Why should you care?

Product catalogs have brand-specific specs — TVs have refreshRate, shoes have sizeUK. Sparse fields waste indexes; attributes keep one flexible shape.

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.insertMany([
  {
    name: "4K TV",
    attrs: [
      { k: "refreshHz", v: 120 },
      { k: "sizeIn", v: 55 }
    ]
  },
  {
    name: "Running Shoes",
    attrs: [
      { k: "sizeUK", v: 9 },
      { k: "color", v: "blue" }
    ]
  }
])
db.products.createIndex({ "attrs.k": 1, "attrs.v": 1 })
db.products.find({ attrs: { $elemMatch: { k: "refreshHz", v: { $gte: 100 } } } })

Run Example »

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

What happened?

• Both products share attrs[].
• The index supports lookups by key/value.
• $elemMatch finds TVs with refreshHz ≥ 100 without requiring a dedicated refreshHz field on shoes.

Practice next

1. Insert the two products.
2. Create the attrs index.
3. Query shoes with sizeUK 9.
4. Find color blue across products.
5. Project only name and attrs.

Remember

Store sparse specs as {k,v} arrays. Index attrs.k and attrs.v. Promote hot fields out of attrs.

Amazon-like hardlines catalog

Each category brings new spec keys every quarter.

Outcome: Engineering ships filters on new attrs without ALTER-style migrations.