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Add Secondary Indexes to Time Series Collections

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  • Use Secondary Indexes to Improve Sort Performance
  • "Last Point" Queries on Time Series Collections
  • Specify Index Hints for Time Series Collections
  • Time Series Secondary Indexes in MongoDB 6.0

To improve query performance for time series collections, add one or more secondary indexes to support common time series query patterns. Specifically, we recommend that you create one or more compound indexes on the fields specified as the timeField and the metaField. If the field value for the metaField field is a document, you can create secondary indexes on fields inside that document.

Note

Not all index types are supported. For a list of unsupported index types, see Limitations for Secondary Indexes on Time Series Collections.

For example, this command creates a compound index on the metadata.sensorId and timestamp fields:

db.weather24h.createIndex( { "metadata.sensorId": 1, "timestamp": 1 } )

Tip

See:

Time Series collections can use indexes to improve sort performance on the timeField and the metaField.

For example, the following sensorData collection contains measurements from weather sensors:

db.sensorData.insertMany( [ {
"metadata": {
"sensorId": 5578,
"location": {
type: "Point",
coordinates: [-77.40711, 39.03335]
}
},
"timestamp": ISODate("2022-01-15T00:00:00.000Z"),
"currentConditions": {
"windDirecton": 127.0,
"tempF": 71.0,
"windSpeed": 2.0,
"cloudCover": null,
"precip": 0.1,
"humidity": 94.0,
}
},
{
"metadata": {
"sensorId": 5578,
"location": {
type: "Point",
coordinates: [-77.40711, 39.03335]
}
},
"timestamp": ISODate("2022-01-15T00:01:00.000Z"),
"currentConditions": {
"windDirecton": 128.0,
"tempF": 69.8,
"windSpeed": 2.2,
"cloudCover": null,
"precip": 0.1,
"humidity": 94.3,
}
},
{
"metadata": {
"sensorId": 5579,
"location": {
type: "Point",
coordinates: [-80.19773, 25.77481]
}
},
"timestamp": ISODate("2022-01-15T00:01:00.000Z"),
"currentConditions": {
"windDirecton": 115.0,
"tempF": 88.0,
"windSpeed": 1.0,
"cloudCover": null,
"precip": 0.0,
"humidity": 99.0,
}
}
]
)

Time Series collections :ref:automatically create an internal clustered index. The query planner uses this index to improve sort performance.

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.

The following sort operation on the timestamp field uses the clustered index to improve performance:

db.sensorData.find().sort( { "timestamp": 1 } )

To confirm that the sort operation used the clustered index, run the operation again with the .explain( "executionStats" ) option:

db.sensorData.find().sort( { "timestamp": 1 } ).explain( "executionStats" )

The winningPlan.queryPlan.inputStage.stage is COLLSCAN and an _internalBoundedSort stage is present in the explain plan output. The interalBoundedSort field indicates that the clustered index was used. For more information on explain plan output, see explain results.

Secondary indexes on Time Series collections can improve performance for sort operations and increase the number of scenarios where indexes can be used.

Sort operations on Time Series collections can use secondary indexes on the timeField. Under certain conditions, sort operations can also use compound secondary indexes on the metaField and timeField.

The Aggregation Pipeline Stages $match and $sort determine which indexes a Time Series collection can use. The following list describes scenarios where an index can be used:

  • Sort on { <timeField:> ±1 } uses the clustered index

  • Sort on { <timeField>: ±1 } uses a secondary index on <timeField>

  • Sort on { <metaField>: ±1, timeField: ±1 } uses a secondary index on { <metaField>: ±1, timeField: ±1 }

  • Sort on { <timeField>: ±1 } uses a secondary index on { metaField: ±1, timeField: ±1 } when there is a point predicate on <metaField>

Create a secondary index on the timestamp field:

db.sensorData.createIndex( { "timestamp": 1 } )

The following sort operation on the timestamp field uses the Secondary Index to improve performance:

db.sensorData.aggregate( [
{ $match: { "timestamp" : { $gte: ISODate("2022-01-15T00:00:00.000Z") } } },
{ $sort: { "timestamp": 1 } }
] )

To confirm that the sort operation used the Secondary Index, run the operation again with the .explain( "executionStats" ) option:

db.sensorData.explain( "executionStats" ).aggregate( [
{ $match: { "timestamp": { $gte: ISODate("2022-01-15T00:00:00.000Z") } } },
{ $sort: { "timestamp": 1 } }
] )

A "last point" query fetches the latest measurement for each unique metadata value. For example, you may want to get the latest temperature reading from all sensors. Improve performance on last point queries by creating any of the following indexes:

{ "metadata.sensorId": 1, "timestamp": 1 }
{ "metadata.sensorId": 1, "timestamp": -1 }
{ "metadata.sensorId": -1, "timestamp": 1 }
{ "metadata.sensorId": -1, "timestamp": -1 }

Note

Last point queries are most performant when they use the DISTINCT_SCAN optimization. This optimization is only available when an index on timeField is descending.

The following command creates a compound secondary index on metaField (ascending) and timeField (descending):

db.sensorData.createIndex( { "metadata.sensorId": 1, "timestamp": -1 } )

The following last point query example uses the descending timeField compound secondary index created above:

db.sensorData.aggregate( [
{
$sort: { "metadata.sensorId": 1, "timestamp": -1 }
},
{
$group: {
_id: "$metadata.sensorId",
ts: { $first: "$timestamp" },
temperatureF: { $first: "$currentConditions.tempF" }
}
}
] )

To confirm that the last point query used the secondary index, run the operation again using .explain( "executionStats" ):

db.getCollection( 'sensorData' ).explain( "executionStats" ).aggregate( [
{
$sort: { "metadata.sensorId": 1, "timestamp": -1 }
},
{
$group: {
_id: "$metadata.sensorId",
ts: { $first: "$timestamp" },
temperatureF: { $first: "$currentConditions.tempF" }
}
}
] )

The winningPlan.queryPlan.inputStage.stage is DISTINCT_SCAN, which indicates that the index was used. For more information on the explain plan output, see Explain Results.

Index hints cause MongoDB to use a specific index for a query. Some operations on time series collections can only take advantage of an index if that index is specified in a hint.

For example, the following query causes MongoDB to use the timestamp_1_metadata.sensorId_1 index:

db.sensorData.find( { "metadata.sensorId": 5578 } ).hint( "timestamp_1_metadata.sensorId_1" )

On a time series collection, you can specify hints using either the index name or the index key pattern. To get the names of the indexes on a collection, use the db.collection.getIndexes() method.

Starting in MongoDB 6.0, you can:

Note

If there are secondary indexes on time series collections and you need to downgrade the Feature Compatibility Version (FCV), you must first drop any secondary indexes that are incompatible with the downgraded FCV. See setFeatureCompatibilityVersion

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