db.collection.createIndexes()
On this page
- Definition
- Compatibility
- Stable API
- Options
- Options for All Index Types
- Option for Collation
- Options for
text
Indexes - Options for
2dsphere
Indexes - Options for
2d
Indexes - Options for
wildcard
indexes - Behaviors
- Recreating an Existing Index
- Index Options
- Wildcard Indexes
- Transactions
- Example
- Create Indexes Without Options
- Create Indexes with Collation Specified
- Create a Wildcard Index
- Create Indexes With Commit Quorum
- Create Multiple Indexes
- Additional Information
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.createIndexes( [ keyPatterns ], options, commitQuorum )
Creates one or more indexes on a collection.
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.
db.collection.createIndexes()
takes the following parameters:ParameterTypeDescriptionkeyPatterns
documentAn array containing index specification documents. Each document contains 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
1
; for descending index, specify a value of-1
.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:
You can create a wildcard index on a specific field and its subpaths or on all of the fields in a document.
For details see, Wildcard Indexes.
options
documentOptional. A document that contains a set of options that controls the creation of the indexes. See Options for details.integer or stringOptional. 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
indexes
as ready. A "voting" member is any replica set member wheremembers[n].votes
is greater than0
.Supports the following values:
"votingMembers"
- all data-bearing voting replica set members (Default)."majority"
- a simple majority of data-bearing voting replica set members.<int>
- a specific number of data-bearing voting replica set members.0
- Disables quorum-voting behavior. Members start the index build simultaneously but do not vote or wait for quorum before completing the index build. If you start an index build with a commit quorum of0
, you cannot later modify the commit quorum usingsetIndexCommitQuorum
.A replica set tag name.
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
Stable API
When using Stable API V1:
You cannot specify any of the following fields in the
options
document:background
bucketSize
sparse
storageEngine
You cannot create geoHaystack or text indexes.
The above unsupported index types are ignored by the query planner in strict mode. For example, attempting to use a
sparse
index withcursor.hint()
will result in the followingBadValue
error:planner returned error :: caused by :: hint provided does not correspond to an existing index
Options
The options
document contains a set of options that control the
creation of the indexes. 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 multiple option documents the db.collection.createIndexes()
operation will fail.
Consider the following db.collection.createIndexes()
operation:
db.collection.createIndexes( [ { "a": 1 }, { "b": 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.
Important
When you specify options to
db.collection.createIndexes()
, the options apply to
all of the specified indexes. For example, if you specify a
collation option, all of the created indexes will include that
collation.
db.collection.createIndexes()
will return an error if you
attempt to create indexes with incompatible options or too many
arguments. Refer to the option descriptions for more information.
Options for All Index Types
The following options are available for all index types unless otherwise specified:
Parameter | Type | Description | |
---|---|---|---|
unique | boolean | Optional. Specifies that each index specified in the 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. Options specified to | |
partialFilterExpression | document | Optional. If specified, the indexes only reference documents that match the filter expression. See Partial Indexes for more information. A filter expression can include:
You can specify a | |
sparse | boolean | Optional. If The following index types are sparse by default and ignore this option: For a compound index that includes Partial indexes have a superset of the sparse index functionality. 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 for the created indexes. 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 |
The following indexes only support simple binary comparison and do not support collation:
Tip
To create a text
or 2d
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
Specify the Default Language for a Text Index on Self-Managed Deployments for an example. |
textIndexVersion | integer | Optional. The For available versions, see Text Index Versions on Self-Managed Deployments. |
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 2dsphere Indexes. |
Options for 2d
Indexes
The following options are available for 2d
indexes
only:
Parameter | Type | Description |
---|---|---|
bits | integer | Optional. For The |
min | number | Optional. For 2d indexes, the lower inclusive boundary for
the longitude and latitude values. The default value is -180.0 . |
max | number | Optional. For 2d indexes, the upper inclusive boundary for
the longitude and latitude values. The default value is 180.0 . |
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. This option is only valid when you create an wildcard index on all document fields. You cannot specify
the
However, you can't define an index that includes the same field in the
wildcard fields and the regular (non-wildcard) fields. To define the
index correctly, use a
The
The
Wildcard indexes omit the
All of the statements in the Options specified to |
Behaviors
Recreating an Existing Index
If you call db.collection.createIndexes()
for an index or
indexes that already exist, MongoDB does not recreate the existing
index or indexes.
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.createIndexes()
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 } );
Wildcard Indexes
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 } } All of the statements in the
wildcardProjection
document must be either inclusion or exclusion statements. You can also include the_id
field with exclusion statements. This is the only exception to the rule.Wildcard indexes do not support:
Wildcard indexes are sparse indexes. They do not support queries when an indexed field does not exist. A wildcard index will index the document if the wildcard field has a
null
value.Starting in MongoDB 7.0, wildcard indexes support ascending (
1
) and descending (-1
) sort order. Earlier versions only supported ascending order.
To learn more, see:
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.createIndexes()
in a transaction, the transaction must use read
concern "local"
. If you specify a read concern level
other than "local"
, the transaction fails.
Example
Create Indexes Without Options
Consider a restaurants
collection containing documents that
resemble the following:
{ location: { type: "Point", coordinates: [-73.856077, 40.848447] }, name: "Morris Park Bake Shop", cuisine: "Cafe", borough: "Bronx", }
The following example creates two indexes on the restaurants
collection: an ascending index on the borough
field and a
2dsphere index on the location
field.
db.restaurants.createIndexes([{"borough": 1}, {"location": "2dsphere"}])
Create Indexes with Collation Specified
The following example creates two indexes on the products
collection: an ascending index on the manufacturer
field and an
ascending index on the category
field. Both indexes use a collation that specifies the locale fr
and
comparison strength 2
:
db.products.createIndexes( [ { "manufacturer": 1}, { "category": 1 } ], { collation: { locale: "fr", strength: 2 } })
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
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.createIndexes( [ { "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" : { $eq: "water" } } )
Note
The path-specific wildcard index syntax is incompatible with the
wildcardProjection
option. See the parameter documentation for more
information.
Create a Wildcard Index on All Field Paths
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
The following operation creates a wildcard index on all scalar fields
(excluding the _id
field):
use inventory db.products_catalog.createIndexes( [ { "$**" : 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" : { $eq: "water" } } )
Note
Wildcard indexes omit the _id
field by default. To include the
_id
field in the wildcard index, you must explicitly include it
in the wildcardProjection
document. See parameter documentation for
more information.
Create a Wildcard Index on Multiple Specific Field Paths
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
The following operation creates a wildcard index and uses
the wildcardProjection
option to include only scalar values of the
product_attributes.elements
and product_attributes.resistance
fields in the index.
use inventory db.products_catalog.createIndexes( [ { "$**" : 1 } ], { "wildcardProjection" : { "product_attributes.elements" : 1, "product_attributes.resistance" : 1 } } )
The pattern "$**"
includes all fields in the document. Use the
wildcardProjection
field to limit the index to the specified 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 wildcard index supports queries on any scalar field included in
the wildcardProjection
:
db.products_catalog.find( { "product_attributes.elements" : { $eq: "Water" } } ) db.products_catalog.find( { "product_attributes.resistance" : "Bludgeoning" } )
Note
Wildcard indexes do not support mixing inclusion and exclusion
statements in the wildcardProjection
document except when
explicitly including the _id
field. For more information on
wildcardProjection
, see the parameter documentation.
Omit Specific Fields from Wildcard Index Coverage
Consider a collection products_catalog
where documents may contain a
product_attributes
field. The product_attributes
field can
contain arbitrary nested fields, including embedded
documents and arrays:
db.products_catalog.insertMany( [ { _id : ObjectId("5c1d358bf383fbee028aea0b"), product_name: "Blaster Gauntlet", product_attributes: { price: { cost: 299.99, currency: "USD" } } }, { _id: ObjectId("5c1d358bf383fbee028aea0c"), product_name: "Super Suit", product_attributes: { superFlight: true, resistance: [ "Bludgeoning", "Piercing", "Slashing" ] } } ] )
This example uses a wildcard index and a wildcardProjection
document to index the scalar fields for each document in the collection.
The wildcard index excludes the product_attributes.elements
and
product_attributes.resistance
fields:
use inventory db.products_catalog.createIndexes( [ { "$**" : 1 } ], { "wildcardProjection" : { "product_attributes.elements" : 0, "product_attributes.resistance" : 0 } } )
The wildcard pattern "$**"
includes all of the fields in the
document. However, 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 index can support queries on any scalar field except fields that
are 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 Indexes 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.
To set the commit quorum, use
createIndexes()
to specify the commitQuorum
value.
commitQuorum
specifies how many data-bearing voting members, or
which voting members, including the primary, must be prepared to commit
the index build before the primary will execute the commit. The default
commit quorum is votingMembers
, which means all data-bearing
members.
The following operation creates an index with a commit quorum of "majority"
:
db.getSiblingDB("examples").invoices.createIndexes( { "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.
Create Multiple Indexes
Create a cakeSales
collection that contains cake sales in the states
of California (CA
) and Washington (WA
):
db.cakeSales.insertMany( [ { _id: 0, type: "chocolate", orderDate: new Date("2020-05-18T14:10:30Z"), state: "CA", price: 13, quantity: 120 }, { _id: 1, type: "chocolate", orderDate: new Date("2021-03-20T11:30:05Z"), state: "WA", price: 14, quantity: 140 }, { _id: 2, type: "vanilla", orderDate: new Date("2021-01-11T06:31:15Z"), state: "CA", price: 12, quantity: 145 }, { _id: 3, type: "vanilla", orderDate: new Date("2020-02-08T13:13:23Z"), state: "WA", price: 13, quantity: 104 }, { _id: 4, type: "strawberry", orderDate: new Date("2019-05-18T16:09:01Z"), state: "CA", price: 41, quantity: 162 }, { _id: 5, type: "strawberry", orderDate: new Date("2019-01-08T06:12:03Z"), state: "WA", price: 43, quantity: 134 } ] )
The following example creates multiple indexes on the cakeSales
collection:
db.cakeSales.createIndexes( [ { "type": 1 }, { "orderDate": 1 }, { "state": 1 }, { "orderDate": 1, "state": -1 } ] )
The first three indexes are on single fields and in ascending order
(1
).
The last index is on orderDate
in ascending order (1
) and
state
in descending order (-1
).
Additional Information
For additional information about indexes, refer to:
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.