Deploying Applications with Docker on AWS Using GitHub Actions

Modern applications require automated deployment pipelines to streamline DevOps workflows. In this guide, we will deploy a Dockerized application to AWS using GitHub Actions, implementing CI/CD for development, staging, and production environments. Additionally, we will integrate feature flags for controlled releases.

Prerequisites

Before we begin, ensure you have the following:

• An AWS account
• Docker installed locally
• A GitHub repository
• AWS CLI configured with IAM credentials
• A container registry (Amazon Elastic Container Registry - ECR)
• A running ECS cluster (Elastic Container Service) or EC2 instance

Step 1: Dockerizing the Application

Create a Dockerfile in your project root:

FROM node:18-alpine
WORKDIR /app
COPY package.json .
RUN npm install
COPY . .
CMD ["npm", "start"]
EXPOSE 3000

Build and Test Locally

docker build -t my-app .
docker run -p 3000:3000 my-app

Step 2: Setting Up AWS ECR

Create an ECR repository:

aws ecr create-repository --repository-name my-app --region us-east-1

Authenticate Docker with ECR:

aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com

Step 3: GitHub Actions CI/CD Pipeline

Create a .github/workflows/deploy.yml file:

name: Deploy to AWS

on:
  push:
    branches:
      - main
      - staging
      - develop

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v2

      - name: Authenticate with AWS
        run: |
          aws configure set aws_access_key_id ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws configure set aws_secret_access_key ${{ secrets.AWS_SECRET_ACCESS_KEY }}

      - name: Login to Amazon ECR
        run: |
          aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com

      - name: Build and Push Docker Image
        run: |
          IMAGE_URI=<AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com/my-app:${{ github.sha }}
          docker build -t $IMAGE_URI .
          docker push $IMAGE_URI

      - name: Deploy to ECS
        run: |
          aws ecs update-service --cluster my-cluster --service my-service --force-new-deployment

Step 4: Feature Flags Implementation

For feature flagging, we can use LaunchDarkly, Unleash, or custom environment variables. Example using LaunchDarkly:

import { initialize } from 'launchdarkly-node-client-sdk';

const ldClient = initialize('LAUNCHDARKLY_SDK_KEY', { key: 'user-key' });

ldClient.waitForInitialization().then(() => {
  const isFeatureEnabled = ldClient.variation('new-feature', false);
  console.log('Feature Enabled:', isFeatureEnabled);
});

Set environment variables in GitHub Actions:

      - name: Set Environment Variables
        run: echo "LAUNCHDARKLY_SDK_KEY=${{ secrets.LAUNCHDARKLY_SDK_KEY }}" >> $GITHUB_ENV

Step 5: Configuring Multi-Environment Pipelines

Modify .github/workflows/deploy.yml to handle different branches:

  deploy:
    runs-on: ubuntu-latest
    environment: ${{ github.ref == 'refs/heads/main' && 'production' || github.ref == 'refs/heads/staging' && 'staging' || 'development' }}

Store environment-specific configurations in GitHub Environments:

• Development: develop
• Staging: staging
• Production: main

Conclusion

By following this guide, we have:

• Dockerized our application
• Pushed images to AWS ECR
• Set up GitHub Actions for automated deployment
• Used feature flags for controlled releases
• Configured multi-environment pipelines

This setup ensures scalable, efficient, and controlled deployments with feature flagging for seamless updates.