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Specialized Data Formats

Time Series Data

On this page

  • Overview
  • Create a Time Series Collection
  • Store Time Series Data
  • Query Time Series Data
  • Additional Information

Overview

In this guide, you can learn how to use the Kotlin Sync driver to store and interact with time series data.

Time series data is composed of the following components:

  • Measured quantity

  • Timestamp for the measurement

  • Metadata that describes the measurement

The following table describes sample situations for which you could store time series data:

Situation
Measured Quantity
Metadata
Recording monthly sales by industry
Revenue in USD
Company, country
Tracking weather changes
Precipitation level
Location, sensor type
Recording fluctuations in housing prices
Monthly rent price
Location, currency

Create a Time Series Collection

Important

Server Version for Time Series Collections

To create and interact with time series collections, you must be connected to a deployment running MongoDB Server 5.0 or later.

You can create a time series collection to store time series data. To create a time series collection, pass the following parameters to the createCollection() method:

  • The name of the new collection to create

  • A CreateCollectionOptions object with the TimeSeriesOptions set using the timeSeriesOptions() method

Example

This example creates the october2024 time series collection in the fall_weather database with the timeField option set to the "timestamp" field:

val database = mongoClient.getDatabase("fall_weather")
val tsOptions = TimeSeriesOptions("timestamp")
val collectionOptions = CreateCollectionOptions().timeSeriesOptions(tsOptions)
database.createCollection("october2024", collectionOptions)

To verify that you successfully created the time series collection, run the listCollections() method on the database and print the results:

val results = database.listCollections()
val jsonSettings = JsonWriterSettings.builder().indent(true).build()
results.forEach { result ->
    println(result.toJson(jsonSettings))
}
 {
   "name": "october2024",
   "type": "timeseries",
   "options": {
     "timeseries": {
       "timeField": "temperature",
       "granularity": "seconds",
       "bucketMaxSpanSeconds": 3600
     }
   },
   "info": {
     "readOnly": false
   }
}
...

Store Time Series Data

You can insert data into a time series collection by using the insertOne() or insertMany() methods and specifying the measurement, timestamp, and metadata in each inserted document.

Tip

To learn more about inserting documents into a collection, see the Insert Documents guide.

Example

This example inserts New York City temperature data into the october2024 time series collection created in the Create a Time Series Collection example. Each document contains the following fields:

  • temperature, which stores temperature measurements in degrees Fahrenheit

  • location, which stores location metadata

  • timestamp, which stores the time of the measurement collection

val collection = database.getCollection<Document>("october2024")
// Temperature data for October 1, 2024
val temperature1 = Document("temperature", 54)
                    .append("location", "New York City")
                    .append("timestamp", Date(1727755200000))
// Temperature data for October 2, 2024
val temperature2 = Document("temperature", 55)
                    .append("location", "New York City")
                    .append("timestamp", Date(1727841600000))
collection.insertMany(listOf(temperature1, temperature2))

Query Time Series Data

You can use the same syntax and conventions to query data stored in a time series collection as you use when performing read or aggregation operations on other collections. To learn more about these operations, see the Additional Information section.

Additional Information

To learn more about the concepts mentioned in this guide, see the following MongoDB Server manual entries:

To learn more about performing read operations, see Read Data from MongoDB.

To learn more about performing aggregation operations, see the Transform Your Data with Aggregation guide.

API Documentation

To learn more about the methods mentioned in this guide, see the following API documentation:

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