DEV Community: Kilama Elie

Deploy a Django Rest Api on AWS EC2 using Docker, NGINX, Gunicorn and GitHub Action.

Kilama Elie — Thu, 24 Apr 2025 11:06:51 +0000

Django, a high-level Python web framework, is a popular choice for building RESTful APIs. Once your Django Rest API has been developed and tested, deploying it becomes a crucial step in making it accessible to users.

In this blog post, we’ll guide you through the process of deploying a Django Rest API on AWS EC2 using Docker for containerization, NGINX as a reverse proxy and GitHub Actions for automated deployment.

And, we’ll take a closer look at each step, explain the concepts, provide code snippets and show you exactly what you need to do. Let’s get to the heart of the matter!

Prerequisites

Before diving into this blog, you need a solid foundation in several technologies and concepts in order to ensure efficient implementation. Such as:

Django: a high-level Python web framework.
Django REST framework: is a powerful and flexible toolkit for building Web APIs.
NGINX: A basic knowledge of NGINX as a web server and reverse proxy will be necessary to set up the server environment.
AWS Account: Create an AWS account and set up an EC2 instance.
Docker: Install Docker on your local machine for building and running containers.
GitHub Repository: Host your Django project on a GitHub repository.

Keep in mind that these requirements are essential, but that you don’t have to be an expert in every field. You can learn and develop your expertise as you go along. Moreover, official documentation online tutorials and communities can provide invaluable support as you learn.

Note that you can folk the source code at this the following repo: Try it yourself.

Create a New Project

Open your terminal and navigate to the directory where you want to create the project, for the purpose of this tutorial we’ll create a directory with a name django-tutorial or you can name it anything depending on your project name. This folder is considered as the root directory for our application which contains all folders and files structures.

Then, we will change our directory into the django-tutorial folder and all other parts of configuration files or folders will be in this folder.

 > mkdir django-tutorial
 > cd django-tutorial

1. Creating the Rest API

First of all, we need to install Django and Django Rest Framework, so make sure you already have Python installed on your machine.

create a virtual environment:

Inside the django-tutorial folder, to create and activate a new virtual environment runs the following command :

python3 -m venv .venv
source .venv/bin/activate

Set up Django project

We’ll be using pip, which is the package installer for Python, to install the required dependencies. Run the following commands:


django-tutorial$ pip install Django
django-tutorial$ pip install djangorestframework
django-tutorial$ pip install gunicorn
django-tutorial$ pip install django-cors-headers

Now let’s create a Django project and name it store

django-tutorial$ django-admin startproject store

Create a Django app:

django-tutorial$ cd store
django-tutorial$ python manage.py startapp book

Define a book model

In the book/models.py, define the book model

from django.db import models

class Book(models.Model):
    title = models.CharField(max_length=200)
    author = models.CharField(max_length=100)
    publication_date = models.DateField()
    description = models.TextField()

Create a serializer for the Book model

Inside book directory, let’s create a file named serializers.py to define the BookSerializer.

from rest_framework import serializers
from .models import Books

class BookSerializer (serializers.ModelSerializer):
  class Meta: 
      model = Books
      fields = '__all__'

Define a Views model

In the book/views.py , let’s define the BookViewSet model, which will handle the API requests

from rest_framework import viewsets
from .models import Books
from .serializers import BookSerializer

class BookViewSet(viewsets.ModelViewSet):
    queryset = Books.objects.all()
    serializer_class = BookSerializer

Define a URL routing

In the core/urls.py, let’s add the route url for the application

from django.urls import include, path
from rest_framework.routers import DefaultRouter
from books.views import BookViewSet

router = DefaultRouter()
router.register(r'book', BookViewSet)

urlpatterns = [
    path('admin/', admin.site.urls),
    path('', include(router.urls)),
]

After that, let's update core/settings.py :

INSTALLED_APPS = [
    ...
    'rest_framework',
    'corsheaders',
    'gunicorn',
    ...
    'book',
]

Also let’s add the Corsheaders middlewares here:

MIDDLEWARE = [
    .....
    "corsheaders.middleware.CorsMiddleware",
    "django.middleware.common.CommonMiddleware",
    ....
]

Test REST API locally

First all, let make the migrations to create the database table

python manage.py makemigrations
python manage.py migrate

Start the Django development server:

python manage.py runserver

And should able to access the REST API at the http://localhost:8000 or http://127.0.0.1:8000

Before diving into the next section, let’s create requirements.txt file in the root directory which contains a list of all the installed packages in your Python environment, along with their corresponding versions, to generate the requirements file using:

pip freeze > requirements.txt

2. Add Docker to the Rest API

Docker provides a standardized way to package and distribute applications, making it easier to deploy and manage across different environments. In this section, we’ll containerize our Django application using Docker.

If you haven’t already, install Docker on your machine by following the instructions for your operating system: https://docs.docker.com/get-docker/

Create a Dockerfile:

In your Django project’s store directory(in the same directory where manage.py is located ), create a file named Dockerfile. This file defines the instructions to build your Docker image.


# Pull the image from Dockerhub
FROM python:alpine3.19

WORKDIR /store

# set up python environment variables

ENV PYTHONDOWNWRITEBYTECODE 1
ENV PYTHONNUNBUFFER 1

# update and  install dependencies
RUN pip install --upgrade pip
COPY ./requirements.txt /api/requirements.txt
RUN pip install -r /api/requirements.txt

# copy project
COPY . .

# Expose the port server is running on
EXPOSE 8000

Since in this project will have more than one container , then we ‘ll need to use docker-compose

Create a Docker-compose file:

Allows you to define and run multiple Docker containers as a single service, making it easier to manage complex applications.

If you haven’t already, you’ll need to install Docker Compose.
Follow the installation instructions for your operating system: https://docs.docker.com/compose/install

In your project’s root directory as django-tutorial, create a file named docker-compose.yml.


version: '3'
services:
  bookApi:
    build:
      context: ./store
      dockerfile: Dockerfile
    command: python manage.py runserver
    ports:
      - "8000:8000"

This defines only one service bookapi but a bit later more will be added.

Run the Django app using Docker:

So in order to test it, just go in your terminal, navigate to the directory containing the docker-compose.yml file.

docker-compose up

You can access your django app at http://localhost:8000. Kill the task in the terminal where docker-compose up is running in order to stop the. To remove the containers and their associated resources:

docker-compose down --rmi all

Adding a Gunicorn:

The Gunicorn “Green Unicorn” is a Python Web Server Gateway Interface HTTP server.

So, let updating the docker-compose file for the api service to start with gunicorn as web server gateway


version: '3'
services:
  api:
    build:
      context: ./store
      dockerfile: Dockerfile
    command: gunicorn core.wsgi:application --bind 0.0.0.0:8000

3. Adding NGINX:

NGINX is a web server for content and application delivery and can also be used as a reverse-proxy which improve performance and manage routing in production environments.

In the root of your project directory, create a Nginx directory named nginx then create a configuration file named nginx.conf . So inside nginx.conf file adds the following configuration:


# nginx.conf  
upstream api_upstream {
   server api:8000;
  }
server {
   listen 80;
   server_name localhost; 
   #server_name can add your domain or Ip address for your server in the production
   client_max_body_size 200M;
   location / {
      proxy_set_header X-Url-Scheme $scheme;
      proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
      proxy_set_header Host $http_host;
      proxy_redirect off;
      proxy_pass http://api_upstream;
     }
 }

in the same directory as your Nginx, create a Dockerfile which defines how to build a custom Nginx Docker Image:


# Use an official Nginx image as the base image
FROM nginx:latest
# Remove any existing config files
RUN rm /etc/nginx/conf.d/*
# Copy the custom Nginx configuration
COPY nginx.conf /etc/nginx/conf.d/
# Expose port 80 for Nginx
EXPOSE 80

Before testing in development, let’s update our docker-compose file by adding the Nginx service:


version: '3'
services:
  api:
    build:
      context: ./store
      dockerfile: Dockerfile
      command: gunicorn core.wsgi:application --bind 0.0.0.0:8000
    volumes:
      - static_volume:/store/staticfiles/
      - media_volume:/store/mediafiles/
    expose:
      - 80
  nginx:
    build:
      context: ./nginx
      dockerfile: Dockerfile
    ports:
      - 80:80
    volumes:
      - static_volume:/store/staticfiles/
      - media_volume:/store/mediafiles/
volumes:
  static_volume:
  media_volume:

Let updating core/settings.py and core/urls.py with STATIC_ROOT to ensure that all static files are gathered and ready to be served by the web server.

core/settings.py

and core/urls.py

For the purpose of testing if everything works, we’ll open a terminal and navigate to your root directory. Run the following command:

docker-compose up

Then let’s testing at http://localhost:80 or http://127.0.0.1:80

To remove the container and its associated resources, run:

docker-compose rm

Then, let’s create docker-compose.ci and docker-compose.prod files for the production and workflow purpose in the root directory of the project

docker-compose.ci

version: '3'
services:
  bookapi:
    build:
      context: ./store
      dockerfile: Dockerfile
      cache_from:
        - '${DJANGO_TUTORIAL_API_IMAGE}'
    image: '${DJANGO_TUTORIAL_API_IMAGE}'
    command: gunicorn core.wsgi:application --bind 0.0.0.0:8000
    expose:
      - 80
  nginx:
    build:
     context: ./nginx
     dockerfile: Dockerfile
    cache_from:
          - '${DJANGO_TUTORIAL_NGINX_IMAGE}'
      image: '${DJANGO_TUTORIAL_NGINX_IMAGE}'
    ports:
      - 80:80

docker-compose.prod

version: '3'
services:
  bookapi:
    container_name: 'django-api'
    image: '${DJANGO_TUTORIAL_API_IMAGE}'
    command: gunicorn core.wsgi:application --bind 0.0.0.0:8000
    volumes:
      - static_volume:/store/staticfiles/
      - media_volume:/store/mediafiles/
    expose:
      - 80
  nginx:
      container_name: 'nginx'
      image: '${DJANGO_TUTORIAL_NGINX_IMAGE}'
      ports:
        - 80:80
      volumes:
        - static_volume:/store/staticfiles/
        - media_volume:/store/mediafiles/
 volumes:
    static_volume:
    media_volume:

So for this tutorial will use Github packages for hosting the images.

GitHub Package Registry:

A package hosting service provided by GitHub. It allows you to publish, share, and manage software packages directly within your GitHub repositories.

4. AWS Setup

Let’s start by setting up an EC2 instance to deploy our application. To do this, and you’ll need to open an AWS account (if you don’t already have one).

If you don’t know how to set up AWS EC2 , please visit this link which will guide you through how to : launch EC2

Once we’ve finished configuring AWS EC2 and the instance is up and running, we can install Docker on it..

So connect via SSH into the instance using your Key Pair as :

$ ssh -i your-key-pair.pem ec2-user@<PUBLIC-IP-ADDRESS>

#example
# ssh -i ~./ssh/django-tutorial.perm ec2-user@51.20.75.55

After accessing the instance, start by updating the server package and installing the latest version of Docker and Docker-compose:

[ec2-user]$ sudo yum update -y
[ec2-user]$ sudo yum install -y docker
[ec2-user]$ sudo service docker start
[ec2-user]$ sudo curl -L https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m) -o /usr/local/bin/docker-compose
[ec2-user]$ sudo chmod +x /usr/local/bin/docker-compose

[ec2-user]$ docker --version
Docker version 20.10.23, build 7155243

[ec2-user]$ docker-compose --version
Docker Compose version v2.18.1

Add the ec2-user to the docker group so that you can use it without running the Docker command with sudo :

[ec2-user]$ sudo usermod -a -G docker ec2-user

Next, let’s generate the SSH KEY

[ec2-user]$ ssh-keygen -t rsa

Please! save the Key without setting any password

Next, copy the public key into the authorized_keys file and set the appropriate authorizations.:

[ec2-user]$ cat ~/.ssh/id_rsa.pub
# Copy this public key into ~/.ssh/authorized_keys
[ec2-user]$ vi ~/.ssh/authorized_keys
# After adding the public key into authorized_key then exits  from vi text editor
# Then add these commands are used to change the permissions of these files
[ec2-user]$ chmod 600 ~/.ssh/authorized_keys
[ec2-user]$ chmod 600 ~/.ssh/id_rsa

Now, copy the contents of the private key

[ec2-user]$ cat ~/.ssh/id_rsa

# Copy the private key somewhere for later use

4. Automating Deployment with GitHub Actions to AWS EC2

Github provide continuous integration and continuous deployment (CI / CD) platform.

Since our workflow will use GitHub’s core features (GitHub packages), GitHub requires you to create an access token, which will be used as a GitHub user ID for all your interactions.

Go to the Personal access tokens area in the Developer settings of your GitHub profile and click Generate new token.

Adding GitHub Actions directory

To configure GitHub Actions, start by adding a new directory called .github to the root of your project, then within this directory, add another directory called workflows . Now to configure the workflow, which is a set of one or more jobs, create a new file in the workflows directory called main.yml . You can also run the following command in your root directory.

mkdir .github && cd .github
mkdir workflows && cd workflows
touch main.yml

You can only fork the complete main.yml on this link :

main.yml

Notes about the secrets

secrets.PERSONAL_ACCESS_TOKEN
secrets.NAMESPACE
secrets.PRIVATE_KEY
secret.AWS_EC2_IP_ADDRESS
secrets.AWS_HOST_USER

All of the secrets need to be set in your repository’s secrets (Settings > Secrets) and use your Github username or your organization name as your NAMESPACE and your personal access token as PERSONAL_ACCESS_TOKEN .

Once you are done, commit and push your code to the Github to trigger the workflow to run, so now you should see the images in the Github Packages.

Workflow successfully finished :

Once all our jobs have been executed, navigate to the IP of your instance, and you should see the django-tutorial application running :

Let’s add the collectstatic files to the RestApi to solve the problem of STATIC_ROOT by ssh into AWS EC2 with the key pair created early then run:

#bookapi as service into the docker-compose.prod file
[ec2-user@ip-172-31-21-196 ~]$ docker-compose -f docker-compose.prod.yml exec bookapi sh
# it'll open the django sh 

/store # python manage.py collectstatic --no-input --clear

160 static files copied to '/store/staticfiles'.
/store # exit

[ec2-user@ip-172-31-21-196 ~]$

Then just reload the IP of your instance

👏 In summary, deploying a Django REST API on AWS EC2 using Docker, NGINX, Gunicorn and GitHub Actions offers a robust and scalable solution for hosting Django applications in a production environment. Automating the deployment process with GitHub Actions improves productivity and reduces the risk of errors, enabling seamless integration of code changes into the deployment pipeline.

If you would like to improve your skills, click on the following link on Master React App Deployment: AWS EC2, Route 53, SSL & GitHub Actions,A comprehensive manual which walks you through the deployment process, leveraging the power of Docker, AWS EC2, Route 53, SSL encryption and GitHub actions to build a robust CI/CD pipeline.

A comprehensive manual which walks you through the deployment process, leveraging the power of AWS EC2, Route 53, SSL encryption and GitHub actions to build a robust CI/CD pipeline.

If you found this article useful, you can support me by buying me a coffee ☕️. Your contribution fuels my efforts to create more relevant content and helps me to continue sharing my knowledge with the community. Also, feel free to fork the GitHub repository associated with this article and contribute your ideas, improvements or comments. Together, we can build a stronger ecosystem and enable developers to excel in deploying Django applications on AWS EC2. Don’t forget to give it a round of applause 👏 if you found it useful!

Deploy a Next.js App on AWS EC2 with Docker, NGINX, and Automate with GitHub Actions.

Kilama Elie — Mon, 21 Apr 2025 05:12:57 +0000

Next.js is a powerful React framework that provides server-side rendering, static site generation, and API routes. Deploying a Next.js app on an AWS EC2 instance allows for better control and customization of the hosting environment. This guide will walk you through the process of setting up a Next.js application on AWS EC2 using Docker and NGINX while automating deployment with GitHub Actions.

Before following this Next.js tutorial, we recommend that you first consult Build and Deploy a ReactJS App to AWS EC2 with Docker, NGINX, and Automate with GitHub Actions.. This guide is a continuation of the series on React deployment, focusing solely on Next.js-specific updates. The basic steps remain the same, and you can refer to the previous article for more details.

Table of Contents

Setting Up the Project
Dockerizing the NextJS application
Setting up NGINX
Automating deployment with GitHub **Actions on **AWS EC2

Note that you can find the link of final source code in the https://github.com/kilamaelie/Deploy-a-Next.js-App-on-AWS-EC2-with-Docker-NGINX-and-Automate-with-GitHub-Actions repository.

Step 1. Setting Up the Project

Open your terminal and navigate to the directory where you want to create the project, for the purpose of this tutorial we’ll create a directory with a name next-tutorial-app or you can name it anything depending on your project name. This folder is considered as the root directory for our application which contains all folders and files structures.

Then, we will change our directory into the next-tutorial-app folder and all other parts of configuration files or folders will be in this folder.

> mkdir next-tutorial-app
> cd next-tutorial-app

Inside the next-tutorial-app folder, to create a new Next project runs the following command using create-next-app@latest

npx create-next-app@latest # website is our app-name or folder name

Let’s change the directory into website as our app-name for NextJs application and start development server.

cd website # app-name
npm run dev # start the development server

*Step 2: Dockerizing the Next.js App
*

Create a Dockerfile in the root directory of your website folder:

FROM node:23.10.0-alpine

WORKDIR /app

COPY ./package*.json ./

# Install dependencies
RUN yarn

# Copy all files
COPY . .

# Expose the listening port
EXPOSE 3000

Then, create a .dockerignore file to exclude unnecessary files:

node_modules
.next
.DS_Store
dist

In your project’s root directory as next-tutorial-app, create a file named docker-compose.yml. This file will contain the configuration for your multi-container application.

services:
  website:
    build:
      context: ./website
      dockerfile: Dockerfile
    command: yarn dev
    ports:
      - "3000:3000"

So to test it, just go in your terminal, navigate to the directory containing the docker-compose.yml file and run:

docker-compose up

Kill the task in the terminal where docker-compose up is running in order to stop the. To remove the containers and their associated resources, run:

docker-compose rm -v

Step 3: Setting Up NGINX as a Reverse Proxy in Docker

In the root of your project directory, create a Nginx directory named nginx then create a configuration file named nginx.conf . So inside nginx.conf file adds the following configuration:

server {
     listen 80;
     server_name localhost; 
     #server_name can add your domain or Ip address for your server in the production

    location / {
        proxy_pass http://website:3000;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection 'upgrade';
        proxy_set_header Host $host;
        proxy_cache_bypass $http_upgrade;
     }
 }

in the same directory as your Nginx, create a Dockerfile which defines how to build a custom Nginx Docker Image:

# Use an official Nginx image as the base image
FROM nginx:latest

# Remove any existing config files
RUN rm /etc/nginx/conf.d/*
# Copy the custom Nginx configuration
COPY nginx.conf /etc/nginx/conf.d/
# Expose port 80 for Nginx
EXPOSE 80

Before testing in development, let’s update our docker-compose file by adding the Nginx service

services:
  website:
    build:
      context: ./website
      dockerfile: Dockerfile
    ports:
    - 3000:3000

  nginx:
    build:
     context: ./nginx
     dockerfile: Dockerfile
    ports:
      - "80:80"
    depends_on:
      - website

Also update the Dockerfile for the next app in the website directory

# Stage 1: Install Dependencies
ARG NODE=node:20-alpine 
FROM ${NODE} AS deps

RUN apk add --no-cache libc6-compat

WORKDIR /app

COPY package*.json ./
RUN npm install

# Stage 2: Build
FROM ${NODE} AS builder

WORKDIR /app

COPY --from=deps /app/node_modules ./node_modules
COPY . .

RUN npm run build

# Stage 3: Production Runner
FROM ${NODE} AS runner

WORKDIR /app

ENV NODE_ENV production
ENV PORT 3000
ENV NEXT_TELEMETRY_DISABLED 1

# Install PM2 globally
RUN npm install -g pm2

# Create a non-root user
RUN addgroup --system --gid 1001 nodegroup
RUN adduser --system --uid 1001 appuser

# Copy necessary files
COPY --from=builder /app/public ./public
COPY --from=builder /app/package.json ./package.json
COPY --from=builder --chown=appuser:nodegroup /app/.next/standalone ./
COPY --from=builder --chown=appuser:nodegroup /app/.next/static ./.next/static

USER appuser

EXPOSE 3000

# Start the application using PM2
CMD ["pm2-runtime", "node", "--", "server.js"]

Before testing, ensure that output: 'standalone' is set in the next.config.js file to optimize the build for Docker deployment.

To test if everything works, open a terminal, navigate to your root directory, and run the following command:

docker-compose up  #

Then let’s testing athttp://localhost:80 or http://127.0.0.1:80

Kill the task in the terminal

docker-compose rm -v

Then, let’s create docker-compose.ci and docker-compose.prod files for the production and workflow purpose in the root directory of the project

Docker-compose.ci

services:
  website:
    container_name: website
    build:
      context: ./website
      dockerfile: Dockerfile
      cache_from:
        - '${NEXT_APP_WEBSITE_IMAGE}'
    image: '${NEXT_APP_WEBSITE_IMAGE}'
    env_file:
      - ./.env
    ports:
      - '3000:3000'

  nginx:
    container_name: nginx
    build:
      context: ./nginx
      dockerfile: Dockerfile
      cache_from:
        - '${NEXT_APP_NGINX_IMAGE}'
    image: '${NEXT_APP_NGINX_IMAGE}'
    ports:
      - '80:80'
    restart: always
    depends_on:
      - website

Docker-compose.prod

services:
  website:
    container_name: website
    image: '${NEXT_APP_WEBSITE_IMAGE}'
    env_file: .env
    ports:
      - '3000:3000'

  nginx:
    container_name: nginx
    image: '${NEXT_APP_NGINX_IMAGE}'
    ports:
      - '80:80'
    depends_on:
      - website
    restart: always

So for this tutorial will use Github packages for hosting the images.

Step 4: Automating Deployment with GitHub Actions

Create a GitHub Actions workflow file at .github/workflows/deploy.yml in the root of the project.

main.yml

Please make sure your on main branch because the workflow’s can only trigger on the main branch other you can it to your preferences.

Workflow successfully finished :

🚀 👏💻 By following these steps, you have successfully deployed a Next.js application on AWS EC2 using Docker and NGINX, with automated deployments using GitHub Actions. You can further enhance this setup by integrating SSL certificates with Let’s Encrypt and using AWS services like RDS or S3 for storage.

Keep exploring, stay curious and keep building on this foundation to create even more sophisticated and powerful projects. Happy coding!

Thanks for reading through and I hope you liked what you read here. Feel free to connect with me on LinkedIn, twitter and GitHub.

Build and Deploy a ReactJS App to AWS EC2 with Docker, NGINX, and Automate with GitHub Actions.

Kilama Elie — Fri, 05 Jan 2024 20:40:00 +0000

In today’s digital landscape, master the art of building, deploying and automating ReactJS Apps using Docker, NGINX and GitHub actions on AWS EC2. Dive into this comprehensive guide and transform your coding journey into an adventure of innovation, efficiency and cutting-edge technologies.

Table of Contents

Prerequisites
Setting Up the Project
Dockerizing the ReactJS application
Setting up NGINX
Automating deployment with GitHub Actions on AWS EC2

1. Prerequisites
Before diving into this project, which involves several technologies and concepts, you need a solid grounding in the following areas to ensure successful learning and implementation such as:

Basic Web development: A good understanding of HTML, CSS and JavaScript is essential, as ReactJS is a JavaScript library for building user interfaces.

Git and version control: A good knowledge of Git and version control concepts will help you manage your code base and collaborate effectively.

Docker: Understanding containerization for docker is crucial to packaging your application and its dependencies in isolated containers.

NGINX: A basic knowledge of NGINX as a web server and reverse proxy will be necessary to set up the server environment.

Remember, while these requirements are essentials, you don’t need to be an expert in every area. You can learn and develop your expertise as you go along. What’s more, official documentation, online tutorials and communities can provide invaluable support as you learn.

Note that you can find the link of final source code in the build and deploy-reactApp to-aws ec2 via github-actions repository.

2. Setting Up the Project
This involves several steps which including the installation, project structure and other basic configuration, here’s a step-by-step guide helping you get started :

Install Node.js and npm:
Before you begin, make sure Node.js and npm ( Node Package Managers) are installed on your system. You can download them from the official website:
https://nodejs.org.
Create a New Project:
Open your terminal and navigate to the directory where you want to create the project, for the purpose of this tutorial we’ll create a directory with a name react-tutorial-app or you can name it anything depending on your project name. This folder is considered as the root directory for our application which contains all folders and files structures.

In software development, “project structure” provides a clear and logical layout that helps developers understand and navigate the codebase more easily. A well-designed project structure promotes maintainability, collaboration, and efficient development.

Then, we will change our directory into the react-tutorial-appfolder and all other parts of configuration files or folders will be in this folder.

> mkdir react-tutorial-app
> cd react-tutorial-app

Inside the react-tutorial-app folder, to create a new React project runs the following command using create-react-appand for more details on how create-react-app works and other staff, please visit the official website: https://create-react-app.dev/

npx create-react-app your-app-name # website is our app-name or folder name

create-react-app is a command-line tool that allows you to quickly and easily set up a new Reactjs Application, this boilerplate setup generates a basic project structure, including essential files and configuration needed to start building a React application .

Let’s change the directory into website as our app-name for React application.

cd website # app-name

To start the development server and preview your React app, use the following command:

npm start

This command will initiate the development server, which will compile your React code, bundle assets, and start a local web server to serve your application. It will also open your app in your default web browser automatically.

Once the development server is up and running, you can access your React app by visiting http://localhost:3000 in your web browser. So well done 👏 for creating a React application and setting up the project.

3. Dockerizing the ReactJS application
Docker provides a standardized way to package and distribute applications, making it easier to deploy and manage across different environments. In this section, we’ll containerize our ReactJS application using Docker.

If you haven’t already, install Docker on your machine by following the instructions for your operating system: https://docs.docker.com/get-docker/

In your React app’s root directory, create a file named Dockerfile. This file defines the instructions to build your Docker image.

# Use an official Node.js runtime as the base image

FROM node:19.6.0-alpine
# Set the working directory within the container
WORKDIR /website

# Copy package.json and package-lock.json to the container
COPY package*.json ./

# Install dependencies
RUN npm install

# Copy the rest of the application code to the container
COPY . .

# Build the React app
EXPOSE 3000

Since in this project will have more than one container , then we ‘ll need to use docker-compose

Docker-compose : allows you to define and run multiple Docker containers as a single service, making it easier to manage complex applications.

If you haven’t already, you’ll need to install Docker Compose. Follow the installation instructions for your operating system: https://docs.docker.com/compose/install/

In your project’s root directory as react-tutorial-app, create a file named docker-compose.yml. This file will contain the configuration for your multi-container application.

version: '3'
services:
  website:
    build:
      context: ./website
      dockerfile: Dockerfile
    command: npm start
    ports:
      - "3000:3000"

This defines only one service website but a bit later more will be added. So in order to test it, just go in your terminal, navigate to the directory containing the docker-compose.yml file and run:

docker-compose up

Docker Compose will build the required images and start the defined containers. You’ll see logs from both services in the terminal and also depending on your configuration, you can access your ReactJS app at http://localhost:3000 . Kill the task in the terminal where docker-compose upis running in order to stop the. To remove the containers and their associated resources, run:

docker-compose rm -v

4. Setting up NGINX
NGINX is a reverse proxy to serve the built static files of your React application. This setup is commonly used to improve performance and manage routing in production environments.

In the root of your project directory, create a Nginx directory named nginx then create a configuration file named nginx.conf . So inside nginx.conf file adds the following configuration:

server {
     listen 80;
     server_name localhost; 
     #server_name can add your domain or Ip address for your server in the production

     location / {
         root /usr/share/nginx/html/website;
         try_files $uri $uri/ /index.html;
     }
 }

In the same directory as your Nginx, create a Dockerfile which defines how to build a custom Nginx Docker Image:

# Use an official Nginx image as the base image
FROM nginx:latest

# Remove any existing config files
RUN rm /etc/nginx/conf.d/*

# Copy the custom Nginx configuration
COPY nginx.conf /etc/nginx/conf.d/

# Expose port 80 for Nginx
EXPOSE 80

Before testing in development, let’s update our docker-composefile by adding the Nginx service

version: '3'
services:
website:
  build:
    context: ./website
    dockerfile: Dockerfile
  volumes:
   - website:/website/build/  # build files will be added at this directory !!!

nginx:
  build:
   context: ./nginx
   dockerfile: Dockerfile
  ports:
    - "80:80"
  volumes:
     - website:/usr/share/nginx/html/website # Copy React App's build files to the Nginx directory

volumes:
   website:

And also update the Dockerfile for the react app in the website directory

# Use an official Node.js runtime as the base image

FROM node:19.6.0-alpine
# Set the working directory within the container
WORKDIR /website

# Copy package.json and package-lock.json to the container
COPY package*.json ./

# Install dependencies
RUN npm install

# Copy the rest of the application code to the container
COPY . .

# Build the React app
RUN npm run build

# Build the React app
EXPOSE 3000

For the purpose of testing if everything works, we’ll open a terminal and navigate to your root directory. Run the following command:

docker-compose up  #

Then let’s testing at http://localhost:80 or http://127.0.0.1:80

Kill the task in the terminal where the container is running. To remove the container and its associated resources, run:

docker-compose rm -v

Then, let’s create docker-compose.ci and docker-compose.prodfiles for the production and workflow purpose in the root directory of the project

Docker-compose.ci

version: '3'
services:
  website:
    build:
      context: ./website
      dockerfile: Dockerfile
    cache_from:
        - '${WEBSITE_IMAGE}'
    image: '${WEBSITE_IMAGE}'
    ports:
      - "3000:3000"
    volumes:
      - website:./website/build/  # build files will be added at this directory !!!

  nginx:
    build:
     context: ./nginx
     dockerfile: Dockerfile
    cache_from:
        - '${NGINX_IMAGE}'
    image: '${NGINX_IMAGE}'
    ports:
      - "80:80"
    volumes:
      - website:/usr/share/nginx/html/website # Copy React App's build files to the Nginx directory

Docker-compose.prod

version: '3'
services:
  website:
    container_name: 'website'
    image: '${WEBSITE_IMAGE}'
    ports:
      - "3000:3000"
    volumes:
      - website:./website/build/  # build files will be added at this directory !!!

  nginx:
    container_name: 'nginx'
    image: '${NGINX_IMAGE}'
    ports:
      - "80:80"
    volumes:
      - website:/usr/share/nginx/html/website # Copy React App's build files to the Nginx directory

volumes:
  website:

So for this tutorial will use Github packages for hosting the images.

GitHub Package Registry is a package hosting service provided by GitHub. It allows you to publish, share, and manage software packages directly within your GitHub repositories.

It lets you host your software, either publicly or privately, for use in your projects on GitHub.

AWS Setup

Let’s start by setting up an EC2 instance to deploy our application. To do this, and you’ll need to open an AWS account (if you don’t already have one).

If you don’t know how to set up AWS EC2 , please visit this link which will guide you through how to : launch EC2

Once we’ve finished configuring AWS EC2 and the instance is up and running, we can install Docker on it.

So connect via SSH into the instance using your Key Pair as :

$ ssh -i your-key-pair.pem ec2-user@<PUBLIC-IP-ADDRESS>

#example 
# ssh -i ~./ssh/react-tutorial.perm ec2-user@54.201.189.94

After accessing the instance, start by updating the server package and installing the latest version of Docker and Docker-compose:

[ec2-user]$ sudo yum update -y
[ec2-user]$ sudo yum install -y docker
[ec2-user]$ sudo service docker start
[ec2-user]$ sudo curl -L https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m) -o /usr/local/bin/docker-compose
[ec2-user]$ sudo chmod +x /usr/local/bin/docker-compose

[ec2-user]$ docker --version
Docker version 20.10.23, build 7155243

[ec2-user]$ docker-compose --version
Docker Compose version v2.18.1

Add the ec2-user to the docker group so that you can use it without running the Docker command with sudo :

[ec2-user]$ sudo usermod -a -G docker ec2-user

Next, let’s generate the SSH KEY

[ec2-user]$ ssh-keygen -t rsa

Please! save the Key without setting any password

Next, copy the public key into the authorized_keys file and set the appropriate authorizations.:

[ec2-user]$ cat ~/.ssh/id_rsa.pub 
# Copy this public key into ~/.ssh/authorized_keys
[ec2-user]$ vi ~/.ssh/authorized_keys
# After adding the public key into authorized_key then exits  from vi text editor 
# Then add these commands are used to change the permissions of these files 
[ec2-user]$ chmod 600 ~/.ssh/authorized_keys
[ec2-user]$ chmod 600 ~/.ssh/id_rsa

Now, copy the contents of the private key

[ec2-user]$ cat ~/.ssh/id_rsa

# Copy the private key somewhere for later use

*5. Automating Deployment with GitHub Actions to AWS EC2
*

It is an integrated continuous integration and continuous deployment (CI / CD) platform provided by GitHub .

Since our workflow will use GitHub’s core features (GitHub packages), GitHub requires you to create an access token, which will be used as a GitHub user ID for all your interactions.

Go to the Personal access tokens area in the Developer settings of your GitHub profile and click Generate new token.

mkdir .github && cd .github
mkdir workflows && cd workflows
touch main.yml

main.yml

So, in the main.yml we define :

set the environment variables
Three jobs to run:

a. build job consist of:

Set various environment variables
Login in the Github package by using the PERSONAL_ACCESS_TOKEN created
Pull images for caching, build the images and push to Github Registry

b. checking-secret consist of :

checking if the deploy variables are existing

Notes about the secrets

secrets.PERSONAL_ACCESS_TOKEN
secrets.NAMESPACE
secrets.PRIVATE_KEY
secret.AWS_EC2_IP_ADDRESS
secrets.AWS_HOST_USER

All of the secrets need to be set in your repository’s secrets (Settings > Secrets) and use your Github username or your organization name as your NAMESPACE and your personal access token as PERSONAL_ACCESS_TOKEN .

c. deploy job consist of:

Add the environment variable to .env file which will be created on the AWS EC2 instance.
Add the private key to ssh-agent for password less.
Deploy the build the images on AWS EC2 instance.

Once you are done, commit and push your code to the Github to trigger the workflow to run, so now you should see the images in the Github Packages.

Please make sure your on main branch because the workflow’s can only trigger on the main branch other you can it to your preferences.

Workflow successfully finished :

Once all our jobs have been executed, navigate to the IP of your instance, and you should see the React application running :

👏 And there you have it, this guide has equipped you with the skills you need to successfully build, deploy and automate ReactJS applications using Docker, NGINX and GitHub Actions on AWS EC2. Keep exploring, stay curious and keep building on this foundation to create even more sophisticated and powerful projects. Happy coding!

Thanks for reading through and I hope you liked what you read here. Feel free to connect with me on LinkedIn, twitter and GitHub.