https://github.com/ozzie-code-alt/link-shortner-dockerized
Link shortener and analytics dashboard built with Next.js, TypeScript, Prisma, and Docker. Features click tracking, geolocation analytics, and a one-command setup.
https://github.com/ozzie-code-alt/link-shortner-dockerized
analytics docker fullstack link-shortener nextjs postgresql prisma rest-api tailwindcss typescript url-shortener
Last synced: 4 months ago
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Link shortener and analytics dashboard built with Next.js, TypeScript, Prisma, and Docker. Features click tracking, geolocation analytics, and a one-command setup.
- Host: GitHub
- URL: https://github.com/ozzie-code-alt/link-shortner-dockerized
- Owner: Ozzie-code-alt
- Created: 2025-08-05T04:13:10.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2025-08-05T04:26:55.000Z (11 months ago)
- Last Synced: 2025-08-05T06:22:12.735Z (11 months ago)
- Topics: analytics, docker, fullstack, link-shortener, nextjs, postgresql, prisma, rest-api, tailwindcss, typescript, url-shortener
- Language: TypeScript
- Homepage:
- Size: 70.3 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Link Shortener & Analytics Dashboard
A full-stack link shortening service built with Next.js, TypeScript, and Prisma. This application allows users to convert long URLs into short, manageable links and provides a detailed analytics dashboard to track click-through performance. The entire application is containerized with Docker for easy setup and deployment.
---
## Features
- **Shorten URLs:** Convert any long URL into a unique, 6-character short link.
- **Link Redirection:** Fast and reliable redirection from the short link to the original URL.
- **Click Analytics:** Every click is tracked and recorded.
- **Analytics Dashboard:** A detailed view for each link, showing:
- Total click count.
- Timeline of click events.
- Geographical data (clicks by country).
- Referrer information (user agent).
- **Fully Typed:** Built with TypeScript from end to end for robust, error-free code.
- **Containerized:** Includes `Dockerfile` and `docker-compose.yml` for a one-command setup.
## Tech Stack
- **Framework:** [Next.js](https://nextjs.org/) (App Router)
- **Language:** [TypeScript](https://www.typescriptlang.org/)
- **Styling:** [Tailwind CSS](https://tailwindcss.com/)
- **Database ORM:** [Prisma](https://www.prisma.io/)
- **Database:** [PostgreSQL](https://www.postgresql.org/)
- **Containerization:** [Docker](https://www.docker.com/) & Docker Compose
## Getting Started
Follow these instructions to get the project up and running on your local machine for development and testing.
### Prerequisites
You must have the following software installed:
- [Node.js](https://nodejs.org/) (v18 or later)
- [Docker](https://www.docker.com/products/docker-desktop/) and Docker Compose
### 1. Running with Docker (Recommended)
This is the easiest way to get the entire stack (Next.js app + PostgreSQL database) running.
1. **Clone the repository:**
```bash
git clone [https://github.com/your-username/link-shortener.git](https://github.com/your-username/link-shortener.git)
cd link-shortener
```
2. **Create an environment file:**
Copy the example environment file. The default values are configured to work with Docker Compose out of the box.
```bash
cp .env.example .env.local
```
3. **Build and run the containers:**
```bash
docker-compose up --build
```
This command will build the Next.js image, pull the official Postgres image, and start both services. The app will be available at `http://localhost:3000`.
### 2. Running Locally (Without Docker)
1. **Clone the repository:**
```bash
git clone [https://github.com/your-username/link-shortener.git](https://github.com/your-username/link-shortener.git)
cd link-shortener
```
2. **Install dependencies:**
```bash
npm install
```
3. **Set up the Database:**
Make sure you have a PostgreSQL server running. Create an `.env.local` file and add your database connection string:
```env
# .env.local
DATABASE_URL="postgresql://USER:PASSWORD@HOST:PORT/DATABASE?schema=public"
```
4. **Run Prisma migrations:**
This will create the necessary tables (`Link`, `Click`) in your database.
```bash
npx prisma migrate dev
```
5. **Run the development server:**
```bash
npm run dev
```
The application will be available at `http://localhost:3000`.
---
## Future Scope & AI Integrations
This project has a solid foundation that can be extended with many powerful features.
### Standard Features
- **User Authentication:** Implement NextAuth.js or Clerk to allow users to sign up, log in, and manage their own links.
- **Custom Slugs:** Allow authenticated users to choose their own custom slugs (e.g., `sh.rt/my-portfolio`).
- **QR Code Generation:** Automatically generate a downloadable QR code for every shortened link.
- **Link Expiration:** Allow users to set an expiration date for their links.
### AI-Powered Integrations
- **🤖 Malicious Link Detection:**
- Integrate an AI service (or a custom-trained model) that analyzes the `originalUrl` upon submission.
- The model could classify URLs as "safe," "suspicious," or "malicious" based on patterns and known threats, preventing the platform from being used for phishing or malware distribution.
- **📈 Predictive Click Analytics:**
- Use the historical click data to train a time-series forecasting model.
- The dashboard could display predictions like, "This link is projected to receive **~200 more clicks** in the next 7 days," providing valuable insights for marketing campaigns.
- **📝 AI-Generated Link Summaries:**
- When a user pastes a link, an AI agent could crawl the destination page in the background.
- Using a Large Language Model (LLM) like Gemini, it could generate a concise, one-sentence summary of the page's content. This summary would be stored and displayed on the dashboard, reminding the user what the link is about.
- **🚨 Anomaly Detection for Bot Traffic:**
- Develop a system that analyzes incoming click patterns in real-time.
- An AI model could learn what "normal" traffic looks like for a link and automatically flag anomalies—such as thousands of clicks from a single IP address in a few seconds—as likely bot activity, helping to keep analytics clean and accurate.