Time-series collections in MongoDB - Mini Project: Build & Apply

Create and Use a Time-Series Collection in MongoDB

📖 Scenario: You are managing a small weather station that records temperature and humidity every hour. You want to store this data efficiently using MongoDB's time-series collections.

🎯 Goal: Build a time-series collection in MongoDB to store hourly weather data with timestamps, then insert and query the data.

📋 What You'll Learn

Create a time-series collection named weatherData with time as the time field

Set the meta field to location to store the station location

Insert three sample documents with exact timestamps and values

Query the collection to find all records for the location StationA

💡 Why This Matters

🌍 Real World

Time-series collections are used in IoT, monitoring, and logging systems to efficiently store and query data that changes over time.

💼 Career

Understanding time-series collections is important for database administrators and backend developers working with real-time data and analytics.

1

Create the time-series collection

Use the db.createCollection command to create a time-series collection called weatherData with time as the time field and location as the meta field.

MongoDB

# Your code here to create the time-series collection

Need a hint?

Use db.createCollection with the timeseries option specifying timeField and metaField.

2

Insert sample weather data

Insert three documents into weatherData with these exact fields: time (ISODate), location (string), temperature (number), and humidity (number). Use these exact values: 2024-06-01T10:00:00Z, StationA, 22.5, 60; 2024-06-01T11:00:00Z, StationA, 23.0, 58; 2024-06-01T12:00:00Z, StationA, 24.0, 55.

MongoDB

db.createCollection("weatherData", {
  timeseries: {
    timeField: "time",
    metaField: "location"
  }
})

# Your code here to insert three documents

Need a hint?

Use insertMany with an array of documents. Use ISODate for the time field.

3

Query data for StationA

Write a query using find on weatherData to get all documents where location is exactly "StationA".

MongoDB

db.createCollection("weatherData", {
  timeseries: {
    timeField: "time",
    metaField: "location"
  }
})

db.weatherData.insertMany([
  { time: ISODate("2024-06-01T10:00:00Z"), location: "StationA", temperature: 22.5, humidity: 60 },
  { time: ISODate("2024-06-01T11:00:00Z"), location: "StationA", temperature: 23.0, humidity: 58 },
  { time: ISODate("2024-06-01T12:00:00Z"), location: "StationA", temperature: 24.0, humidity: 55 }
])

# Your code here to query documents for StationA

Need a hint?

Use find with a filter object specifying location: "StationA".

4

Create an index on the time field

Create an index on the time field in the weatherData collection to optimize queries by time.

MongoDB

db.createCollection("weatherData", {
  timeseries: {
    timeField: "time",
    metaField: "location"
  }
})

db.weatherData.insertMany([
  { time: ISODate("2024-06-01T10:00:00Z"), location: "StationA", temperature: 22.5, humidity: 60 },
  { time: ISODate("2024-06-01T11:00:00Z"), location: "StationA", temperature: 23.0, humidity: 58 },
  { time: ISODate("2024-06-01T12:00:00Z"), location: "StationA", temperature: 24.0, humidity: 55 }
])

const stationARecords = db.weatherData.find({ location: "StationA" })

# Your code here to create an index on the time field

Need a hint?

Use createIndex on the time field with ascending order 1.