Quickstart Overview

The purpose of this guide is to show you how to harness the full potential of Bunnyshell. To that end, we'll be diving into a simple project involving a predefined application, using some of the most important Bunnyshell features and discussing our platform's functionality.

What you'll learn

• How to create an Environment in Bunnyshell
• How to deploy your Environment
• How to create and use Ephemeral Environments
• How to use Remote Development
• How to debug frequent issues
• How to integrate and extend Bunnyshell using the CLI tool and our Public API

And a number of other useful features that Bunnyshell provides.

Resources

The Demo Repository

As part of this guide, we crafted a simple repository containing a demo application covering both learning paths (Helm and Docker-compose) and adding a Terraform module.

The repository: https://github.com/bunnyshell/demo-books

You can choose to run this on local, or directly in Bunnyshell.

The Demo Application

Bunnyshell Books, our demo Application, is composed of:

• The React frontend
• The backend (API made with Nodejs and Express)
• A Postgres database

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Disclaimer

The Bunnyshell Books demo app is inspired by this tutorial. The original repositories are located here.

The application allows easy local development as it uses either the Helm chart stored in helm folder, or a docker-compose.yaml file stored in the root of the repo. Each component is located in its own folder (an approach commonly referred to as monorepo).

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Note

Please note that the application is as simple as possible, to allow for a quick understanding of how it works and ease focusing on how Bunnyshell works.

It is by no means production ready, with concerns such as code architecture and error handling being largely ignored.

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Note

Because Bunnyshell handles the certificate for HTTPS, the current setup skips certificate generation and configuration.

Enhancing the application

Using Terraform, we'll expand the functionality of the app by adding images for books. To simplify app code changes, the images will be generated. Then, the images will be uploaded into an S3 bucket which will be created using a Terraform Module for AWS S3.

You will be able to choose the learning path (Helm or Docker-compose) later down the line.