Query Optimization¶

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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 the mongo shell, 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 both of the following apply:

all the fields in the query are part of an index, and
all the fields returned in the results are in the same index.

For example, a collection inventory has the following index on the type and item fields:

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

This index will cover 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.

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¶

Restrictions on Indexed Fields¶

An index cannot cover a query if:

any of the indexed fields in any of the documents in the collection includes an array. If an indexed field is an array, the index becomes a multi-key index and cannot support a covered query.
A geospatial indexes cannot cover a query.
any of the indexed fields in the query predicate or returned in the projection are fields in embedded documents. [2] For example, consider a collection users with documents of the following form:
```
{ _id: 1, user: { login: "tester" } }
```
The collection has the following index:
```
{ "user.login": 1 }
```
The { "user.login": 1 } index will not cover the query below because this index is on a field in an embedded document:
```
db.users.find( { "user.login": "tester" }, { "user.login": 1, _id: 0 } )
```
This query can still use the { "user.login": 1 } index to find matching documents but it will examine and fetch documents to satisfy the query.

Restrictions on Sharded Collection¶

An index cannot cover a query on a sharded collection when run against a mongos if the index does not contain the shard key, with the following exception for the _id index: If a query on a sharded collection only specifies a condition on the _id field and returns only the _id field, the _id index can cover the query when run against a mongos even if the _id field is not the shard key.

Changed in version 3.0: In previous versions, an index cannot cover a query on a sharded collection when run against a mongos.

`explain`¶

To determine whether a query is a covered query, use the db.collection.explain() or the explain() method and review the results.

db.collection.explain() provides information on the execution of other operations, such as db.collection.update(). See db.collection.explain() for details.

For more information see Measure Index Use.

[2]	To index fields in embedded documents, use dot notation.

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