Why MongoDB for Time Series
Easier
Quickly bring new apps to market with native time series collections which automatically optimize your schema.
Faster
Gain faster insights, higher performance and scale with automatic indexing, rich window functions, and compressed storage footprint.
Lower Cost
Eliminate data movement and blend time series with the rest of your enterprise data in a single application data platform.
Prior to MongoDB 5.0, the Bosch IoT Insights cloud service relied on a custom solution with its own bespoke data model for time series data. This approach added complexity and friction to both our own developers and to our customers”, said Erwin Segerer, Bosch.IO. “MongoDB 5.0 and its time series collections radically simplifies our technology stack and improves user experience. IoT data is automatically stored in a highly optimized format that reduces storage consumption while also enabling fast and efficient queries and analytics against the data. As a result users unlock insights faster, no matter if it is time series or non time series data, all while working with a single intuitive and powerful query API
Erwin Segerer, Bosch.IO
After utilizing MongoDB’s native time series collections in our applications for monitoring subsea robots, we have found what we consider a state-of-the-art database platform for IoT data. Utilizing out-of-the-box functionality for optimized memory consumption, rapid query performance, and excellent aggregation pipeline operators that help us analyze data and understand trends improves our delivery performance for our Industrial IoT solutions.
Christoffer Lange, CEO at Digitread ConnectPrior to MongoDB 5.0, the Bosch IoT Insights cloud service relied on a custom solution with its own bespoke data model for time series data. This approach added complexity and friction to both our own developers and to our customers”, said Erwin Segerer, Bosch.IO. “MongoDB 5.0 and its time series collections radically simplifies our technology stack and improves user experience. IoT data is automatically stored in a highly optimized format that reduces storage consumption while also enabling fast and efficient queries and analytics against the data. As a result users unlock insights faster, no matter if it is time series or non time series data, all while working with a single intuitive and powerful query API
Erwin Segerer, Bosch.IOAfter utilizing MongoDB’s native time series collections in our applications for monitoring subsea robots, we have found what we consider a state-of-the-art database platform for IoT data. Utilizing out-of-the-box functionality for optimized memory consumption, rapid query performance, and excellent aggregation pipeline operators that help us analyze data and understand trends improves our delivery performance for our Industrial IoT solutions.
Christoffer Lange, CEO at Digitread Connect
As developers continually look to improve their velocity, they are increasingly looking for versatile platforms that allow them to offload operational responsibilities. Whether it’s adding native time series capability or introducing live resharding, MongoDB is striving to offer developers what they want.
Stephen O’Grady, Principal Analyst with RedMonk
IoT data can be complex. IoT platforms and their applications have to handle diverse, rapidly changing data at an extreme scale. We have been a partner of MongoDB’s for over five years, working to help solve this incredibly important challenge with our Cumulocity IoT platform which turns data from customers’ connected devices into immediate value. We’re now excited to be able to handle time-series natively within the MongoDB platform. We believe these new developments will help organisations explore IoT data more effectively and efficiently, as the majority of IoT data is time-series, namely measurements produced by sensors over time. With this new innovation, we can better support the storage and analysis of such data, leading to better applications and better outcomes for customers.
Dr. Jürgen Krämer, General Manager, IoT & Analytics at SoftwareAGHow Time Series Helps You
db.createCollection("weather", {
timeseries: {
timeField: "timestamp",
metaField: "sensor_id",
granularity: “hours”
},
expireAfterSeconds: 86400
});Native Time Series Collections
Quickly get started with a flexible schema optimized for high storage efficiency that helps handle out of order data.
Low Latency Queries
Efficiently scale your queries with automatic clustered indexes on time and secondary indexes on any metadata field.
{
"$setWindowFields": {
"partitionBy": "$game",
"sortBy": { "score":-1},
"output": {
"userRank": {
"$rank": {}
}
}
}
}Real-Time Analytics
Uncover hidden patterns with window functions that let you calculate moving averages and cumulative sums over flexible time windows. Aggregate data across custom time intervals with temporal expressions.
Full Data Lifecycle Management
Natively support the entire time series data lifecycle from ingestion, storage, querying, real-time analysis, and visualization through to online archiving as data ages.
What’s New
MongoDB’s New Time-Series Collections
Learn more about the new time series collections and how you can start building time series applications today!
Learn More →
New version of the Kafka Connector
Check out our docs page to learn more about the new MongoDB Time Series Kafka Connector.
Learn More →
MongoDB 5.1 What’s New
Check out all the new features in MongoDB 5.1!
Learn More →
Frequently Asked Questions
What is Time Series?
Time Series is a sequence of measurements recorded over a period of time. Common data properties include (but not limited to) ordered data, time ingested data and new data that is more valuable than historical data.
How do I store time-series data in MongoDB?
Time series data is straightforward to store in MongoDB. Native-time series collections are optimized for storage efficiency and low latency queries, allowing you to get started with your schema design quickly. To ingest time-series data into your collection, simply insert like you would any other individual document or measurement.
How do I use time-series data?
You can query time-series data in the same way you query other data in MongoDB - using the MongoDB Query API that is available in any popular programming language. With our new clustered indexes, you can query time series at low latency. The MongoDB Query API gives you window functions, allowing you to run analytical queries like time based moving averages and cumulative sums to uncover hidden patterns and new advanced temporal operators, enabling you to write less code and get insights in real-time. You can then use MongoDB Charts to visualize your time series data in real-time.
What are some use cases that use time series data?
Time series data is constantly being generated everywhere and is relevant in every industry. You can find time series data in industries like Energy with IoT sensors collecting power consumption data, water, and temperature measurements to Financial services like loans, stock tickers, or transaction history. Time series data is ubiquitous and found in more places than you think.
How do I combine time series data with other data in my apps?
Time series data sits in a collection right alongside your other enterprise data. You can use the Aggregation Pipeline and MongoDB Query API overall to combine and enrich the two without having to move your data out of specialized time series data stores - one platform, one API, no data movement - making it easy and fast to work with any type of data.
How do I ingest time series data?
You can ingest data directly into time series collections. Optionally, the latest release of the MongoDB Connector for Apache Kafka adds native support for time series. You can automatically create time series collections directly from Kafka topic messages. This enables you to collect data at the edge, process and aggregate it as needed before persisting it into time series collections in MongoDB.
How do I archive old time series data?
You can use our Online Archive - configure a policy, this is coming soon. Also, you can use federated query to combine and work with data across live operational Atlas clusters and cold storage.