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Single Field Indexes

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  • Compatibility
  • Create an Ascending Index on a Single Field
  • Create an Index on an Embedded Field
  • Create an Index on Embedded Document
  • Additional Considerations

MongoDB provides complete support for indexes on any field in a collection of documents. By default, all collections have an index on the _id field, and applications and users may add additional indexes to support important queries and operations.

This document describes ascending/descending indexes on a single field.

Diagram of an index on the ``score`` field (ascending).

Compatibility

You can use single field indexes for deployments hosted in MongoDB Atlas.

To learn more about managing indexes for deployments hosted in MongoDB Atlas, see Create, View, Drop, and Hide Indexes.

Create an Ascending Index on a Single Field

Consider a collection schools that contains the following sample document:

db.schools.insertOne(
   {
      "_id": ObjectId("570c04a4ad233577f97dc459"),
      "studentsEnrolled": 1034,
      "location": { state: "NY", city: "New York" }
   }
)

The following operation creates an ascending index on the studentsEnrolled field of the schools collection:

db.schools.createIndex( { studentsEnrolled: 1 } )

The value of the field in the index specification describes the kind of index for that field. For example, a value of 1 specifies an index that orders items in ascending order. A value of -1 specifies an index that orders items in descending order. For additional index types, see index types.

The created index supports queries that select on the field studentsEnrolled, such as the following:

db.schools.find( { studentsEnrolled: 1034 } )
db.schools.find( { studentsEnrolled: { $gt: 500 } } )

Create an Index on an Embedded Field

You can create indexes on fields within embedded documents, just as you can index top-level fields in documents. Indexes on embedded fields differ from indexes on embedded documents, which include the full content up to the maximum index size of the embedded document in the index. Instead, indexes on embedded fields allow you to use a "dot notation," to introspect into embedded documents.

Consider a collection named records that holds documents that resemble the following sample document:

{
  "_id": ObjectId("570c04a4ad233577f97dc459"),
  "studentsEnrolled": 1034,
  "location": { state: "NY", city: "New York" }
}

The following operation creates an index on the location.state field:

db.schools.createIndex( { "location.state": 1 } )

The created index will support queries that select on the field location.state, such as the following:

db.schools.find( { "location.state": "CA" } )
db.schools.find( { "location.city": "Albany", "location.state": "NY" } )

Create an Index on Embedded Document

You can also create indexes on embedded document as a whole.

When you create an index on an embedded document, only queries that specify the entire embedded document use the index. Queries on a specific field within the document do not use the index.

Consider a collection named schools that holds documents that resemble the following sample document:

{
  "_id": ObjectId("570c04a4ad233577f97dc459"),
  "studentsEnrolled": 1034,
  "location": { state: "NY", city: "New York" }
}

The location field is an embedded document, containing the embedded fields city and state. The following command creates an index on the location field as a whole:

db.schools.createIndex( { location: 1 } )

The following query can use the index on the location field:

db.schools.find( { location: { city: "New York", state: "NY" } } )

Note

Although the query can use the index, the result set does not include the sample document above. When performing equality matches on embedded documents, field order matters and the embedded documents must match exactly. See Query Embedded Documents for more information regarding querying on embedded documents.

Additional Considerations

During index builds, applications may encounter reduced performance or limited read/write access to the collection being indexed.

For more information on the index build process, see Index Builds on Populated Collections, especially the Index Builds in Replicated Environments section.

Some drivers use NumberLong(1) instead of 1 to specify the index order. The resulting indexes are the same.

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Compound Indexes →

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