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 uniquely identifies a series and rarely changes.
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.
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.
Time series collections use an underlying columnar storage format and store data in time-order with an automatically created clustered index. The columnar storage 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 behave like normal collections. You can insert and query your data as you normally would.
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.
When you query time series collections, you operate on one document per measurement. Queries on time series collections take advantage of the optimized internal storage format and return results faster.
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.
Internal Index
When you create a time series collection, MongoDB automatically creates an internal clustered index on the time field. This index improves query efficiency and reduces disk usage. To learn more about the performance benefits of clustered indexes, see Clustered Collections.
The internal index is not listed when you run listIndexes.
Note
If you insert a document into a collection with a timeField
value before 1970-01-01T00:00:00.000Z or after
2038-01-19T03:14:07.000Z,
MongoDB logs a warning and prevents some query optimizations from
using the internal index. Create a secondary index
on the timeField to regain query performance and resolve the log
warning.
Get Started
To get started with time series collections, see Create and Query a Time Series Collection.