Udagram is a simple cloud application developed alongside the Udacity Cloud Developer Nanodegree. It allows users to register and log into a web client and post photos to the feed.

The project is split into two parts:

1. Frontend - Angular web application built with Ionic Framework
2. Backend RESTful API - Node-Express application

Image Courtesy

1. /feed and /user backends are separated into independent microservices.
2. The project includes Dockerfiles for each project to successfully create Docker images for /feed, /user (backend), frontend and reverse-proxy

The project includes Travis CI pipeline to automatically push the Docker images of the microservices into Dockerhub

1. The Microservices are deployed using Kubernetes cluster on AWS.
2. Uses the NGINX reverse proxy to redirect the request to the correct microservice (/feed or /users) based on path based load-balancing.
3. The Kubernetes pods are replicted for self-healing.
4. The Kubernetes deployments are autoscaled with CPU metrics threshold.

The backend API logging is improved using request IDs.

Create a PostgreSQL database either locally or on AWS RDS. Set the config values for environment variables prefixed with POSTGRES_.

Create an AWS S3 bucket. Set the config values for environment variables prefixed with AWS_

• To download all the package dependencies, run the command from the directory udagram-api/:

  npm install .

• To run the application locally, run:

  npm run dev

• You can visit http://localhost:8080/api/v0/feed in your web browser to verify that the application is running. You should see a JSON payload.

• To download all the package dependencies, run the command from the directory udagram-frontend/:

  npm install .

• Install Ionic Framework's Command Line tools for us to build and run the application:

  npm install -g ionic

• Prepare your application by compiling them into static files.

  ionic build

• Run the application locally using files created from the ionic build command.

  ionic serve

• You can visit http://localhost:8100 in your web browser to verify that the application is running. You should see a web interface.

The deployment files for pods and services are available here. These files can be applied on the K8S cluster using

kubectl -f <file.yml>