Indexing in MongoDB using Python

In PyMongo, indexing is used to improve the performance of queries by allowing MongoDB to quickly locate and access the requested data without scanning every document in a collection. create_index() defines indexes to optimize queries and enforce constraints. MongoDB auto-indexes _id, but custom indexes can be added on fields using various directions and options.

Syntax

collection.create_index([(field, direction)], **options)

Parameters:

• field: Field to index (e.g., "name")
• direction: pymongo.ASCENDING or pymongo.DESCENDING
• options: Additional index options like unique=True, name="customIndex" etc.

Here is our sample data.

Output

Sample data inserted

Explanation:

• Connects to the local MongoDB server and selects the indexDB database and users collection.
• Clears existing documents in the collection using delete_many({}).
• Inserts three sample user records into the collection using insert_many().

Examples

Example 1: Create index on a field

Output

Explanation: Creates an ascending index on the name field to speed up queries using name.

Example 2: List all index

Output

Explanation: Displays all indexes on the users collection. The default _id_ index and the created name_1 index will be shown.

Example 3: Drop an index

Output

Index dropped

Explanation: Drops the index named "name_1". You must pass the exact index name created earlier.

Example 4: Create index on a new field (default ascending)

Output

index_created_1

Explanation: Creates an ascending index (default) on a new field index_created.

Example 5: Create compound index

Output

Explanation: ascending_index is indexed in ascending order, second_descending_index in descending order. This index improves performance for queries and sorts using both fields together.

Related articles

• Python MongoDB
• Insert and Update Operations in MongoDB
• MongoDB Aggregation Pipeline using Python
• How to Setup MongoDB with Python on Localhost