Programming language API

Get information about programming languages

Features

• Retrieve all programming languages
• Retrieve programming languages by a group of years (the 1940s, 1950s, 1990s, 2000s, etc.)
• Retrieve successors and predecessors of a language
• Retrieve programming languages authors

Prerequisites

• Node.js 12+
• Yarn or NPM
• MongoDB 4+
• Docker and Docker Compose

Installation

Clone the project and install the dependencies for each project

git clone https://github.com/osscameroon/prolang-api.git
cd prolang-api/apidoc && yarn install
cd ../backend && yarn install
cd ../frontend && yarn install

If you want to launch the application locally with configuring each project, run the command below at the project root directory

docker-compose up -d

Wait for the application to be ready then, navigate to http://localhost:5701 and explore the app.

Backend

The backend interacts with a Mongo database so, make sure you set up one before continuing. Check out this tutorial to see how to set up.

Create the environment file from the template and update the database URL to your

cp .env.tempalte .env
nano .env # update the properties, save and exit

Generate GraphQL types and start the application

yarn generate:types
yarn start

• Navigate to http://localhost:5700 for the REST API
• Navigate to http://localhost:5700/graphql for the GraphQL Playground

Apidoc

No additional action is required here to start working. Check out the Readme inside the project for more details

Frontend

Make sure the Backend is up and running before launching this project.

Create the environment file from the template and start the project

cp .env.template .env.development.local
yarn dev

Navigate to http://localhost:5701 to view the website

Testing

Every project has tests to validate the integrity of the feature before shipping in production.

Apidoc

The test verifies that the API definition is valid according to the OpenAPI specification. Run the command to validate:

yarn lint

Backend

The purpose is to make sure critical features of the application still work as expected by running unit and integration tests.

Testcontainers is used to create a database for testing purpose. So, you don't have to worry about your local DB is being polluted.

yarn test

Frontend

As same as for the Backend, unit and end-to-end tests are written on the critical component. Cypress is used to testing the navigation flow of the application. Jest is used to test component behavior

Run end-to-end tests with Cypress:

yarn test:it
# to launch it in headless browser mode
yarn test:it:ci 

Run component tests with Jest

yarn test

Deployment

We use GitHub Action for Continuous Integration and Continuous Delivery.

• The frontend deployment is handled by Vercel Bot that gives you a preview of the website when you create a pull request. It is deployed in production once merged on the main branch

• The backend deployment is handled also automated through a bash script that connects to the production server, pulls the new docker image, and runs it.

How to contribute

• Create an issue where you explain clearly the problem you want to solve
• Make a Pull Request
• If it's relevant, we're going to merge it. Yeah, it's simple as this!

License

• LICENSE: MIT