In this guide, you can learn how to use PyMongo 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.
To create a time series collection, pass the following arguments to the create_collection() method:
Name of the new collection to create
timeseries argument
The timeseries argument is of type dict. It contains the following fields:
timeField: Specifies the field that stores a timestamp in each time series document.
metaField: Specifies the field that stores metadata in each time series document.
granularity: Specifies the approximate time between consecutive timestamps. The possible values are 'seconds', 'minutes', and 'hours'.
bucketMaxSpanSeconds: Sets the maximum time between timestamps in the same bucket.
bucketRoundingSeconds: Sets the number of seconds to round down by when MongoDB sets the minimum timestamp for a new bucket. Must be equal to bucketMaxSpanSeconds.
See Command Fields to learn more about these fields.
The following example creates a time series collection named october2024 with the timeField option set to "timestamp". Select the Synchronous or Asynchronous tab to see the corresponding code:
database = client.get_database("weather")time_series_options = { "timeField": "timestamp"}database.create_collection("october2024", timeseries=time_series_options)database = client.get_database("weather")time_series_options = { "timeField": "timestamp"}await database.create_collection("october2024", timeseries=time_series_options)To check if you successfully created the collection, you can get a list of all collections in your database and filter by collection name. Select the Synchronous or Asynchronous tab to see the corresponding code:
print(list(database.list_collections(filter={'name': 'october2024'}))){ "name": "october2024", "type": "timeseries", "options": { "timeseries": { "timeField": "timestamp", "granularity": "seconds", "bucketMaxSpanSeconds": 3600 } }, "info": { "readOnly": False }}collections = await database.list_collections(filter={'name': 'october2024'})print(await collections.to_list()){ "name": "october2024", "type": "timeseries", "options": { "timeseries": { "timeField": "timestamp", "granularity": "seconds", "bucketMaxSpanSeconds": 3600 } }, "info": { "readOnly": False }}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, see Insert Documents.
This example inserts New York City temperature data into the october2024 time series collection created in Create a Time Series Collection. Each document contains the following fields:
temperature, which stores temperature measurements in degrees Fahrenheit
location, which stores location metadata
timestamp, which stores the measurement timestamp
Select the Synchronous or Asynchronous tab to see the corresponding code:
from datetime import datetimecollection = database["october2024"]document_list = [ { "temperature": 77, "location": "New York City", "timestamp": datetime(2024, 10, 22, 6, 0, 0) }, { "temperature": 74, "location": "New York City", "timestamp": datetime(2024, 10, 23, 6, 0, 0) }]collection.insert_many(document_list)from datetime import datetimecollection = database["october2024"]document_list = [ { "temperature": 77, "location": "New York City", "timestamp": datetime(2024, 10, 22, 6, 0, 0) }, { "temperature": 74, "location": "New York City", "timestamp": datetime(2024, 10, 23, 6, 0, 0) }]await collection.insert_many(document_list)To learn more about using datetime objects in PyMongo, see Dates and Times.
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 Query and Transform Your Data with Aggregation.
To learn more about the concepts in this guide, see the following MongoDB Server manual entries:
To learn more about the methods mentioned in this guide, see the following API documentation:
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