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Time Series.
Build faster, gain insight, reduce cost.

Build and run data-intensive analytical applications by combining the flexibility of the document model with time series collections.
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TIME SERIES OVERVIEW
Optimized for the demands of analytical and IoT applications, MongoDB Time Series collections offer reliable data ingestion, a columnar storage format, and fast query processing. This cost-effective solution is designed to meet the most demanding requirements for performance and scale.
View documentation
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Build time series apps faster

Simplify and accelerate app development with native time series collections that automatically handle the complexities and challenges of time series data, without the need for extra instrumentation by developers. This means faster time to market and a better developer experience.
Fully managed with Atlas
Multi-cloud data distribution
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Analytical insights, simplified

Discovering insights and patterns from your time series data is easier with the unified Query API that includes rich window functions and temporal operators for common and complex analytical queries.
Learn about the Query API
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A streamlined time series experience

Seamlessly manage the entire time series data lifecycle – ingest, storage, analysis, visualization, and archive. There's no need to worry about performance or scalability since columnar storage and compression optimize for query speed and cost efficiency, even as data grows over time.
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Reduce complexity and cost

Eliminate costly, specialized databases that lead to complex data silos, data movement, and operational overhead. Instead, efficiently and securely manage both time series and operational data within a single versatile, general-purpose database.
Developer data platform
Feature overview
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Native time series collections

Store time series data in an optimized columnar format, reducing storage and I/O demands for greater performance and scale.

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Columnar storage format

Dramatically reduce your database storage footprint by more than 90% with columnar storage format and best-in-class compression algorithms.

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Faster queries

Optimize your queries with compound secondary indexes on all fields to achieve faster queries at scale.

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Full data lifecycle management

Support the entire time series data lifecycle from ingest, storage, analysis, and visualization to archiving.

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Real-time analytics

Use window functions to calculate moving averages and sums over flexible time-based windows.

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Gap filling and densification

Handle missing data points using specialized gap filling and densification functions.

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Fine-grained data modification

Ability to freely modify the data with updates and deletes giving you more flexibility and control.

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Scale horizontally

Horizontally distribute large data sets to reduce latency and comply with data sovereignty regulations.

Get started with
time series

Effortlessly handle large volumes of data with a cost-effective solution designed to meet the most demanding requirements of time series data.
View Documentation
Time series collections
Automatically store time series data in a specialized columnar format optimized for high storage efficiency, reduced I/O and low latency queries.
Read the docs
Window functions
Unlock insights faster with the unified and expressive Query API, leveraging Window Functions and Temporal Operators.
Learn about the MongoDB Query API
Data densification
Handle missing or uneven data with densification and gap-filling functions. Perform analytics and ensure correctness by eliminating gaps in time or filling in missing values with methods like linear interpolation.
View aggregation pipeline operators
Time series collections
Automatically store time series data in a specialized columnar format optimized for high storage efficiency, reduced I/O and low latency queries.
Read the docs
JSON
Window functions
Unlock insights faster with the unified and expressive Query API, leveraging Window Functions and Temporal Operators.
Learn about the MongoDB Query API
CRUD
Data densification
Handle missing or uneven data with densification and gap-filling functions. Perform analytics and ensure correctness by eliminating gaps in time or filling in missing values with methods like linear interpolation.
View aggregation pipeline operators
Shell
JSON

Deliver insights from time series data

Find out how to leverage time series data to create great application experiences.
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MongoDB time series collections
Learn more about the new time series collections and how you can start building time series
applications today.
Learn More
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Turning time series data into value

Learn how Digitread Connect converts industrial IoT data into leading-edge insight with MongoDB Time Series

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Currency analysis with time series collections

Read the three-part blog on how to build a currency analysis platform with MongoDB
time series

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Kafka Connector supports Time Series

Our Kafka Connector now supports time series

Get the most out of Atlas

Build and run applications like IoT and financial analytics with MongoDB native time series capabilities.
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Query API

Perform analytics on your time series collections using the unified, expressive Query API to easily uncover insights and patterns.

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Charts

Create graphs from times series collections and embed visualizations into your applications for a rich user experience.

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Online Archive

Automatically tier aged data out of the database and into cloud object storage without losing access to it or dealing with ETL pipelines.

Build time series applications on MongoDB

Natively support the entire time series data lifecycle – from ingestion, storage, querying, real-time analysis, and visualization to online archiving.
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MONGODB NATIVE TIME SERIES
  • Time series collections
  • Columnar compression
  • Time series queries & analytics
  • Automated data lifecycle
  • Support for updates & deletes
  • Sharding support