Indexes

Create and Manage Indexes in MongoDB Atlas

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.

Create and Manage Indexes with a Driver Method or the MongoDB Shell

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

Index Names

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:

db.products.createIndex(
  { 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.

Single Field

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.

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.

Compound Index

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.

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:

• Compound Indexes,

• Sort on Multiple Fields, and

• The ESR (Equality, Sort, Range) Rule

Multikey Index

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.

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

Geospatial Index

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.

Text Search 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 on Self-Managed Deployments.

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.

Hashed 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.

Wildcard Indexes

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.

Unique 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.

Partial Indexes

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.

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

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.

Hidden Indexes

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.