Indexes in MongoDB are not one-size-fits-all; there are several types of indexes that cater to different querying needs.

Understanding the various index types helps in designing efficient and optimized database schemas. In this lesson, we will explore different types of indexes, including their syntax, parameters, and examples.

Example Setup

First, let's insert some documents into the library collection to work with:

db.library.insertMany([
  { title: "To Kill a Mockingbird", author: "Harper Lee", genre: "Fiction", pages: 281 },
  { title: "1984", author: "George Orwell", genre: "Dystopian", pages: 328 },
  { title: "Moby Dick", author: "Herman Melville", genre: "Adventure", pages: 635 },
  { title: "The Great Gatsby", author: "F. Scott Fitzgerald", genre: "Fiction", pages: 180 },
  { title: "War and Peace", author: "Leo Tolstoy", genre: "Historical", pages: 1225 }
])

Single Field Index

A single field index is used to index a single field of a document. It can speed up queries that filter or sort by that field.

Syntax

db.collection.createIndex({ <field>: <type> })

• field: The field to be indexed.
• type: The type of index, usually 1 for ascending order or -1 for descending order.

Example

db.library.createIndex({ author: 1 })

• db.library.createIndex({ author: 1 }): This command creates an index on the author field in ascending order. This index will speed up queries that filter or sort by the author field.

Compound Index

A compound index is used to index multiple fields within a document. It can speed up queries that filter or sort by multiple fields.

Syntax

db.collection.createIndex({ <field1>: <type1>, <field2>: <type2> })

• field1, field2: The fields to be indexed.
• type1, type2: The type of index for each field. Use 1 for ascending order or -1 for descending order.

Example

db.library.createIndex({ genre: 1, pages: -1 })

• db.library.createIndex({ genre: 1, pages: -1 }): This command creates a compound index on the genre field in ascending order and the pages field in descending order. This index will speed up queries that filter or sort by both genre and pages.

Multikey Index

A multikey index is used to index an array field in a document. It indexes each element of the array, allowing queries to filter or sort based on array values.

Syntax

db.collection.createIndex({ <arrayField>: <type> })

• arrayField: The array field to be indexed.
• type: The type of index, usually 1 for ascending order or -1 for descending order.

Example

Insert documents with an array field into the library collection:

db.library.insertMany([
  { title: "In Search of Lost Time", authors: ["Marcel Proust"], genre: "Modernist" },
  { title: "Ulysses", authors: ["James Joyce"], genre: "Modernist" },
  { title: "Don Quixote", authors: ["Miguel de Cervantes"], genre: "Adventure" }
])

Command:

db.library.createIndex({ authors: 1 })

• db.library.createIndex({ authors: 1 }): This command creates a multikey index on the authors array field. This index will speed up queries that filter or sort by the elements of the authors array.

Text Index

A text index is used to perform text searches on a field or fields. It supports language-specific text search, allowing for efficient search of words within string content.

Syntax

db.collection.createIndex({ <field>: "text" })

• field: The field to be indexed as text.

Example

Command:

db.library.createIndex({ title: "text", genre: "text" })

• db.library.createIndex({ title: "text", genre: "text" }): This command creates a text index on the title and genre fields. This index enables text search queries on these fields.

Geospatial Index

A geospatial index is used to support queries that involve geographic locations. This type of index is essential for applications that require location-based queries, such as finding nearby points of interest or performing spatial analysis.

Syntax

db.collection.createIndex({ <locationField>: "2dsphere" })

• locationField: The field containing geographic location data.

Example

Setup:

Insert documents with location data into the places collection:

db.places.insertMany([
  { name: "Central Park", location: { type: "Point", coordinates: [-73.968285, 40.785091] } },
  { name: "Golden Gate Bridge", location: { type: "Point", coordinates: [-122.478255, 37.819929] } }
])

Command:

db.places.createIndex({ location: "2dsphere" })

• db.places.createIndex({ location: "2dsphere" }): This command creates a geospatial index on the location field using the "2dsphere" type. This index will speed up queries that involve geographic location data.

Hashed Index

A hashed index is used to support hash-based sharding and speeds up equality queries. This type of index is particularly useful for distributing data evenly across a sharded cluster and improving query performance for equality comparisons.

Syntax

db.collection.createIndex({ <field>: "hashed" })

• field: The field to be indexed as hashed.

Example

db.library.createIndex({ title: "hashed" })

• db.library.createIndex({ title: "hashed" }): This command creates a hashed index on the title field. This index supports hash-based sharding and speeds up equality queries on the title field.

Wildcard Index

A wildcard index is used to index fields that have a dynamic schema. This type of index is useful when the document structure is not consistent, and you want to index all fields or specific fields that match a pattern.

Example

Command:

db.library.createIndex({ "$**": 1 })

• db.library.createIndex({ "$**": 1 }): This command creates a wildcard index that indexes all fields in the documents. This index is beneficial for collections with a dynamic schema where the document structure can vary.

Understanding the different types of indexes in MongoDB is crucial for optimizing query performance and ensuring efficient data retrieval. Each index type serves a specific purpose and is suited for different query patterns. By using the appropriate index type, you can significantly enhance the performance of your MongoDB queries.

In next lessons, we will explore more specific indexing strategies, such as single field indexes, compound indexes, multikey indexes, and text indexes.