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Work with Indexes

Wildcard Indexes

On this page

  • Overview
  • Sample Data
  • Create a Wildcard Index
  • Collation

Overview

Wildcard indexes enable queries against unknown or arbitrary fields. These indexes can be beneficial if you are using a dynamic schema.

Sample Data

The examples in this guide use the sample_mflix.movies collection from the Atlas sample datasets. To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see the Get Started with PyMongo.

Create a Wildcard Index

The following example creates an ascending wildcard index on all values of the location field, including values nested in subdocuments and arrays:

movies.create_index({ "location.$**": pymongo.ASCENDING })

For more information, see the Wildcard Indexes page in the MongoDB Server manual.

Collation

When you create an index, you can specify a default collation for all operations you perform on fields that are included in the index.

A collation is a set of language-specific rules for string comparison, such as for letter case and accent marks.

To specify a collation, create an instance of the Collation class or a Python dictionary. For a list of options to pass to the Collation constructor or include as keys in the dictionary, see Collation in the MongoDB Server manual.

Tip

Import Collation

To create an instance of the Collation class, you must import it from pymongo.collation.

To use an index with a specified collation, your operation must meet the following criteria:

  • The operation uses the same collation as the one specified in the index.

  • The operation is covered by the index that contains the collation.

The following example creates the same index as the previous example, but with a default collation of fr_CA:

movies.create_index({ "location.$**": pymongo.ASCENDING },
                    collation=Collation(locale='fr_CA'))

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