KubeLearn Project Documentation

Overview

KubeLearn is a tool designed to help users test and expand their knowledge of Kubernetes. This project includes both a backend, which runs various Kubernetes-related tasks, and a frontend, which provides a user interface for interacting with the tool.

Project Structure

The project is organized into the following directories:

.
├── cmd
│   └── main.go                # Backend Go application
├── kubelearn-frontend         # Frontend React application
│   ├── postcss.config.js
│   ├── src
│   │   ├── App.js
│   │   ├── App.test.js
│   │   ├── index.js
│   │   ├── reportWebVitals.js
│   │   └── setupTests.js
│   └── tailwind.config.js
├── makefile                   # Makefile for managing the project
└── pkg
    ├── k8s                    # Kubernetes-related utilities
    │   └── client.go
    ├── resources              # Contains Kubernetes-related questions
    │   ├── easy
    │   ├── hard
    │   └── medium
    └── utils                  # Additional utilities

Prerequisites

To run the project locally, you need to have the following installed:

• Go (Golang)
• Node.js and npm
• Terraform
• Kubernetes (kubectl)
• Kind (for local Kubernetes clusters)

Makefile Commands

The Makefile provides several commands to manage the project efficiently. Below is a summary of the available targets:

Setup and Run KubeLearn

This command initializes Terraform, sets up the backend and frontend, and starts the services.

make Kubelearn

Stop KubeLearn

This command stops the backend and frontend services.

make stopKubelearn

Terraform Commands

• init: Initializes the Terraform configuration.

  make init

• apply: Applies the Terraform configuration.

  make apply

• destroy: Destroys the Terraform-managed infrastructure.

  make destroy

Kubernetes Manifest Management

• all: Installs all Kubernetes manifests from the manifests directory.

  make all

• clean: Deletes all installed Kubernetes resources.

  make clean

• check-syntax: Validates the syntax of all Kubernetes manifests without applying them.

  make check-syntax

Running the Project

1. Setup Environment: Run make Kubelearn to initialize Terraform and start both the backend and frontend.

2. Access the Application: After running the setup, the frontend will be available at http://localhost:3000 by default, and the backend API at http://localhost:8083.

3. Stop the Services: Run make stopKubelearn to stop the backend and frontend services.

Additional Information

• Logs for the backend and frontend are stored in backend.log and frontend.log, respectively.
• Ensure that your Kubernetes environment is properly configured before running the application.

Troubleshooting

• If Terraform fails to initialize, ensure that your Terraform installation is correct and that the config directory contains valid configurations.
• If the frontend doesn't start, verify that all npm dependencies are installed correctly.

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This documentation should cover the basic setup and usage of the KubeLearn project. For more detailed instructions or troubleshooting, please refer to the specific sections in this document.

You can use this documentation as a starting point and expand it further as needed. It covers the basic commands, project structure, and steps to run the project locally.

Answer the questions below

Questions	Description
1	Create a new pod called nginx with nginx:alpine image in default namespace.
2	Create a new deployment called nginx-deployment with nginx:alpine image and 4 replicas in default namespace.
3	Create a new deployment called redis with image redis:alpine in latam namespace, and create a service called redis-service with port 6379 in same namespace.
4	Create a namespace called europe
5	Create a configmap europe-configmap with data France=Paris
6	Create a pod thsoot with label country=china, amazon/amazon-ecs-network-sidecar:latest image and namespace asia
7	Create a persistent volume unicorn-pv with capacity 1Gi and access mode ReadWriteMany and host path /tmp/data
8	Create a persistent volume claim unicorn-pvc with capacity 400Mi and access mode ReadWriteMany
9	Create a pod webserver in public namespace with nginx:alpine image and a volume mount /usr/share/nginx/html and a persistent volume claim unicorn-pvc
10	There is a pod with problem, Can you able to solve it ?
11	Create a network policy allow-policy-colors to allow redmobile-webserver to access bluemobile-dbcache (There objects are created in colors namespace)
12	Create a secret secret-colors with data color=red in colors namespace
13	Add a secret secret-purple with data singer=prince to the pod purple with image redis:alpine in colors namespace
14	Create a service account america-sa in default namespace
15	Add service account america-sa to the deployment mark42
16	Change the replica count of the deployment mark42 to 5
17	Create a horizontal pod autoscaler for deployment mark43 with cpu percent 80, min replicas 2 and max replicas 8
18	Prevent privilege escalation in the deployment mark42
19	Add a liveness probe to the pod mark50 with initial delay 5s, period 10s and path / in namespace shield
20	Create a deployment yellow-deployment with bonovoo/node-app:1.0 image and 2 replicas in namespace colors
21	Create a service yellow-service for the deployment yellow-deployment in namespace colors with port 80 and target port 3000
22	Create an ingress ingress-colors with host yellow.com, path /yellow and service yellow-service in namespace colors
23	Create a role apple-one with verbs get, list, watch in namespace fruits
24	Create a job job-gain with parallelism 2, completions 4, backoffLimit 3 and deadlineSeconds 40
25	Create a cronjob cronjob-gain run a each 5 minutes with image busybox:1.28, command 'sleep 3600' and restartPolicy Never
26	Create a statefulset statefulset-gain with image busybox:1.28, command 'sleep 3600' and replicas 3