MLA Fitness App

• MLA Fitness App
  • Introduction
  • Current Features
  • Building entire project with Docker Compose
    • Start existing containers (no rebuild of images)
      • Spinning up a single service
      • Shutting down a service
  • Running Unit Tests
    • Goals
    • Analytics
    • Activity Tracking
    • Auth Service
    • Frontend
  • MongoDB
    • spin up MongoDB without docker-compose:
    • Spin up MongoDB with docker-compose:
      • Connect to MongoDB
  • Development without using Docker-Compose
    • Running Node.js Activity Tracker
    • Running Flask application
      • Analytics
      • Goals
    • Running Java application
    • Start the Frontend
  • Development in Github Codespaces
    • Starting a new Devcontainer
    • Check needed packages are installed:
  • Deployment

Introduction

A simple and interactive fitness tracking application built with multiple microservices and programming languages. This application allows users to track their exercises and monitor their progress over time.

The Activity Tracking functionality uses the MERN stack (MongoDB, Express.js, React, Node.js), the Analytics service uses Python/Flask, the Goals service uses Python/Flask, and the Authentication Microservice using Java.

Current Features

• User registration for personalized tracking
• Log various types of exercises with descriptions, duration, and date
• See weekly and overall statistics
• Set and track goals
• Interactive UI with Material-UI components
• Real-time data persistence with MongoDB

Building entire project with Docker Compose

docker-compose up --build

Start existing containers (no rebuild of images)

docker-compose up

Spinning up a single service

docker-compose up [servicename]

Shutting down a service

docker-compose down [servicename]

Running Unit Tests

Goals

1. It is recommended to use a virtual environment to avoid conflicts with other Python projects. In order to know which Python version to use for the virtual environment, check the Dockerfile in the goals directory. Once you have an environment set up and activated, run the following command inside the goals directory:

   pip install -r requirements.txt

2. Then run the following command:

   python -m unittest discover ./tests

Analytics

1. It is recommended to use a virtual environment to avoid conflicts with other Python projects. In order to know which Python version to use for the virtual environment, check the Dockerfile in the analytics directory. Once you have an environment set up and activated, run the following command inside the analytics directory:

   pip install -r requirements.txt

2. Then run the following command:

   python -m unittest discover ./tests

Activity Tracking

The application uses a Node.js server for activity tracking. If you decide to spin up the microservices individually you will need to first make sure you are located in the activity tracking directory:

cd activity-tracking

Then you will need to run the following command first to bypass conflicting dependencies in the package.json :

npm install  --legacy-peer-deps

After running this command you can start running the activity tracking unit tests by running the following command:

npm run test -- --passWithNoTests

To generate an activity tracking code coverage report, which will be available to view in the folder location activity-tracking/coverage/cobertura-coverage.xml, run the following command:

npm run test -- --coverage --coverageDirectory=./coverage --coverageReporters=cobertura

Auth Service

To run unit tests for the auth service you can open IntellJ and run the test by right clicking on the test folder. This will run all the tests. In order to see the coverage you can also run "Test with Coverage"

Example of test coverage so far below:

Frontend

The application uses a React frontend. If you decide to spin up the microservices individually you will need to first make sure you are located in the frontend directory:

cd frontend

Then you will need to run the following command first to bypass conflicting dependencies in the package.json :

npm install  --legacy-peer-deps

After running this command you can start running the frontend unit tests by running the following command:

npm run test

To generate a frontend code coverage report, which will be available to view in the folder location frontend/coverage/cobertura-coverage.xml, run the following command:

npm run test -- --coverage --coverageDirectory=./coverage --coverageReporters=cobertura

MongoDB

The application uses mongoDB as the database. If you decide to spin up the microservices individually, you will still need to have a mongoDB instance running. There are many ways to install mongoDB; for example, you can install it on your local machine or use docker to spin up a mongoDB container.

This instance will need to be set up with credentials that match the configuration in the application. If not, you can change the environment variables in the docker-compose file and the microservice's configuration to match your mongoDB instance.

spin up MongoDB without docker-compose:

docker run --name mongodb -d -p 27017:27017 -v mongodbdata:/data/db -e MONGO_INITDB_ROOT_USERNAME=root MONGO_INITDB_ROOT_PASSWORD=cfgmla23 mongo:latest

Spin up MongoDB with docker-compose:

docker-compose up mongodb

Connect to MongoDB

mongosh -u root -p cfgmla23 --authenticationDatabase admin --host localhost --port 27017

Development without using Docker-Compose

Running Node.js Activity Tracker

cd activity-tracking
npm install
nodemon server

Running Flask application

It is recommended that you use a Python virtual environment to avoid conflict with other Python projects. You can check which Python version is needed in the Dockerfile for each of the microservices.

Analytics

cd analytics

pip install -r requirements.txt

flask run -h localhost -p 5050

Goals

cd goals

pip install -r requirements.txt

flask run -h localhost -p 8000

Running Java application

cd authservice
./gradlew clean build
./gradlew bootRun

Start the Frontend

cd frontend
npm install
npm start

Development in Github Codespaces

Starting a new Devcontainer

1. Click on "Code"

2. Switch to the "Codespaces" tab

3. Create new Codespace from main

4. Open Codespace in VS code for best experience:

Walktrough:

https://docs.github.com/en/codespaces/developing-in-a-codespace/using-github-codespaces-in-visual-studio-code

Check needed packages are installed:

sh .devcontainer/check-installation.sh

expected output:

Checking installations...
node is /usr/local/bin/node
node is installed with version: v18.16.0
npm is /usr/local/bin/npm
npm is installed with version: 9.5.1
python3 is /usr/bin/python3
python3 is installed with version: Python 3.9.2
pip3 is /usr/bin/pip3
pip3 is installed with version: pip 20.3.4 from /usr/lib/python3/dist-packages/pip (python 3.9)
gradle is /usr/bin/gradle
gradle is installed with version:
------------------------------------------------------------
Gradle 4.4.1
------------------------------------------------------------
......
Done checking installations.

if you're missing any version, please contact your course administrator.

Deployment

The application is containerized using Docker and can be deployed on any platform that supports Docker containers. For AWS deployment, a GitHub Actions pipeline is configured for CI/CD.