db.collection.createIndex()
MongoDB with drivers
This page documents a mongosh
method. To see the equivalent
method in a MongoDB driver, see the corresponding page for your
programming language:
Definition
db.collection.createIndex(keys, options, commitQuorum)
Creates indexes on collections.
To minimize the impact of building an index on replica sets and sharded clusters, use a rolling index build procedure as described on Rolling Index Builds on Replica Sets.
Compatibility
This method is available in deployments hosted in the following environments:
MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud
Note
This command is supported in all MongoDB Atlas clusters. For information on Atlas support for all commands, see Unsupported Commands.
MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB
Syntax
The createIndex()
method has the following
form:
db.collection.createIndex( <keys>, <options>, <commitQuorum>)
The createIndex()
method takes the following
parameters:
Parameter | Type | Description |
---|---|---|
keys | document | A document that contains the field and value pairs where the field is the index key and the value describes the type of index for that field. For an ascending index on a field, specify a value of An asterisk ( MongoDB supports several different index types, including: See index types for more information. Wildcard indexes support workloads where users query against custom fields or a large variety of fields in a collection.
|
options | document | Optional. A document that contains a set of options that controls the creation
of the index. See Options for details. |
integer or string | Optional. The minimum number of data-bearing voting replica
set members (i.e. commit quorum), including the primary, that
must report a successful index build before the primary
marks the Supports the following values:
|
Options
The options
document contains a set of options that controls the
creation of the index. Different index types can have additional
options specific for that type.
Multiple index options can be specified in the same document. However,
if you specify mutiple option documents the db.collection.createIndex()
operation will fail.
Consider the following db.collection.createIndex()
operation:
db.collection.createIndex( { "a": 1 }, { unique: true, sparse: true, expireAfterSeconds: 3600 } )
If the options specification had been split into multiple documents
like this:
{ unique: true }, { sparse: true, expireAfterSeconds: 3600 }
the index creation operation would have failed.
Options for All Index Types
The following options are available for all index types unless otherwise specified:
Parameter | Type | Description | |
---|---|---|---|
unique | boolean | Optional. Creates a unique index so that the collection will not accept insertion or update of documents where the index key value matches an existing value in the index. Specify The option is unavailable for hashed indexes. | |
name | string | Optional. The name of the index. If unspecified, MongoDB generates an index name
by concatenating the names of the indexed fields and the sort order. | |
partialFilterExpression | document | Optional. If specified, the index only references documents that match the filter expression. See Partial Indexes for more information. A filter expression can include:
You can specify a New in version 3.2. | |
sparse | boolean | Optional. If The following index types are sparse by default and ignore this option: For a compound index that includes TipPartial indexes offer a superset of the functionality of sparse indexes. Unless your application has a specific requirement, use partial indexes instead of sparse indexes. | |
expireAfterSeconds | integer | Optional. Specifies a value, in seconds, as a time to live (TTL) to control how long MongoDB retains documents in this collection. This option only applies to TTL indexes. See Expire Data from Collections by Setting TTL for more information. If you use TTL indexes created before MongoDB 5.0, or if you want to sync data created in MongDB 5.0 with a pre-5.0 installation, see Indexes Configured Using NaN to avoid misconfiguration issues. The TTL index | |
boolean | Optional. A flag that determines whether the index is hidden from the query planner. A hidden index is not evaluated as part of the query plan selection. Default is | ||
storageEngine | document | Optional. Allows users to configure the storage engine on a per-index basis when creating an index. The
Storage engine configuration options specified when creating indexes are validated and logged to the oplog during replication to support replica sets with members that use different storage engines. |
Option for Collation
Parameter | Type | Description | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
collation | document | Optional. Specifies the collation for the index. Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks. If you have specified a collation at the collection level, then:
The collation option has the following syntax:
When specifying collation, the New in version 3.4. |
The following indexes only support simple binary comparison and do not support collation:
text indexes,
2d indexes, and
geoHaystack indexes.
Tip
To create a text
, a 2d
, or a geoHaystack
index on a
collection that has a non-simple collation, you must explicitly
specify {collation: {locale: "simple"} }
when creating the
index.
Collation and Index Use
If you have specified a collation at the collection level, then:
If you do not specify a collation when creating the index, MongoDB creates the index with the collection's default collation.
If you do specify a collation when creating the index, MongoDB creates the index with the specified collation.
Tip
By specifying a collation strength
of 1
or 2
, you can
create a case-insensitive index. Index with a collation strength
of 1
is both diacritic- and case-insensitive.
You can create multiple indexes on the same key(s) with different collations. To create indexes with the same key pattern but different collations, you must supply unique index names.
To use an index for string comparisons, an operation must also specify the same collation. That is, an index with a collation cannot support an operation that performs string comparisons on the indexed fields if the operation specifies a different collation.
Warning
Because indexes that are configured with collation use ICU collation keys to achieve sort order, collation-aware index keys may be larger than index keys for indexes without collation.
For example, the collection myColl
has an index on a string
field category
with the collation locale "fr"
.
db.myColl.createIndex( { category: 1 }, { collation: { locale: "fr" } } )
The following query operation, which specifies the same collation as the index, can use the index:
db.myColl.find( { category: "cafe" } ).collation( { locale: "fr" } )
However, the following query operation, which by default uses the "simple" binary collator, cannot use the index:
db.myColl.find( { category: "cafe" } )
For a compound index where the index prefix keys are not strings, arrays, and embedded documents, an operation that specifies a different collation can still use the index to support comparisons on the index prefix keys.
For example, the collection myColl
has a compound index on the
numeric fields score
and price
and the string field
category
; the index is created with the collation locale
"fr"
for string comparisons:
db.myColl.createIndex( { score: 1, price: 1, category: 1 }, { collation: { locale: "fr" } } )
The following operations, which use "simple"
binary collation
for string comparisons, can use the index:
db.myColl.find( { score: 5 } ).sort( { price: 1 } ) db.myColl.find( { score: 5, price: { $gt: NumberDecimal( "10" ) } } ).sort( { price: 1 } )
The following operation, which uses "simple"
binary collation
for string comparisons on the indexed category
field, can use
the index to fulfill only the score: 5
portion of the query:
db.myColl.find( { score: 5, category: "cafe" } )
Important
Matches against document keys, including embedded document keys, use simple binary comparison. This means that a query for a key like "foo.bár" will not match the key "foo.bar", regardless of the value you set for the strength parameter.
Options for text
Indexes
The following options are available for text indexes only:
Parameter | Type | Description |
---|---|---|
weights | document | Optional. For text indexes, a document that contains
field and weight pairs. The weight is an integer ranging from 1 to
99,999 and denotes the significance of the field relative to the
other indexed fields in terms of the score. You can specify weights
for some or all the indexed fields. See
Assign Weights to Text Search Results on Self-Managed Deployments to adjust the scores.
The default value is Starting in MongoDB 5.0, the weights option is only allowed for text indexes. |
default_language | string | Optional. For text indexes, the language that
determines the list of stop words and the rules for the stemmer and
tokenizer. See Text Search Languages on Self-Managed Deployments for the available
languages and Specify the Default Language for a Text Index on Self-Managed Deployments for
more information and examples. The default value is english . |
language_override | string | Optional. For text indexes, the name of the field, in
the collection's documents, that contains the override language for
the document. The default value is language . See
Use any Field to Specify the Language for a Document for an example. |
textIndexVersion | integer | Optional. The For available versions, see Versions. |
Options for 2dsphere
Indexes
The following option is available for 2dsphere indexes only:
Parameter | Type | Description |
---|---|---|
2dsphereIndexVersion | integer | Optional. The For the available versions, see Versions. |
Options for 2d
Indexes
The following options are available for 2d indexes only:
Options for geoHaystack
Indexes
The following option is available for geoHaystack indexes only:
Parameter | Type | Description |
---|---|---|
bucketSize | number | For geoHaystack indexes, specify the number of units within which to group the location values; i.e. group in the same bucket those location values that are within the specified number of units to each other. The value must be greater than 0. |
Note
Removed in MongoDB 5.0
MongoDB 5.0 removes the deprecated geoHaystack index and geoSearch
command. Use a
2d index with $geoNear
or one of the
supported geospatial query operators
instead.
Upgrading your MongoDB instance to 5.0 and setting
featureCompatibilityVersion to 5.0
will delete any
pre-existing geoHaystack indexes.
Options for wildcard
indexes
The following option is available for wildcard indexes only:
Parameter | Type | Description | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
wildcardProjection | document | Optional. Allows users to include or exclude specific field paths from
a wildcard index using the
The
The
Wildcard indexes omit the
With the exception of explicitly including |
Behaviors
Concurrency
Changed in version 4.2.
MongoDB uses an optimized build process that obtains and holds an exclusive
lock on the specified collection at the start and end of the index build. All
subsequent operations on the collection must wait until createIndex()
releases
the exclusive lock. createIndex()
allows interleaving read and write
operations during the majority of the index build.
For more information on the locking behavior of createIndex()
, see
Index Builds on Populated Collections.
Recreating an Existing Index
If you call db.collection.createIndex()
for an index that
already exists, MongoDB does not recreate the index.
Index Options
Non-Collation and Non-Hidden Options
With the exception of the collation option, if you create an index with one set of index options and then try to recreate the same index but with different index options, MongoDB will not change the options nor recreate the index.
The hidden option can be changed without dropping and recreating the index. See Hidden Option.
To change the other index options, drop the existing index with
db.collection.dropIndex()
before running
db.collection.createIndex()
with the new options.
Collation Option
You can create multiple indexes on the same key(s) with different collations. To create indexes with the same key pattern but different collations, you must supply unique index names.
Hidden Option
To hide or unhide existing indexes, you can use the following
mongosh
methods:
For example,
To change the
hidden
option for an index totrue
, use thedb.collection.hideIndex()
method:db.restaurants.hideIndex( { borough: 1, ratings: 1 } ); To change the
hidden
option for an index tofalse
, use thedb.collection.unhideIndex()
method:db.restaurants.unhideIndex( { borough: 1, city: 1 } );
Transactions
You can create collections and indexes inside a distributed transaction if the transaction is not a cross-shard write transaction.
To use db.collection.createIndex()
in a transaction, the transaction must use read
concern "local"
. If you specify a read concern level
other than "local"
, the transaction fails.
Examples
Create an Ascending Index on a Single Field
The following example creates an ascending index on the field
orderDate
.
db.collection.createIndex( { orderDate: 1 } )
If the keys
document specifies more than one field, then
createIndex()
creates a compound index.
Create an Index on a Multiple Fields
The following example creates a compound index on the
orderDate
field (in ascending order) and the zipcode
field (in descending order.)
db.collection.createIndex( { orderDate: 1, zipcode: -1 } )
Compound indexes can include a single hashed field.
Compound hashed indexes require featureCompatibilityVersion
set to at least 5.0
.
The following example creates a compound index on the state
field
(in ascending order) and the zipcode
field (hashed):
db.collection.createIndex( { "state" : 1, "zipcode" : "hashed" } )
The order of fields in a compound index is important for supporting
sort()
operations using the index.
Create Indexes with Collation Specified
New in version 3.4.
The following example creates an index named category_fr
. The
example creates the index with the collation that specifies the locale fr
and
comparison strength 2
:
db.collection.createIndex( { category: 1 }, { name: "category_fr", collation: { locale: "fr", strength: 2 } } )
The following example creates a compound index named
date_category_fr
with a collation.
The collation applies only to the index keys with string values.
db.collection.createIndex( { orderDate: 1, category: 1 }, { name: "date_category_fr", collation: { locale: "fr", strength: 2 } } )
The collation applies to the indexed keys whose values are string.
For queries or sort operations on the indexed keys that uses the same collation rules, MongoDB can use the index. For details, see Collation and Index Use.
Create a Wildcard Index
New in version 4.2.
The mongod
featureCompatibilityVersion must be 4.2
to
create wildcard indexes. For instructions on setting the fCV, see
Set Feature Compatibility Version on MongoDB 5.0 Deployments.
Wildcard indexes omit the
_id
field by default. To include the_id
field in the wildcard index, you must explicitly include it in thewildcardProjection
document:{ "wildcardProjection" : { "_id" : 1, "<field>" : 0|1 } } With the exception of explicitly including
_id
field, you cannot combine inclusion and exclusion statements in thewildcardProjection
document.Wildcard indexes do not support the following index types or properties:
Note
Wildcard Indexes are distinct from and incompatible with Wildcard Text Indexes. Wildcard indexes cannot support queries using the
$text
operator.For complete documentation on wildcard index restrictions, see Wildcard Index Restrictions.
For complete documentation on Wildcard Indexes, see Wildcard Indexes.
The following lists examples of wildcard index creation:
Create a Wildcard Index on a Single Field Path
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
The following operation creates a wildcard index on the
product_attributes
field:
use inventory db.products_catalog.createIndex( { "product_attributes.$**" : 1 } )
With this wildcard index, MongoDB indexes all scalar values of
product_attributes
. If the field is a nested document or array, the
wildcard index recurses into the document/array and indexes all scalar
fields in the document/array.
The wildcard index can support arbitrary single-field queries on
product_attributes
or one of its nested fields:
db.products_catalog.find( { "product_attributes.superFlight" : true } ) db.products_catalog.find( { "product_attributes.maxSpeed" : { $gt : 20 } } ) db.products_catalog.find( { "product_attributes.elements" : { $eq: "water" } } )
Note
The path-specific wildcard index syntax is incompatible with the
wildcardProjection
option. See the parameter documentation for more
information.
Create a Wildcard Index on All Field Paths
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
The following operation creates a wildcard index on all scalar fields
(excluding the _id
field):
use inventory db.products_catalog.createIndex( { "$**" : 1 } )
With this wildcard index, MongoDB indexes all scalar fields for each document in the collection. If a given field is a nested document or array, the wildcard index recurses into the document/array and indexes all scalar fields in the document/array.
The created index can support queries on any arbitrary field within documents in the collection:
db.products_catalog.find( { "product_price" : { $lt : 25 } } ) db.products_catalog.find( { "product_attributes.elements" : { $eq: "water" } } )
Note
Wildcard indexes omit the _id
field by default. To include the
_id
field in the wildcard index, you must explicitly include it
in the wildcardProjection
document. See parameter documentation for
more information.
Include Specific Fields in Wildcard Index Coverage
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
The following operation creates a wildcard index and uses
the wildcardProjection
option to include only scalar values of the
product_attributes.elements
and product_attributes.resistance
fields in the index.
use inventory db.products_catalog.createIndex( { "$**" : 1 }, { "wildcardProjection" : { "product_attributes.elements" : 1, "product_attributes.resistance" : 1 } } )
While the key pattern "$**"
covers all fields in the document, the
wildcardProjection
field limits the index to only the included
fields. For complete documentation on wildcardProjection
, see
Options for wildcard
indexes.
If a field is a nested document or array, the wildcard index recurses into the document/array and indexes all scalar fields in the document/array.
The created index can support queries on any scalar field
included in the wildcardProjection
:
db.products_catalog.find( { "product_attributes.elements" : { $eq: "Water" } } ) db.products_catalog.find( { "product_attributes.resistance" : "Bludgeoning" } )
Note
Wildcard indexes do not support mixing inclusion and exclusion
statements in the wildcardProjection
document except when
explicitly including the _id
field. For more information on
wildcardProjection
, see the parameter documentation.
Omit Specific Fields from Wildcard Index Coverage
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
The following operation creates a wildcard index and uses
the wildcardProjection
document to index all scalar fields
for each document in the collection, excluding the
product_attributes.elements
and product_attributes.resistance
fields:
use inventory db.products_catalog.createIndex( { "$**" : 1 }, { "wildcardProjection" : { "product_attributes.elements" : 0, "product_attributes.resistance" : 0 } } )
While the key pattern "$**"
covers all fields in the document, the
wildcardProjection
field excludes the specified fields from the
index. For complete documentation on wildcardProjection
, see
Options for wildcard
indexes.
If a field is a nested document or array, the wildcard index recurses into the document/array and indexes all scalar fields in the document/array.
The created index can support queries on any scalar field except
those excluded by wildcardProjection
:
db.products_catalog.find( { "product_attributes.maxSpeed" : { $gt: 25 } } ) db.products_catalog.find( { "product_attributes.superStrength" : true } )
Note
Wildcard indexes do not support mixing inclusion and exclusion
statements in the wildcardProjection
document except when
explicitly including the _id
field. For more information on
wildcardProjection
, see the parameter documentation.
Create Index With Commit Quorum
Note
Requires featureCompatibilityVersion 4.4+
Each mongod
in the replica set or sharded cluster
must have featureCompatibilityVersion set to at
least 4.4
to start index builds simultaneously across
replica set members.
Index builds on a replica set or sharded cluster build simultaneously across
all data-bearing replica set members. For sharded clusters, the index build
occurs only on shards containing data for the collection being indexed.
The primary requires a minimum number of data-bearing voting
members (i.e commit quorum), including itself,
that must complete the build before marking the index as ready for
use. See Index Builds in Replicated Environments for more
information.
Specify the commitQuorum
parameter to the createIndex()
operation to set
the minimum number of data-bearing voting members (i.e commit
quorum), including the primary, which must complete the
index build before the primary marks the indexes as ready. The default
commit quorum is votingMembers
, or all data-bearing voting replica
set members.
The following operation creates an index with a commit quorum of "majority"
, or a
simple majority of data-bearing voting members:
db.getSiblingDB("examples").invoices.createIndex( { "invoices" : 1 }, { }, "majority" )
The primary marks index build as ready only after a simple majority of data-bearing voting members "vote" to commit the index build. For more information on index builds and the voting process, see Index Builds in Replicated Environments.
Additional Information
The Indexes section of this manual for full documentation of indexes and indexing in MongoDB.
db.collection.getIndexes()
to view the specifications of existing indexes for a collection.Text Indexes on Self-Managed Deployments for details on creating
text
indexes.Geospatial Indexes for geospatial queries.
TTL Indexes for expiration of data.