In this guide, you can learn how to use the C++ 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
Important Server Version for Time Series CollectionsTo 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, perform the following actions:
Create a BSON document that specifies the properties of your time series collection.
Call the create_collection() method and pass the collection name and the time series BSON document as arguments.
This example creates the sept2023 time series collection in the precipitation database with the following configuration:
timeField is set to "timestamp"
metaField is set to "location"
granularity is set to "minutes"
auto db = client["precipitation"];auto ts_info = make_document( kvp("timeseries", make_document( kvp("timeField", "timestamp"), kvp("metaField", "location"), kvp("granularity", "minutes"))));auto collection = db.create_collection("sept2023", ts_info.view());To verify that you successfully created the time series collection, run the list_collections() method on the database and print the results:
auto cursor = db.list_collections();for(auto&& doc : cursor) { std::cout << bsoncxx::to_json(doc) << std::endl;}{ "name" : "sept2023", "type" : "timeseries", "options" : { "timeseries" :{ "timeField" : "timestamp", "metaField" : "location", "granularity" :"minutes", "bucketMaxSpanSeconds" : 86400 } }, "info" : ... }You can insert data into a time series collection by using the insert_one() or insert_many() methods and specifying the measurement, timestamp, and metadata in each inserted document.
To learn more about inserting documents into a collection, see the Insert Documents guide.
This example inserts New York City precipitation data into the sept2023 time series collection created in the Create a Time Series Collection example. Each document contains the following fields:
precipitation_mm, which stores precipitation measurements in millimeters
location, which stores location metadata
timestamp, which stores the time of the measurement collection
auto collection = db["sept2023"];std::vector<bsoncxx::document::value> ts_data;ts_data.push_back(make_document(kvp("precipitation_mm", 0.5), kvp("location", "New York City"), kvp("timestamp", bsoncxx::types::b_date{std::chrono::milliseconds{1694829060000}})));ts_data.push_back(make_document(kvp("precipitation_mm", 2.8), kvp("location", "New York City"), kvp("timestamp", bsoncxx::types::b_date{std::chrono::milliseconds{1695594780000}})));auto result = collection.insert_many(ts_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 find more information about these operations, see the Additional Information section.
To learn more about the concepts mentioned in this guide, see the following Server manual entries:
To learn more about performing read operations, see Read Data.
To learn more about performing aggregation operations, see the Transform Your Data with Aggregation guide.
To learn more about the methods mentioned in this guide, see the following API documentation:
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