Why should you care?

Sales by category, messages per room, and average rating per course all need grouping.

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([
  { category: "audio", price: 1000 },
  { category: "audio", price: 3000 },
  { category: "storage", price: 4000 }
])
db.products.aggregate([
  { $group: {
      _id: "$category",
      avgPrice: { $avg: "$price" },
      count: { $sum: 1 },
      prices: { $push: "$price" }
  }}
])

Run Example »

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

What happened?

• _id: "$category" creates one bucket per category.
• $avg computes mean price.
• $sum: 1 counts docs.
• $push builds an array of prices — useful but can get large.

Practice next

1. Run the pipeline and read both buckets.
2. Try _id: null to aggregate the whole collection into one row.
3. Use $max and $min on price.
4. Group orders by { tenantId: "$tenantId", status: "$status" }.
5. Replace $push with $addToSet.

Remember

$group creates buckets via _id. Accumulators compute metrics. Keep $push arrays bounded.

Category average price report

Merchandising checks avg selling price per category weekly.

Outcome: $group returns one row per category for the sheet.