Indexes are a crucial part of optimizing query performance in MongoDB. They allow the database to quickly locate and access the data without having to scan every document in a collection. In this section, we will explore the different types of indexes available in MongoDB, their use cases, and how to create them.
- Single Field Indexes
Definition
A single field index is an index on a single field of a document. It is the most basic type of index and is used to improve the performance of queries that filter or sort by that field.
Example
In this example, the 1 indicates an ascending order. You can also use -1 for descending order.
Use Case
Single field indexes are useful when you frequently query a collection based on a single field, such as finding a user by their username.
- Compound Indexes
Definition
A compound index is an index on multiple fields. It can support queries that filter or sort by multiple fields.
Example
// Create a compound index on the "lastName" and "firstName" fields
db.collection.createIndex({ lastName: 1, firstName: 1 });Use Case
Compound indexes are beneficial when you have queries that filter or sort by multiple fields, such as finding a user by their last name and first name.
- Multikey Indexes
Definition
A multikey index is an index on an array field. MongoDB creates an index entry for each element in the array.
Example
Use Case
Multikey indexes are useful when you need to query documents based on the elements of an array, such as finding articles tagged with specific keywords.
- Text Indexes
Definition
A text index supports text search queries on string content. It can index any field that contains string data.
Example
// Create a text index on the "description" field
db.collection.createIndex({ description: "text" });Use Case
Text indexes are ideal for full-text search capabilities, such as searching for articles containing specific words or phrases.
- Geospatial Indexes
Definition
Geospatial indexes support queries that calculate geometries on a 2D plane or a sphere. MongoDB provides two types of geospatial indexes: 2d and 2dsphere.
Example
// Create a 2dsphere index on the "location" field
db.collection.createIndex({ location: "2dsphere" });Use Case
Geospatial indexes are used for location-based queries, such as finding nearby restaurants or stores.
- Hashed Indexes
Definition
A hashed index is an index on a field's hashed value. It is used to support equality queries.
Example
Use Case
Hashed indexes are useful for sharding, where documents are distributed across multiple shards based on the hashed value of a field.
- Wildcard Indexes
Definition
A wildcard index is an index on all fields or a subset of fields in a document. It is useful for indexing fields with unknown or dynamic names.
Example
Use Case
Wildcard indexes are beneficial when you have documents with a large number of fields or fields with dynamic names, such as user-generated content.
Summary
In this section, we covered the various types of indexes available in MongoDB:
- Single Field Indexes: Indexes on a single field.
- Compound Indexes: Indexes on multiple fields.
- Multikey Indexes: Indexes on array fields.
- Text Indexes: Indexes for full-text search.
- Geospatial Indexes: Indexes for location-based queries.
- Hashed Indexes: Indexes on hashed values for equality queries.
- Wildcard Indexes: Indexes on all or a subset of fields.
Understanding and utilizing these indexes can significantly improve the performance of your MongoDB queries. In the next section, we will delve into the Aggregation Framework, which allows for more complex data processing and analysis.