How to Use Facets with Atlas Search
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This tutorial describes how to create an index with a facet
definition on string, date, and numeric fields
in the sample_mflix.movies collection. It shows how to run an Atlas Search
query against those fields for results grouped by values for the string
field and by ranges for the date and numeric fields, including the
count for each of those groups. It takes you through the following
steps:
Set up an Atlas Search index with facet definition on the
genres,released, andyearfields in thesample_mflix.moviescollection.Run Atlas Search query against the
releasedfield in thesample_mflix.moviescollection for results grouped by values for thegenresfield and by ranges for theyearfield.
Prerequisites
To complete these tutorials, in addition to the prerequisites listed in the Atlas Search Tutorials page, you must have an Atlas cluster running one of the following versions:
MongoDB 4.4.11 or later
MongoDB 5.0.4 or later
MongoDB 6.0
Create the Atlas Search Index for Facet
In this section, you will create an Atlas Search index on the genres,
year, and released fields in the sample_mflix.movies
collection.
Navigate to the Atlas Search page for your project.
If it is not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
If it is not already displayed, select your desired project from the Projects menu in the navigation bar.
Click your cluster's name.
Click the Search tab.
Click Create Index.
Select a Configuration Method and click Next.
For a guided experience, select Visual Editor.
To edit the raw index definition, select JSON Editor.
Enter the Index Name, and set the Database and Collection.
In the Index Name field, enter
facet-tutorial.Note
If you name your index
default, you don't need to specify anindexparameter when using the $search pipeline stage. Otherwise, you must specify the index name using theindexparameter.In the Database and Collection section, find the
sample_mflixdatabase, and select themoviescollection.
Specify an index definition.
The following index definition uses lucene.standard as the
default analyzer for both indexing and querying the fields and
specifies the following for the fields to index:
Field Name | Data Type |
|---|---|
genres | |
year | |
released |
You can use the Visual Editor or the JSON Editor in the Atlas user interface to create the index.
Click Create Search Index.
Close the You're All Set! Modal Window.
A modal window appears to let you know your index is building. Click the Close button.
Wait for the index to finish building.
The index should take about one minute to build. While it is
building, the Status column reads Build in
Progress. When it is finished building, the
Status column reads Active.
Search the Collection
➤ Use the Select your language drop-down menu to set the language of the examples in this section.
You can use facet in queries that use the
$search and $searchMeta stages. In this
section, connect to your Atlas cluster and the run the sample query
against the sample_mflix.movies collection using the
$searchMeta stage. MongoDB recommends using the
$searchMeta stage to retrieve metadata results only.
The results show metadata results for two types of facet search. The
genresFacet document shows the number of movies in each genre and
the yearFacet document shows a count of the number of movies
within the boundaries:
1910, inclusive lower bound the1910bucket1920, exclusive upper bound for the1910bucket and inclusive lower bound for the1920bucket1930, exclusive upper bound for the1920bucket and inclusive lower bound for the1930bucket
Continue Learning
To learn more about using facets in Atlas Search, take Unit 9 of the Intro To
MongoDB Course on MongoDB University. The 1.5
hour unit includes an overview of Atlas Search and lessons on creating Atlas Search
indexes, running $search queries using compound operators,
and grouping results using facet.