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Indexes support the efficient execution of queries in MongoDB. Without indexes, MongoDB must perform a collection scan, i.e. scan every document in a collection, to select those documents that match the query statement. If an appropriate index exists for a query, MongoDB can use the index to limit the number of documents it must inspect.

Indexes support efficient execution of queries in MongoDB. Without indexes, MongoDB must scan every document in a collection to return query results. If an appropriate index exists for a query, MongoDB uses the index to limit the number of documents it must scan.

Although indexes improve query performance, adding an index has negative performance impact for write operations. For collections with a high write-to-read ratio, indexes are expensive because each insert must also update any indexes.

If your application is repeatedly running queries on the same fields, you can create an index on those fields to improve performance. For example, consider the following scenarios:

Index Type
A human resources department often needs to look up employees by employee ID. You can create an index on the employee ID field to improve query performance.

A salesperson often needs to look up client information by location. Location is stored in an embedded object with fields like state, city, and zipcode. You can create an index on the location object to improve performance for queries on that object.


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.

Single Field Index on an embedded document
A grocery store manager often needs to look up inventory items by name and quantity to determine which items are low stock. You can create a single index on both the item and quantity fields to improve query performance.

You can create and manage indexes in MongoDB Atlas, with a driver method, or with the MongoDB Shell. MongoDB Atlas is the fully managed service for MongoDB deployments in the cloud.

For deployments hosted in MongoDB Atlas, you can create and manage indexes with the MongoDB Atlas UI or the Atlas CLI. MongoDB Atlas also includes a Performance Advisor that recommends indexes to improve slow queries, ranks suggested indexes by impact, and recommends which indexes to drop.

To learn how to create and manage indexes the MongoDB Atlas UI or the Atlas CLI, see Create, View, Drop, and Hide Indexes.

To learn more about the MongoDB Atlas Performance Advisor, see Monitor and Improve Slow Queries.

You can create and manage indexes with a driver method or the MongoDB Shell. To learn more, see the resources on this page.

Indexes are special data structures that store a small portion of the collection's data set in an easy-to-traverse form. MongoDB indexes use a B-tree data structure.

The index stores the value of a specific field or set of fields, ordered by the value of the field. The ordering of the index entries supports efficient equality matches and range-based query operations. In addition, MongoDB can return sorted results by using the ordering in the index.

The following diagram illustrates a query that selects and orders the matching documents using an index:

Diagram of a query that uses an index to select and return sorted results. The index stores ``score`` values in ascending order. MongoDB can traverse the index in either ascending or descending order to return sorted results.
click to enlarge

Fundamentally, indexes in MongoDB are similar to indexes in other database systems. MongoDB defines indexes at the collection level and supports indexes on any field or sub-field of the documents in a MongoDB collection.

MongoDB creates a unique index on the _id field during the creation of a collection. The _id index prevents clients from inserting two documents with the same value for the _id field. You cannot drop this index on the _id field.


In sharded clusters, if you do not use the _id field as the shard key, then your application must ensure the uniqueness of the values in the _id field to prevent errors. This is most-often done by using a standard auto-generated ObjectId.

Use the Select your language drop-down menu in the upper-right to set the language of the examples on this page.

[1] MongoDB indexes use a B-tree data structure.

The default name for an index is the concatenation of the indexed keys and each key's direction in the index ( i.e. 1 or -1) using underscores as a separator. For example, an index created on { item : 1, quantity: -1 } has the name item_1_quantity_-1.

You can create indexes with a custom name, such as one that is more human-readable than the default. For example, consider an application that frequently queries the products collection to populate data on existing inventory. The following createIndex() method creates an index on item and quantity named query for inventory:

{ item: 1, quantity: -1 } ,
{ name: "query for inventory" }

You can view index names using the db.collection.getIndexes() method. You cannot rename an index once created. Instead, you must drop and re-create the index with a new name.

MongoDB provides a number of different index types to support specific types of data and queries.

In addition to the MongoDB-defined _id index, MongoDB supports the creation of user-defined ascending/descending indexes on a single field of a document.

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

For a single-field index and sort operations, the sort order (i.e. ascending or descending) of the index key does not matter because MongoDB can traverse the index in either direction.

See Single Field Indexes and Sort with a Single Field Index for more information on single-field indexes.

MongoDB also supports user-defined indexes on multiple fields, i.e. compound indexes.

The order of fields listed in a compound index has significance. For instance, if a compound index consists of { userid: 1, score: -1 }, the index sorts first by userid and then, within each userid value, sorts by score.

Diagram of a compound index on the ``userid`` field (ascending) and the ``score`` field (descending). The index sorts first by the ``userid`` field and then by the ``score`` field.

For compound indexes and sort operations, the sort order (i.e. ascending or descending) of the index keys can determine whether the index can support a sort operation. See Sort Order for more information on the impact of index order on results in compound indexes.

See also:

MongoDB uses multikey indexes to index the content stored in arrays. If you index a field that holds an array value, MongoDB creates separate index entries for every unique element of the array. These multikey indexes allow queries to select documents that contain arrays by matching on element or elements of the arrays. MongoDB automatically determines whether to create a multikey index if the indexed field contains an array value; you do not need to explicitly specify the multikey type.

Diagram of a multikey index on the ```` field. The ``addr`` field contains an array of address documents. The address documents contain the ``zip`` field.

See Multikey Indexes and Multikey Index Bounds for more information on multikey indexes.

To support efficient queries of geospatial coordinate data, MongoDB provides two special indexes: 2d indexes that uses planar geometry when returning results and 2dsphere indexes that use spherical geometry to return results.

This index is a sparse index. If you want to use a sparse index to create a compound index, first review the special considerations of using sparse compound indexes.

See Geospatial Indexes for a high level introduction to geospatial indexes.

For data hosted on MongoDB Atlas, you can support full-text search with Atlas Search indexes. To learn more, see Create an Atlas Search Index.

For self-managed (non-Atlas) deployments, MongoDB provides a text index type that supports searching for string content in a collection. To learn more about self-managed text indexes, see Text Indexes.

This index is a sparse index. If you want to use a sparse index to create a compound index, first review the special considerations of using sparse compound indexes.

To support hash based sharding, MongoDB provides a hashed index type, which indexes the hash of the value of a field. These indexes have a more random distribution of values along their range, but only support equality matches and cannot support range-based queries.

Starting in MongoDB 4.2, you can use wildcard indexes to support queries against multiple, arbitrary, or unknown fields. When you create a wildcard index, you specify $** to represent all fields or all values, allowing you to index any of the following with a single command:

  • All values of a field

  • All fields in a document

  • All fields in a document except specific field paths

  • Multiple specific fields in a document

To learn more, see Wildcard Indexes.

The unique property for an index causes MongoDB to reject duplicate values for the indexed field. Other than the unique constraint, unique indexes are functionally interchangeable with other MongoDB indexes.

New in version 3.2.

Partial indexes only index the documents in a collection that meet a specified filter expression. By indexing a subset of the documents in a collection, partial indexes have lower storage requirements and reduced performance costs for index creation and maintenance.

Partial indexes offer a superset of the functionality of sparse indexes and should be preferred over sparse indexes.

The sparse property of an index ensures that the index only contain entries for documents that have the indexed field. The index skips documents that do not have the indexed field.

You can combine the sparse index option with the unique index option to prevent inserting documents that have duplicate values for the indexed field(s) and skip indexing documents that lack the indexed field(s).

TTL indexes are special indexes that MongoDB can use to automatically remove documents from a collection after a certain amount of time. This is ideal for certain types of information like machine generated event data, logs, and session information that only need to persist in a database for a finite amount of time.

See: Expire Data from Collections by Setting TTL for implementation instructions.

New in version 4.4.

Hidden indexes are not visible to the query planner and cannot be used to support a query.

By hiding an index from the planner, users can evaluate the potential impact of dropping an index without actually dropping the index. If the impact is negative, the user can unhide the index instead of having to recreate a dropped index. And because indexes are fully maintained while hidden, the indexes are immediately available for use once unhidden.

Except for the _id index, you can hide any indexes.

Indexes can improve the efficiency of read operations. The Analyze Query Performance tutorial provides an example of the execution statistics of a query with and without an index.

For information on how MongoDB chooses an index to use, see query optimizer.

New in version 3.4.

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

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.


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:

{ 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" } )


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 "", regardless of the value you set for the strength parameter.

For more information on collation, see the collation reference page.

The following indexes only support simple binary comparison and do not support collation:

When the query criteria and the projection of a query include only the indexed fields, MongoDB returns results directly from the index without scanning any documents or bringing documents into memory. These covered queries can be very efficient.

Diagram of a query that uses only the index to match the query criteria and return the results. MongoDB does not need to inspect data outside of the index to fulfill the query.
click to enlarge

For more information on covered queries, see Covered Query.

MongoDB can use the intersection of indexes to fulfill queries. For queries that specify compound query conditions, if one index can fulfill a part of a query condition, and another index can fulfill another part of the query condition, then MongoDB can use the intersection of the two indexes to fulfill the query. Whether the use of a compound index or the use of an index intersection is more efficient depends on the particular query and the system.

For details on index intersection, see Index Intersection.

Certain restrictions apply to indexes, such as the length of the index keys or the number of indexes per collection. See Index Limitations for details.

Although indexes can improve query performances, indexes also present some operational considerations. See Operational Considerations for Indexes for more information.

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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