Why should you care?

Factories and fleets emit endless points. Without time series + indexes, storage and query costs explode.

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.devices.insertOne({ _id: "sensor-42", site: "Pune-Plant-1", firmware: "1.4.2" })
db.createCollection("telemetry", {
  timeseries: { timeField: "ts", metaField: "deviceId", granularity: "seconds" }
})
db.telemetry.insertOne({ ts: new Date(), deviceId: "sensor-42", temp: 31.5, humidity: 60 })
db.telemetry.aggregate([
  { $match: { deviceId: "sensor-42", ts: { $gte: ISODate("2026-07-19T00:00:00Z") } } },
  { $group: { _id: { $dateTrunc: { date: "$ts", unit: "minute" } }, avgTemp: { $avg: "$temp" } } },
  { $sort: { _id: 1 } }
])
db.alerts.insertOne({ deviceId: "sensor-42", type: "TEMP_HIGH", value: 31.5, at: new Date() })

Run Example »

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

What happened?

• devices holds metadata.
• telemetry is time series keyed by deviceId.
• Aggregation builds per-minute averages for charts.
• alerts captures threshold breaches for ops paging.

Practice next

1. Create devices + telemetry.
2. Insert a stream of points.
3. Run the minute-average pipeline.
4. Add $max humidity per hour.
5. TTL or offline archive old telemetry per policy.

Remember

Time series for telemetry. Separate device metadata. Aggregate for dashboards; alert on thresholds.

Plant heat monitors

Pune factory watches machine temps; alerts page on-call when thresholds break.

Outcome: Downtime drops because spikes are visible in minutes.