GrubSync

An intelligent, group‑centric restaurant recommender that helps friends to find the best compromise when choosing where to eat. GrubSync collects each member’s cuisine preferences, dietary restrictions, budget, and real‑time location, then processes those events at scale with Redis Streams, Dask, and MongoDB to surface curated dining options.

Features

User Authentication: Sign up, log in, JWT‑based sessions

Group Management: Create groups, invite codes, join by code

Preference Collection: Submit/update cuisines, dietary restrictions, budget

Real‑Time Pipeline: Redis Streams → Streamz + Dask → low‑latency serving

Batch Analytics & ML: Nightly Dask job trains a ranking model on full history

Responsive UI: React + Vite frontend styled with Tailwind CSS

Tech Stack

Frontend: React (Vite), TypeScript, Tailwind CSS

Backend: Node.js + Express, MongoDB via Mongoose

Event Broker: Redis Streams

Streaming & Batch Compute: Dask Distributed, Streamz, Pandas

ML & Analytics: scikit‑learn, Dask DataFrame

Orchestration: cron or Airflow/Prefect for nightly jobs

Dev Tools: Docker Compose, ESLint, Prettier

Demo Video

Check out the demo video of GrubSync in action:

Watch the Demo

Prerequisites

Docker & Docker Compose

Node.js ≥ 16.x & npm

Python 3.8+ & pip

MongoDB instance (local or Atlas)

Redis instance (local or managed)

Docker Setup (Local Dev)

Create a docker-compose.yml in the repo root:

docker-compose up -d

Getting Started

1. Clone the Repository

   git clone https://github.com/siri1404/GrubSync-BigData.git
   cd GrubSync-BigData

2. Configure Environment
   Update the following variables in the .env file:

   MONGODB_URI=mongodb+srv://<user>:<pass>@cluster0.mongodb.net/grubsync?retryWrites=true&w=majority
   JWT_SECRET=your_jwt_secret
   REDIS_URL=redis://localhost:6379
   DASK_SCHEDULER=tcp://localhost:8786
   YELP_API_KEY=your_yelp_api_key
   GOOGLE_API_KEY=your_google_maps_key
   
   # Server
   PORT=3001
   
   # Batch training output
   MODEL_OUTPUT=./models

3. Install Dependencies
   Install dependencies for both the backend and frontend:

   npm install

   Install Python dependencies for the pipeline:

   pip install -r requirements.txt

4. Running Services

   • Backend API
     Start the backend server:

     cd server
     npm run dev

     The backend will be available at: http://localhost:3001

   • Frontend App
     Start the frontend application:

     cd src
     npm run dev

     The frontend will be available at: http://localhost:5173