MLA Fitness App

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 and Health Tracking functionality uses the MERN stack (MongoDB, Express.js, React, Node.js), the Analytics 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, distance, level of effort and date
• Log health data including height, weight, heart rate and blood pressure
• User focused dashboard showing key stats
• See more detailed weekly and overall statistics for exercises
• Interactive UI with Material-UI components
• Real-time data persistence with MongoDB

Prerequisites

• Node.js
• MongoDB
• npm or yarn
• Python Flask
• Java 8 (all already installed in the devcontainer)

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.

Building entire project with Docker (+ starting containers up)

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]

Development without using Docker-Compose

Running Node.js Activity Tracker

cd activity-tracking
npm install
nodemon server

Running Flask application

cd analytics
flask run -h localhost -p 5050

Running Java application

cd authservice
./gradlew clean build
./gradlew bootRun

Start the Frontend

cd frontend
npm install
npm start

spin up MongoDB without docker-compose:

docker run --name mongodb -d -p 27017:27017 -v mongodbdata:/data/db mongo:latest

Connect to MongoDB

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

show registered activities:

db.exercises.find()

show registered users:

db.users.find()

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.

Monitoring & Analytics:

1. Start Services

docker-compose up -d

2. Access Grafana

Open your web browser and go to http://localhost:3000. Log in with the default username admin and the password you set in the docker-compose.yml file.

3. Configure Prometheus as a Data Source

Once logged into Grafana, follow these steps to add Prometheus as a data source: Click on the gear icon on the left panel to open the Configuration menu. Select "Data Sources." Click on the "Add data source" button. Choose "Prometheus" as the data source type. Set the URL to http://prometheus:9090. Click "Save & Test" to ensure that Grafana can connect to your Prometheus instance.

4. Create Dashboards and Panels

Click on Dashboards and then on Create new Dashboard". Click "Add new visualisation." From the panel editor, select the Prometheus data source from the drop-down menu. Write your Prometheus query to fetch the metrics you want to visualise. Customize your panel with the visualisation options provided by Grafana. Save the panel and dashboard when you're done.

5. Explore Metrics

Use the Explore feature in Grafana to experiment with queries against your Prometheus data source and see the results in real time.

6. Set Up Alerts (Optional)

Grafana can also be used to set up alerts based on specific conditions within your dashboard panels. To set up alerts, edit a panel and go to the "Alert" tab to define alert rules.