Indexes
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Overview
In this guide, you can learn how to use indexes with the MongoDB .NET/C# Driver. Indexes can improve the efficiency of queries and add functionality to querying and storing documents.
Without indexes, MongoDB must scan every document in a collection to find the documents that match each query. These collection scans are slow and can negatively affect the performance of your application. However, if an appropriate index exists for a query, MongoDB can use the index to limit the documents it must inspect.
Query Coverage and Performance
When you execute a query against MongoDB, your query can include various elements:
Query criteria that specify fields and values that you are looking for
Options that affect the query's execution, such as read concern
Projection criteria to specify the fields you want MongoDB to return
Sort criteria to specify the order of documents returned from MongoDB
When all the fields specified in the query, projection, and sort are in the same index, MongoDB returns results directly from that index, also called a covered query.
For more information about how to ensure your index covers your query criteria and projection, see the Covered Query section in the MongoDB server manual.
Operational Considerations
To improve query performance, build indexes on fields that appear often in your application's queries and operations that return sorted results. Each index that you add consumes disk space and memory when active, so it might be necessary to track index memory and disk usage for capacity planning. In addition, when a write operation updates an indexed field, MongoDB also updates the related index.
Since MongoDB supports dynamic schemas, applications can query against fields whose names cannot be known in advance or are arbitrary. MongoDB 4.2 introduced wildcard indexes to help support these queries. Wildcard indexes are not designed to replace workload-based index planning.
For more information about designing your data model and choosing indexes appropriate for your application, see the Server documentation on Indexing Strategies and Data Modeling and Indexes.
Index Types
MongoDB provides several different index types to support querying your data. The following sections describe the most common index types and provide sample code for creating each index type.
Note
These example uses the sample_mflix.movies
and sample_mflix.theaters
collections
from the Atlas sample datasets. To learn how to create a
free MongoDB Atlas cluster and load the sample datasets, see Quick Start.
Single Field Indexes
Single-field indexes are indexes with a reference to a single field within a collection's documents. They improve single field query and sort performance, and support TTL Indexes that automatically remove documents from a collection after a certain amount of time or at a specific clock time.
Note
The _id_
index is an example of a single-field index. This index is automatically
created on the _id
field when a new collection is created.
The following example creates an index in ascending order on the title
field within
the sample_mflix.movies
collection:
var indexModel = new CreateIndexModel<Movie>(Builders<Movie>.IndexKeys.Ascending(m => m.Title)); collection.Indexes.CreateOne(indexModel);
The following is an example of a query that is covered by the index created in the preceding code snippet:
// Define query parameters var filter = Builders<Movie>.Filter.Eq(m => m.Title, "Batman"); var sort = Builders<Movie>.Sort.Ascending(m => m.Title); var projection = Builders<Movie>.Projection.Include(m => m.Title).Exclude(m => m.Id); // Execute query var results = collection.Find(filter).Sort(sort).Project(projection);
For more information, see Single Field Indexes in the Server manual.
Compound Indexes
Compound indexes hold references to multiple fields within a collection's documents, improving query and sort performance.
The following example creates a compound index on the type
and rated
fields within
the sample_mflix.movies
collection:
var indexModel = new CreateIndexModel<Movie>(Builders<Movie>.IndexKeys .Ascending(m => m.Type) .Ascending(m => m.Rated)); collection.Indexes.CreateOne(indexModel);
The following is an example of a query that is covered by the index created in the preceding code snippet:
// Define query parameters var typeFilter = Builders<Movie>.Filter.Eq(m => m.Type, "movie"); var ratedFilter = Builders<Movie>.Filter.Eq(m => m.Rated, "G"); var filter = Builders<Movie>.Filter.And(typeFilter, ratedFilter); var sort = Builders<Movie>.Sort.Ascending(m => m.Type).Ascending(m => m.Rated); var projection = Builders<Movie>.Projection .Include(m => m.Type) .Include(m => m.Rated) .Exclude(m => m.Id); // Execute query var results = collection.Find(filter).Sort(sort).Project(projection);
For more information, see Compound Indexes in the Server manual.
Multikey Indexes
Multikey indexes collect and sort data from fields containing array values. You can define a multikey index using the same syntax as a single field or compound index.
The following example creates a compound, multikey index on the rated
, genres
(an array of Strings), and title
fields within the sample_mflix.movies
collection:
var indexModel = new CreateIndexModel<Movie>(Builders<Movie>.IndexKeys .Ascending(m => m.Rated) .Ascending(m => m.Genres) .Ascending(m => m.Title)); collection.Indexes.CreateOne(indexModel);
The following is an example of a query that is covered by the index created in the preceding code snippet:
// Define query parameters var genreFilter = Builders<Movie>.Filter.AnyEq(m => m.Genres, "Animation"); var ratedFilter = Builders<Movie>.Filter.Eq(m => m.Rated, "G"); var filter = Builders<Movie>.Filter.And(genreFilter, ratedFilter); var sort = Builders<Movie>.Sort.Ascending(m => m.Title); var projection = Builders<Movie>.Projection .Include(m => m.Title) .Include(m => m.Rated) .Exclude(m => m.Id); // Execute query var results = collection.Find(filter).Sort(sort).Project(projection);
Multikey indexes behave differently from other indexes in terms of query coverage, index bound computation, and sort behavior. To learn more about multikey indexes, including a discussion of their behavior and limitations, see the Multikey Indexes page in the Server manual.
Clustered Indexes
Clustered indexes instruct a collection to store documents ordered by a key value.
To create a clustered index, specify the clustered index
option with the _id
field as the key and the Unique
property as
true
when you create your collection. A collection can only contain a single clustered
index. If you want to create a clustered index, then it must be specified when you create
a collection.
The following example creates a clustered index on the _id
field while creating a new
sample_mflix.reviews
collection:
var database = mongoClient.GetDatabase("sample_mflix"); var clusteredIndexOptions = new ClusteredIndexOptions<Review> { Key = Builders<Review>.IndexKeys.Ascending(r => r.Id), Unique = true }; database.CreateCollection("reviews", new CreateCollectionOptions<Review> { ClusteredIndex = clusteredIndexOptions });
To learn more, see Clustered Indexes and Clustered Collections in the Server manual.
Atlas Search Indexes
The Atlas Search feature enables you to perform full-text searches on collections hosted on MongoDB Atlas. The indexes specify the behavior of the search and which fields to index.
To learn more about MongoDB Atlas Search, see the Atlas Search Indexes documentation.
Note
The Atlas Search Index management methods run asynchronously. The driver methods can return before confirming that they ran successfully. To determine the current status of the indexes, call the IMongoSearchIndexManager.List() method.
The following sections contain links to tutorials that demonstrate how to create and interact with Atlas Search indexes.
Create a Search Index
Before you can perform a search on an Atlas collection, you must first create an Atlas Search index on the collection. To learn how to create an Atlas Search index using the .NET/C# Driver, see Create an Atlas Search Index in the Atlas manual and select C# from the language dropdown.
List Search Indexes
To learn how to view a list of your Atlas Search indexes using the .NET/C# Driver, see View an Atlas Search Index in the Atlas manual and select C# from the language dropdown.
Update a Search Index
To learn how to modify an existing Atlas Search index using the .NET/C# Driver, see Edit an Atlas Search Index in the Atlas manual and select C# from the language dropdown.
Drop a Search Index
To learn how to delete an Atlas Search index using the .NET/C# Driver, see Delete an Atlas Search Index in the Atlas manual and select C# from the language dropdown.
Text Indexes
Text indexes support text search queries on string content. These indexes can include any field whose value is a string or an array of string elements. MongoDB supports text search for various languages. You can specify the default language as an option when creating the index.
Tip
MongoDB offers an improved full-text search solution, Atlas Search. To learn more about Atlas Search indexes and how to use them, see the Atlas Search Indexes section of this guide.
Note that text indexes cannot support Atlas Search queries, and Atlas Search indexes cannot support text queries.
Single Field
The following example creates a text index on the plot
field within the
sample_mflix.movies
collection:
var indexModel = new CreateIndexModel<Movie>(Builders<Movie>.IndexKeys.Text(m => m.Plot)); collection.Indexes.CreateOne(indexModel);
The following query uses the text index created in the preceding code snippet:
// Define query parameters var filter = Builders<Movie>.Filter.Text("java coffee shop"); var projection = Builders<Movie>.Projection.Include(m => m.Plot).Exclude(m => m.Id); // Execute query var results = collection.Find(filter).Project(projection);
Multiple Fields
A collection can only contain one text index. If you want to create a text index for multiple text fields, you must create a compound index. A text search runs on all the text fields within the compound index.
The following snippet creates a compound text index for the title
and genre
fields within the sample_mflix.movies
collection:
var indexModel = new CreateIndexModel<Movie>(Builders<Movie>.IndexKeys .Text(m => m.Title) .Text(m => m.Genre)); collection.Indexes.CreateOne(indexModel);
For more information, see Compound Text Index Restrictions and Text Indexes in the Server manual.
Geospatial Indexes
MongoDB supports queries of geospatial coordinate data using 2dsphere indexes. With a 2dsphere index, you can query the geospatial data for inclusion, intersection, and proximity.
To create a 2dsphere index, you must specify a field that contains only GeoJSON objects. For more details about this type, see GeoJSON objects in the MongoDB Server manual.
The location.geo
field in the following sample document from the
sample_mflix.theaters
collection is a GeoJSON Point object that describes the
coordinates of the theater:
{ "_id" : ObjectId("59a47286cfa9a3a73e51e75c"), "theaterId" : 104, "location" : { "address" : { "street1" : "5000 W 147th St", "city" : "Hawthorne", "state" : "CA", "zipcode" : "90250" }, "geo" : { "type" : "Point", "coordinates" : [ -118.36559, 33.897167 ] } } }
The following example creates a 2dsphere
index on the location.geo
field:
Important
Attempting to create a geospatial index on a field that is already covered by a geospatial index results in an error.
var indexModel = new CreateIndexModel<Theater>(Builders<Theater>.IndexKeys.Geo2DSphere(t => t.Location.Geo)); collection.Indexes.CreateOne(indexModel);
The following is an example of a geospatial query using the "location.geo" index:
// Stores the coordinates of the NY MongoDB headquarters var refPoint = GeoJson.Point(GeoJson.Position(-73.98456, 40.7612)); // Creates a filter to match documents that represent locations up to 1000 meters from the specified point directly from the geospatial index var filter = Builders<Theater>.Filter.Near(m => m.Location.Geo, refPoint, 1000.0, 0.0); // Execute the query var results = collection.Find(filter);
MongoDB also supports 2d
indexes for calculating distances on a Euclidean plane and
for working with the "legacy coordinate pairs" syntax used in MongoDB 2.2 and earlier.
To learn more, see Geospatial Queries in the Server manual.
Unique Indexes
Unique indexes ensure that the indexed fields do not store duplicate values. By
default, MongoDB creates a unique index on the _id
field during the creation of a
collection. To create a unique index, specify the fields that you want to prevent
duplication on and set the Unique
option to true
.
The following example creates a unique, descending index on the theaterId
field within
the sample_mflix.theaters
collection.
var options = new CreateIndexOptions { Unique = true }; var indexModel = new CreateIndexModel<Theater>(Builders<Theater>.IndexKeys.Descending(t => t.TheaterId), options); collection.Indexes.CreateOne(indexModel);
If you attempt to perform a write operation that stores a duplicate value that violates the unique index, MongoDB will throw an error that resembles the following:
E11000 duplicate key error index
To learn more, see Unique Indexes in the Server manual.
Wildcard Indexes
Wildcard indexes enable queries against unknown or arbitrary fields. These indexes can be beneficial if you are using a dynamic schema.
The following example creates an ascending wildcard index on all
values of the location
field within the sample_mflix.theaters
collection,
including values nested in subdocuments and arrays:
var indexModel = new CreateIndexModel<Theater>(Builders<Theater>.IndexKeys.Wildcard(t => t.Location)); collection.Indexes.CreateOne(indexModel);
For more information, see the Wildcard Indexes page in the Server manual.
List Indexes
You can use the List() method to retrieve a list of indexes in your collection.
The following example uses the List()
method to list
all indexes in a collection:
var indexes = collection.Indexes.List(); foreach (var index in indexes.ToList()) { Console.WriteLine(index); }