Time Series Collections

On this page

Procedures

Behavior

New in version 5.0.

Time series collections efficiently store sequences of measurements over a period of time. Time series data is any data that is collected over time and is uniquely identified by one or more unchanging parameters. The unchanging parameters that identify your time series data is generally your data source's metadata.

Example	Measurement	Metadata
Weather data	Temperature	Sensor identifier, location
Stock data	Stock price	Stock ticker, exchange
Website visitors	View count	URL

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.

Procedures

Create a Time Series Collection

Note

You can only create time series collections on a system with featureCompatibilityVersion set to 5.0.

Before you can insert data into a time series collection, you must explicitly create the collection using either the db.createCollection() method or the create command:

db.createCollection(
    "weather",
    {
       timeseries: {
          timeField: "timestamp",
          metaField: "metadata",
          granularity: "hours"
       }
    }
)

When creating a time series collection, specify the following options:

Field	Type	Description
`timeseries.timeField`	string	Required. The name of the field which contains the date in each time series document. Documents in a time series collection must have a valid BSON date as the value for the `timeField`.
`timeseries.metaField`	string	Optional. The name of the field which contains metadata in each time series document. The metadata in the specified field should be data that is used to label a unique series of documents. The metadata should rarely, if ever, change. The name of the specified field may not be `_id` or the same as the `timeseries.timeField`. The field can be of any type.
`timeseries.granularity`	string	Optional. Possible values are `"seconds"`, `"minutes"`, and `"hours"`. By default, MongoDB sets the `granularity` to `"seconds"` for high-frequency ingestion. Manually set the `granularity` parameter to improve performance by optimizing how data in the time series collection is stored internally. To select a value for `granularity`, choose the closest match to the time span between consecutive incoming measurements. If you specify the `timeseries.metaField`, consider the time span between consecutive incoming measurements that have the same unique value for the `metaField` field. Measurements often have the same unique value for the `metaField` field if they come from the same source. If you do not specify `timeseries.metaField`, consider the time span between all measurements that are inserted in the collection.
`expireAfterSeconds`	number	Optional. Enable the automatic deletion of documents in a time series collection by specifying the number of seconds after which documents expire. MongoDB deletes expired documents automatically. See Set up Automatic Removal for Time Series Collections (TTL) for more information.

Other options allowed with the timeseries option are:

storageEngine
indexOptionDefaults
collation
writeConcern
comment

Tip

See:

db.createCollection() and create.

Insert Measurements into a Time Series Collection

Each document you insert should contain a single measurement. To insert multiple documents at once, issue the following command:

db.weather.insertMany( [
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T00:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T04:00:00.000Z"),
      "temp": 11
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T08:00:00.000Z"),
      "temp": 11
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T12:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T16:00:00.000Z"),
      "temp": 16
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T20:00:00.000Z"),
      "temp": 15
   }, {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T00:00:00.000Z"),
      "temp": 13
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T04:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T08:00:00.000Z"),
      "temp": 11
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T12:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T16:00:00.000Z"),
      "temp": 17
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T20:00:00.000Z"),
      "temp": 12
   }
] )

To insert a single document, use the db.collection.insertOne() method.

Query a Time Series Collection

To retrieve one document from a time series collection, issue the following command:

db.weather.findOne({
   "timestamp": ISODate("2021-05-18T00:00:00.000Z")
})

Run Aggregations on a Time Series Collection

For additional query functionality, use an aggregation pipeline such as:

db.weather.aggregate( [
   {
      $project: {
         date: {
            $dateToParts: { date: "$timestamp" }
         },
         temp: 1
      }
   },
   {
      $group: {
         _id: {
            date: {
               year: "$date.year",
               month: "$date.month",
               day: "$date.day"
            }
         },
         avgTmp: { $avg: "$temp" }
      }
   }
] )

The example aggregation pipeline groups all documents by the date of the measurement and then returns the average of all temperature measurements that day:

 {
  "_id" : {
    "date" : {
      "year" : 2021,
      "month" : 5,
      "day" : 18
    }
  },
  "avgTmp" : 12.714285714285714
}
{
  "_id" : {
    "date" : {
      "year" : 2021,
      "month" : 5,
      "day" : 19
    }
  },
  "avgTmp" : 13
}

Check if a Collection is of Type Time Series

To determine if a collection is of type time series, use the listCollections command:

db.runCommand( { listCollections: 1.0 } )

If the collection is a time series collection, it returns this:

{
    cursor: {
       id: <number>,
       ns: 'test.$cmd.listCollections',
       firstBatch: [
         {
            name: <string>,
            type: 'timeseries',
            options: {
               expireAfterSeconds: <number>,
               timeseries: { ... }
            },
            ...
         },
         ...
       ]
    }
 }

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 on internal collections that automatically organize time series data into an optimized storage format on insert.

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.

Index

The implementation of time series collections uses internal collections that reduce disk usage and improve query efficiency. Time series collections automatically order and index data by time. The internal index for a time series collection is not displayed by listIndexes.

Tip

To improve query performance, you can manually add secondary indexes on the fields specified as the metaField and the timeField.

Default Compression Algorithm

Time series collections ignore the global default compression algorithm, snappy, in favor of zstd, unless a different compression algorithm is specified using the storageEngine option when the collection was created. For example, to change the compression algorithm to snappy for a new weather collection, add the following option:

db.createCollection(
  "weather",
  {
     timeseries: {
        timeField: "timestamp"
     },
     storageEngine: {
        wiredTiger: {
           configString: "block_compressor=snappy"
        }
     }
  }
)

Valid block_compressor options are:

snappy
zlib
zstd (default)
none

← Capped Collections Time Series Collection Limitations →