Why should you care?

Flipkart-scale browsing needs flexible specs, fast filters, and correct historical prices on invoices.

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.products.insertOne({
  sku: "TV-55-4K",
  name: "55 inch 4K TV",
  category: "electronics",
  price: 42999,
  attrs: [{ k: "refreshHz", v: 120 }, { k: "os", v: "AndroidTV" }],
  stock: 25,
  active: true
})
db.products.createIndex({ category: 1, price: 1 })
db.products.createIndex({ "attrs.k": 1, "attrs.v": 1 })
db.products.find({ category: "electronics", price: { $lte: 50000 }, active: true }).sort({ price: 1 })
db.orders.insertOne({
  items: [{ sku: "TV-55-4K", name: "55 inch 4K TV", unitPrice: 42999, qty: 1 }],
  total: 42999,
  status: "placed"
})

Run Example »

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

What happened?

• products uses attribute pattern for specs and compound indexes for browse.
• orders snapshots name/price so later catalog edits do not rewrite history.
• active filters hide drafts.

Practice next

1. Insert 5 products across categories.
2. Query a price-filtered category list.
3. Place an order with snapshots.
4. Add Atlas Search on name/description.
5. updateMany stock decrements carefully with $inc.

Remember

Flexible product docs + browse indexes. Snapshot prices on orders. Attrs handle sparse specs.

Festival electronics aisle

Shoppers filter TVs under ₹50,000 with 120Hz panels via attrs.

Outcome: Catalog API returns the right grid with stable invoice history.