Why should you care?

Food delivery, cab apps, and store locators need “restaurants near me” — geospatial queries are built for that.

See it live — copy this example

Open mongosh or MongoDB Compass, select database nosqlverse, then run the example. Change one field and run again.

db.stores.insertMany([
  { name: "Cafe A", location: { type: "Point", coordinates: [77.5946, 12.9716] } }, // Bengaluru
  { name: "Cafe B", location: { type: "Point", coordinates: [72.8777, 19.0760] } }  // Mumbai
])
db.stores.createIndex({ location: "2dsphere" })
db.stores.find({
  location: {
    $near: {
      $geometry: { type: "Point", coordinates: [77.6, 12.97] },
      $maxDistance: 5000
    }
  }
})

Run Example »

Edit the code below and click Run to see the result in Toolliyo’s live editor.

What happened?

• coordinates are [longitude, latitude] — easy to swap by mistake.
• 2dsphere index supports $near.
• The query returns cafes within 5000 meters of a Bengaluru point — Cafe A.

Practice next

1. Insert the two stores.
2. Create 2dsphere index.
3. Run $near from Bengaluru.
4. Add category and compound index if needed carefully.
5. Use $maxDistance: 1000 for a tighter radius.

Remember

2dsphere indexes GeoJSON locations. $near finds closest places. Coordinates are [lng, lat].

Swiggy restaurant radius

App sends user coordinates; API finds open restaurants within X km.

Outcome: Only deliverable kitchens appear in the list.