Web Analytics Event Service

A backend and frontend system to collect, store, and analyze user interaction events (view, click, location) using Node.js, Express.js, and MongoDB.

Setup Instructions (Windows)

1. Install Requirements

• Node.js (v18+ recommended)
• MongoDB (local instance)

Verify installation:

node -v
npm -v
mongod --version

Start MongoDB service if it's not running:

mongod 

2. Clone Repository:

git clone https://github.com/DEVESH1709/Web-Analytics-Event-Service.g
cd <repo-folder>

4. Backend Setup:

cd backend
npm install 

Create a .env file:

MONGO_URI
PORT

4. Generate Sample Data:

node scripts/generateData.js

6. Start Backend Server:

npm start

8. Frontend Setup:

cd ../frontend
npx http-server .

--> API Endpoints

Description	Create a new event
Method	POST
Content-Type	application/json

Body Example (for click):

{
  "user_id": "user123",
  "event_type": "click",
  "timestamp": "2025-05-20T12:00:00Z",
  "payload": {
    "element_id": "btn-login",
    "text": "Login",
    "xpath": "/html/body/button"
  }
}

Success: 201 Created Errors: 400 Bad Request, 500 Internal Server Error

GET /analytics/event-counts

Description	Get total event count (with filters)
Method	GET

Query Parameters (optional):

1-event_type=view

2-start_date=2025-05-01

3-end_date=2025-05-29

GET /analytics/event-counts-by-type

Description	Get count grouped by event_type
Method	GET

Query Parameters (optional):

1-start_date=2025-05-01 2-end_date=2025-05-29

 GET /analytics/event-counts-by-type?start_date=2025-05-01&end_date=2025-05-29

[
  { "event_type": "view", "count": 2100 },
  { "event_type": "click", "count": 1200 },
  { "event_type": "location", "count": 800 }
]

--> Technologies Used

Tech	Purpose
Node.js	Server-side JavaScript runtime
Express.js	Web framework for routing/API
MongoDB	NoSQL database
Mongoose	ODM for MongoDB schema/queries
Faker.js	Data generation for testing
HTML/JS	Frontend UI and event triggers
ServiceWorker	Async background event posting
Morgan	Logging HTTP requests

--> Database Schema (events) :

{
  user_id: String,
  event_type: "view" | "click" | "location",
  timestamp: Date,
  payload: Object
}

1- Indexed fields: event_type, timestamp

2- payload varies based on event type

3- timestamp used for date filtering

--> Testing with Postman

1- POST /events: Send valid JSON events

2- GET /analytics/event-counts: Try with and without filters

3- GET /analytics/event-counts-by-type: View grouped results

--> Challenges Faced

1- Handling flexible payload validation

2- Ensuring accurate UTC timestamps

3- MongoDB aggregation with dynamic filters

4- IP whitelisting for Atlas (initially)

--> Future Improvements

1- Auth/API keys for secure tracking

2- Analytics dashboard with charts

3- Caching frequent queries (Redis)

4- Batch event API support

5- Geolocation clustering with geospatial indexing

6-Cloud deployment with Docker + CI/CD

7- Service Worker scope and async fetch handling