Why should you care?

Product analytics and age demographics need histograms, not raw row dumps.

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: "A", price: 500 },
  { name: "B", price: 1500 },
  { name: "C", price: 7000 },
  { name: "D", price: 2500 }
])
db.products.aggregate([
  { $bucket: {
      groupBy: "$price",
      boundaries: [0, 1000, 5000, 10000],
      default: "other",
      output: { count: { $sum: 1 }, products: { $push: "$name" } }
  }}
])

Run Example »

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

What happened?

• Prices fall into [0,1000), [1000,5000), [5000,10000).
• Each bucket outputs count and product names.
• default catches values outside boundaries.

Practice next

1. Run the $bucket pipeline.
2. Change boundaries and re-run.
3. Try $bucketAuto with buckets: 3.
4. Bucket orders by total for AOV bands.
5. Output avgPrice with $avg.

Remember

$bucket builds histograms. boundaries define ranges. output customizes bucket fields.

Price band assortment

Category managers see how many SKUs sit in budget vs premium bands.

Outcome: Assortment gaps become visible quickly.