MongoDB Time Series Data
MongoDB allows you to store and process time series data at scale. Learn how to store and analyze your time series data using a MongoDB cluster.
FAQs
Time series data are measurements taken at time intervals from one or more sources. When analyzed, the individual data pieces form a meaningful insight over a period of time. Examples are weather measurement data and stock trading data.
MongoDB is a general purpose document database that has native support for time series data. MongoDB's time series collections are optimized and purpose-built for ingesting, storing, and querying time series data. Therefore, users can use a single unified Query API utilizing MongoDB as a time series database alongside other database use cases.
MongoDB 5.0 has an optimized time series collection type which is designed to efficiently store and consume time series data. Prior to version 5.0, MongoDB had a suggested data model for time series data. You can read more here
MongoDB time series collections are available in all 5.0+ versions of MongoDB including Community, Enterprise Advanced, and Atlas.
There are multiple ways to use time series data. For example, you can consume time series data to perform calculations using aggregation pipelines and plot graphs on the application side, via MongoDB Charts. This makes MongoDB and MongoDB Atlas a compelling store for large volumes of time series data.
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