom-hpa.yml

• Using Kubectl Load Generator or Apache Bench to generate traffic and increase CPU load: kubectl run -i --tty load-generator --rm --image=busybox:1.28 --restart=Never -- /bin/sh -c "while sleep 0.01; do wget -q -O- http://a8a04786e58eb4c5e8c38676cc7d08eb-1285750877.eu-north-1.elb.amazonaws.com/; done" kubectl run -i --rm --restart=Never --image=mocoso/apachebench apachebench-1 -- bash -c "ab -n 10000 -c 1000 http://a8a04786e58eb4c5e8c38676cc7d08eb-1285750877.eu-north-1.elb.amazonaws.com/"

Implement Persistent Storage

• Defined Deployment, PersistentVolumeClaim, PersistentVolume and StorageClass for MariaDB Storage needs

Helm Charts

• Defined Helm Charts for the application making deployment and management on Kubernetes clusters more efficient and scalable