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Query Optimization

On this page

  • Create an Index to Support Read Operations
  • Query Selectivity
  • Covered Query
  • Example
  • Embedded Documents
  • Multikey Covering
  • Performance
  • Limitations
  • explain

Indexes improve the efficiency of read operations by reducing the amount of data that query operations need to process. This simplifies the work associated with fulfilling queries within MongoDB.

Create an Index to Support Read Operations

If your application queries a collection on a particular field or set of fields, then an index on the queried field or a compound index on the set of fields can prevent the query from scanning the whole collection to find and return the query results. For more information about indexes, see the complete documentation of indexes in MongoDB.

Example

An application queries the inventory collection on the type field. The value of the type field is user-driven.

var typeValue = <someUserInput>;
db.inventory.find( { type: typeValue } );

To improve the performance of this query, add an ascending or a descending index to the inventory collection on the type field. [1] In mongosh, you can create indexes using the db.collection.createIndex() method:

db.inventory.createIndex( { type: 1 } )

This index can prevent the above query on type from scanning the whole collection to return the results.

To analyze the performance of the query with an index, see Analyze Query Performance.

In addition to optimizing read operations, indexes can support sort operations and allow for a more efficient storage utilization. See db.collection.createIndex() and Indexes for more information about index creation.

[1] For single-field indexes, the selection between ascending and descending order is immaterial. For compound indexes, the selection is important. See indexing order for more details.

Query Selectivity

Query selectivity refers to how well the query predicate excludes or filters out documents in a collection. Query selectivity can determine whether or not queries can use indexes effectively or even use indexes at all.

More selective queries match a smaller percentage of documents. For instance, an equality match on the unique _id field is highly selective as it can match at most one document.

Less selective queries match a larger percentage of documents. Less selective queries cannot use indexes effectively or even at all.

For instance, the inequality operators $nin and $ne are not very selective since they often match a large portion of the index. As a result, in many cases, a $nin or $ne query with an index may perform no better than a $nin or $ne query that must scan all documents in a collection.

The selectivity of regular expressions depends on the expressions themselves. For details, see regular expression and index use.

Covered Query

A covered query is a query that can be satisfied entirely using an index and does not have to examine any documents. An index covers a query when all of the following apply:

  • All the fields in the query (both as specified by the application and as needed internally such as for sharding purposes) are part of an index.

  • All the fields returned in the results are in the same index.

  • No fields in the query are equal to null. For example, the following query predicates cannot result in covered queries:

    • { "field": null }

    • { "field": { $eq: null } }

Example

An inventory collection has the following index on the type and item fields:

db.inventory.createIndex( { type: 1, item: 1 } )

The index covers the following operation which queries on the type and item fields and returns only the item field:

db.inventory.find(
   { type: "food", item:/^c/ },
   { item: 1, _id: 0 }
)

For the specified index to cover the query, the projection document must explicitly specify _id: 0 to exclude the _id field from the result since the index does not include the _id field.

Embedded Documents

An index can cover a query on fields within embedded documents.

For example, consider a userdata collection with documents of the following form:

db.userdata.insertOne(
   { _id: 1, user: { login: "tester" } }
)

The collection has the following index:

db.userdata.createIndex(
   { "user.login": 1 }
)

The { "user.login": 1 } index covers the following query:

db.userdata.find(
   { "user.login": "tester" },
   { "user.login": 1, _id: 0 }
)

Note

To index fields in embedded documents, use dot notation. See Create an Index on an Embedded Field.

Multikey Covering

Multikey indexes can cover queries over the non-array fields if the index tracks which field or fields cause the index to be multikey.

Multikey indexes cannot cover queries over array field(s).

For an example of a covered query with a multikey index, see Covered Queries on the multikey indexes page.

Performance

Because the index contains all fields required by the query, MongoDB can both match the query conditions and return the results using only the index.

Querying only the index can be much faster than querying documents outside of the index. Index keys are typically smaller than the documents they catalog, and indexes are typically available in RAM or located sequentially on disk.

Limitations

Index Types

Not all index types can cover queries. For details on covered index support, refer to the documentation page for the corresponding index type.

Restrictions on Sharded Collection

When run on mongos, indexes can only cover queries on sharded collections if the index contains the shard key.

explain

To determine whether a query is a covered query, use the db.collection.explain() or the explain() method. See Covered Queries.

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