Why should you care?

If you $group millions of docs first, you waste CPU. $match on tenantId and date first keeps pipelines cheap.

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.orders.aggregate([
  { $match: {
      tenantId: "acme",
      status: { $in: ["paid", "shipped"] },
      placedAt: { $gte: ISODate("2026-07-01") }
  }},
  { $count: "matched" }
])

Run Example »

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

What happened?

• Only Acme paid/shipped orders since July pass.
• $count returns how many matched.
• Indexes on tenantId + placedAt help this $match use IXSCAN.

Practice next

1. Seed orders with tenantId and placedAt.
2. Run the $match + $count pipeline.
3. explain with db.orders.aggregate([...], { explain: true }) if available in your version.
4. Add total: { $gte: 1000 } to $match.
5. Match nested "address.city": "Pune".

Remember

$match filters like find. Put it early in pipelines. Index the $match fields.

Tenant-scoped analytics

Every report pipeline starts with $match on tenantId.

Outcome: No cross-tenant data ever enters the group stage.