Why should you care?

IoT, markets, and CPU metrics write insane volumes. Time series collections reduce storage and speed range queries versus naive one-doc-per-event designs.

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.createCollection("readings", {
  timeseries: {
    timeField: "ts",
    metaField: "deviceId",
    granularity: "seconds"
  }
})
db.readings.insertMany([
  { ts: ISODate("2026-07-19T10:00:01Z"), deviceId: "sensor-42", temp: 28.1 },
  { ts: ISODate("2026-07-19T10:00:02Z"), deviceId: "sensor-42", temp: 28.2 }
])
db.readings.find({ deviceId: "sensor-42", ts: { $gte: ISODate("2026-07-19T10:00:00Z") } })

Run Example »

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

What happened?

• createCollection declares ts as the time field and deviceId as metadata.
• Inserts look normal.
• Queries by device and time range are the sweet spot.
• You do not manually bucket as often as the old pattern required.

Practice next

1. Create a readings time series collection.
2. Insert a burst of points.
3. Query a time window for one device.
4. Set granularity to minutes for coarser data.
5. Aggregate avg temp per minute with $group.

Remember

Time series collections optimize timestamped metrics. Define timeField and metaField. Best for IoT and monitoring.

Cold-chain truck sensors

Pharma trucks stream temperatures every second into readings.

Outcome: Compliance charts load quickly over multi-day windows.