Project 2: Swiggy Clone — Restaurant Listing API

Swiggy's most important feature is finding restaurants near you. We'll implement pincode-based search using MongoDB indexes.

Pincode search without index (slow — scans every document):

// ❌ SLOW — Full collection scan!
// MongoDB checks EVERY document one by one
const restaurants = await Restaurant.find({ 'address.pincode': '500001' });
// With 1 million restaurants, this checks all 1 million! 😭

Pincode search WITH index (fast):

// ✅ FAST — Uses B-tree index!
// MongoDB uses the index to jump directly to matching documents
// Add index: restaurantSchema.index({ 'address.pincode': 1 });
const restaurants = await Restaurant.find({ 'address.pincode': '500001' });
// With 1 million restaurants, this finds matches in MILLISECONDS! 🚀

Complete search controller:

// controllers/restaurant.controller.js — Search & Listing
import Restaurant from '../models/Restaurant.js';
import AppError from '../utils/AppError.js';
import asyncHandler from '../utils/asyncHandler.js';

// GET /api/restaurants?pincode=500001&cuisine=Biryani&rating=4
export const searchRestaurants = asyncHandler(async (req, res) => {
  const { pincode, cuisine, rating, search, page = 1, limit = 10, sort = '-rating' } = req.query;

  // Build query filter
  const query = {};

  // Text search on restaurant name
  if (search) {
    query.$text = { $search: search };
  }

  // Filter by pincode (with index!)
  if (pincode) {
    query['address.pincode'] = pincode;
  }

  // Filter by cuisine (array contains)
  if (cuisine) {
    query.cuisine = { $in: [cuisine] };
  }

  // Filter by minimum rating
  if (rating) {
    query.rating = { $gte: parseFloat(rating) };
  }

  // Only open restaurants
  query.isOpen = true;

  const restaurants = await Restaurant.find(query)
    .populate('reviews') // Populate reviews
    .skip((page - 1) * limit)
    .limit(parseInt(limit))
    .sort(sort);

  const total = await Restaurant.countDocuments(query);

  res.json({
    success: true,
    count: restaurants.length,
    total,
    page: parseInt(page),
    totalPages: Math.ceil(total / parseInt(limit)),
    data: restaurants
  });
});

// GET /api/restaurants/nearby?lng=78.46&lat=17.38&maxDistance=5000
export const getNearbyRestaurants = asyncHandler(async (req, res) => {
  const { lng, lat, maxDistance = 5000 } = req.query;

  if (!lng || !lat) {
    throw new AppError('Longitude and latitude required! Coordinates daalo bhai! 📍', 400);
  }

  const restaurants = await Restaurant.find({
    'address.location': {
      $near: {
        $geometry: {
          type: 'Point',
          coordinates: [parseFloat(lng), parseFloat(lat)]
        },
        $maxDistance: parseInt(maxDistance) // In meters
      }
    },
    isOpen: true
  }).populate('reviews');

  res.json({
    success: true,
    count: restaurants.length,
    data: restaurants
  });
});

How indexes work: An index is like the index of a book. Without an index, you read EVERY page to find what you need. With an index, you jump directly to the right page. MongoDB uses B-tree indexes which find data in O(log n) time — even with millions of documents!