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.
Implementing Time Series
Time series data is truly industry-agnostic. It's created across use cases, from financial services to smart manufacturing. However, it can be challenging to work with due to its enormous storage footprint, which creates further challenges for querying and analyses to extract real-time insights. In this talk, we will cover the fundamentals of time series data and its usage.
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.
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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Chief Technology Officer, Picap
Feature overview
Native time series collections
Store time series data in an optimized columnar format, reducing storage and I/O demands for greater performance and scale.
Columnar storage format
Dramatically reduce your database storage footprint by more than 90% with columnar storage format and best-in-class compression algorithms.
Real-time analytics with fast queries
Significantly faster query performance with block-based processing model designed for handling large-scale data in time series aggregations.
Full data lifecycle management
Support the entire time series data lifecycle from ingest, storage, analysis, and visualization to archiving.
Enriched index support
Advanced support for compound indexes on all fields, along with geo and clustered indexes, optimized for efficient querying.
Gap filling and densification
Handle missing data points using specialized gap filling and densification functions.
Fine-grained data modification
Ability to freely modify the data with updates and deletes giving you more flexibility and control.
Scale horizontally
Horizontally distribute large data sets to reduce latency and comply with data sovereignty regulations.
Get started with
time series
Time series collections
xxxxxxxxxxdb.createCollection("weather", { timeseries: { timeField: "timestamp", metaField: "sensor_id", granularity: “hours” }, expireAfterSeconds: 86400});Window functions
Data densification
xxxxxxxxxx{ "$densify": { "field": "ts", "partitionFields": ["meta.location", "meta.model"], "step": 5, "unit": "minute", "Range":[ ISODate("2014-03-08T03:05:15"), ISODate("2014-03-08T03:10:15") ] } }Deliver insights from time series data
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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.
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Build time series applications on MongoDB
- Time series collections
- Columnar compression
- Time series queries & analytics
- Automated data lifecycle
- Support for updates & deletes
- Sharding support