An index covers a query when the index contains all of the fields scanned by the query. A covered query scans the index and not the collection, which improves query performance.
Indexes can also partially support queries if a subset of the fields queried are indexed.
About this Task
A single collection can have a maximum of 64 indexes. However, too many indexes can degrade performance before that limit is reached. For collections with a high write-to-read ratio, indexes can degrade performance because each insert must also update any indexes.
Steps
Identify common queries
To identify common query patterns in your application, use the
$queryStats aggregation stage. $queryStats reports
metrics for query shapes, which group
queries based on shared fields.
Create indexes to support common queries
After you know which fields your application frequently queries, you can create indexes to support queries on those fields. For more information, see Examples.
Analyze index use
After your application begins using indexes, you can analyze your indexes' effectiveness. To see index statistics and usage, you can:
Use the
$indexStatsaggregation stage.For MongoDB Atlas deployments, view Indexes in the Atlas UI.
Consider deleting unused indexes to improve application performance. For more information, see Remove Unnecessary Indexes.
Repeat this procedure periodically to ensure that your indexes support your current workload.
Examples
The examples on this page use data from the sample_mflix sample dataset. For details on how to load this dataset into your self-managed MongoDB deployment, see Load the sample dataset. If you made any modifications to the sample databases, you may need to drop and recreate the databases to run the examples on this page.
Note
Documents in the movies collection contain additional fields not
shown here.
Create a Single-Key Index
If your application only queries on a single key in a given collection,
then you need to create a single-key index for that collection. For
example, you can create an index on title in the movies
collection:
db.movies.createIndex( { title: 1 } )
This index supports this query:
db.movies.find( { title: 'Mulholland Drive' } )
Create a Compound Index
If your application performs queries on both a single key and multiple
keys, a compound index is more efficient
than a single-key index. For example, you can create an index on the
year, runtime, and title fields:
db.movies.createIndex( { year: 1, runtime: 1, title: 1 } )
Index Prefixes
A compound index supports queries on index prefixes, which are the beginning subsets of indexed fields. For example, the preceding index supports this query:
db.movies.find( { year: 2012, runtime: { $gt: Decimal128( "120" ) } }, { title: 1 } )
For more information and performance considerations on index prefixes, see Index Prefixes.
Create Indexes to Support $text Queries
For data hosted on MongoDB, you can support full-text search with MongoDB Search indexes. To learn more, see Create a MongoDB 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.
Create Vector Search Indexes
Vector Search Indexes support queries on vector embeddings. To create Vector Search Indexes, see Index Fields for Vector Search.
Index Use and Collation
To use an index for string comparisons, an operation must also specify the same collation. If an operation specifies a different collation than the index specifies, the index cannot support string comparisons on the indexed fields.
Warning
Collation-aware index keys might be larger than index keys for indexes without collation because indexes that are configured with collation use ICU collation keys to achieve sort order.