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• Create an Index to Support Read Operations
• Query Selectivity
• Covered Query

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.

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

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

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.

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.

For example, consider a collection userdata 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 cover the query below:

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