Time Series
Time series data is a sequence of data points in which insights are gained by analyzing changes over time.
Time series data is generally composed of these components:
Time when the data point was recorded.
metadata (sometimes referred to as source), which is a label or tag that identifies a data series and rarely changes. Metadata is stored in a metaField. You cannot add metaFields to time series documents after you create them. For more information on metaField behavior and selection, see metaFields.
Measurements (sometimes referred to as metrics or values), which are the data points tracked at increments in time. Generally these are key-value pairs that change over time.
This table shows examples of time series data:
Example | Measurement | Metadata |
|---|---|---|
Stock data | Stock price | Stock ticker, exchange |
Weather data | Temperature | Sensor identifier, location |
Website visitors | View count | URL |
For efficient time series data storage, MongoDB provides time series collections.
Time Series Collections
New in version 5.0.
Time series collections efficiently store time series data. In time series collections, writes are organized so that data from the same source is stored alongside other data points from a similar point in time.
You can create time series collections in the UI for deployments hosted in MongoDB Atlas.
Benefits
Compared to normal collections, storing time series data in time series collections improves query efficiency and reduces the disk usage for time series data and secondary indexes. MongoDB 6.3 and later automatically creates a compound index on the time and metadata fields for new time series collections.
Time series collections use an underlying columnar storage format and store data in time-order. This format provides the following benefits:
Reduced complexity for working with time series data
Improved query efficiency
Reduced disk usage
Reduced I/O for read operations
Increased WiredTiger cache usage
Behavior
Time series collections generally behave like other MongoDB collections. You insert and query data as usual.
Warning
Match expressions in update and delete commands can only specify the metaField. You can't update other fields in a time series document. For more details, see Time Series Delete and Update Limitations.
MongoDB treats time series collections as writable non-materialized views backed by an internal collection. When you insert data, the internal collection automatically organizes time series data into an optimized storage format.
Starting in MongoDB 6.3: if you create a new time series collection, MongoDB also generates a compound index on the metaField and timeField fields. To improve query performance, queries on time series collections use the new compound index. The compound index also uses the optimized storage format.
Tip
To improve query performance, you can manually add secondary indexes on measurement fields or any field in your time series collection.
Important
Backward-Incompatible Feature
You must drop time series collections before downgrading:
MongoDB 6.0 or later to MongoDB 5.0.7 or earlier.
MongoDB 5.3 to MongoDB 5.0.5 or earlier.
Warning
Do not attempt to create a time series collection or view with the
name system.profile. MongoDB 6.3 and later versions return an
IllegalOperation error if you attempt to do so. Earlier MongoDB
versions crash.
metaFields
Time series documents can contain an optional metaField to group sets of
documents, both for internal storage optimization and query efficiency.
A metaField should rarely change and can be any data type. A metaField
can be an object and can contain subfields. Once you define a field as
the metaField, you can change the value of the metaField but you cannot
redefine the metaField as another field. For example, if you create time
series documents with the metaField defined as field A, you cannot
later convert a field B to be the metaField. However, if the value
of metaField A is an object, you can add new subfields to field
A.
Note
Using an array as a metaField may cause unexpected collection behavior because array equality depends on specific order.
Buckets
MongoDB uses the metaField to partition data for efficient organization and retrieval. When you create a time series collection, MongoDB groups documents into buckets. Documents within a bucket share an identical metaField value and have timeField values that are close together.
The number of buckets in a time series collection depends on the number of unique metaField values. Collections with fine-grained or dynamic metaField values may generate more, sparsely packed, short-lived buckets than collections with simple metaFields that rarely or never change. Fine-grained and dynamic metaField values typically decrease storage and query effiency.
Indexes
MongoDB automatically creates a compound index on both the metaField and timeField of a time series collection.
metaField Best Practices
Select fields that rarely or never change as part of your metaField.
If possible, select identifiers or other stable values that are common in filter expressions as part of your metaField.
Avoid selecting fields that are not used for filtering as part of your metaField. Instead, use those fields as measurements.
Get Started
To get started with time series collections, see Create and Query a Time Series Collection.