How to use Facets with Atlas Search¶

Navigate to the Atlas Search page for your project.¶

1. If it is not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
2. If it is not already displayed, select your desired project from the Projects menu in the navigation bar.
3. Click your cluster's name.
4. Click the Search tab.

Click Create Index.¶

Select the JSON Editor Configuration Method and click Next.¶

The Visual Editor Configuration Method doesn't support stringFacet.

Enter the Index Name, and set the Database and Collection.¶

1. In the Index Name field, enter default.

   The index name defaults to default. You can leave the default name in place or choose one of your own.

   Note

   If you name your index default, you don't need to specify an index parameter when using the $search pipeline stage. Otherwise, you must specify the index name using the index parameter.

   Index names must be unique within their namespace.

2. In the Database and Collection section, find the sample_mflix database, and select the movies collection.

Replace the default index definition with the following index definition and then click Next.¶

{
  "mappings": {
    "dynamic": false,
    "fields": {
      "genres": {
        "type": "stringFacet"
      },
      "year": {
        "type": "number"
      },
      "released": {
        "type": "date"
      }
    }
  }
}

The index definition above uses lucene.standard as the default analyzer for both indexing and querying the fields and specifies the following for the fields to 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.

Connect to your cluster in mongosh.¶

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

Use the sample_mflix database.¶

Run the following command at mongosh prompt:

use sample_mflix

Run the following query on the movies collection.¶

The following query searches for movies released near November 11, 1921. It specifies a pivot distance from origin of approximately three months. It requests metadata on the genres and year field. The query requests a count of the:

• Number of movies in each genre in the genres string array field
• Number of movies in the years 1910 to 1939, inclusive

db.movies.aggregate([
  {
    "$searchMeta": {
      "facet": {
        "operator": {
          "near": {
            "path": "released",
            "origin": ISODate("1921-11-01T00:00:00.000+00:00"),
            "pivot": 7776000000
          }
        },
        "facets": {
          "genresFacet": {
            "type": "string",
            "path": "genres"
          },
          "yearFacet" : {
            "type" : "number",
            "path" : "year",
            "boundaries" : [1910,1920,1930,1940]
          }
        }
      }
    }
  }
])

The query returns the following results:

1
{
2
  "count" : {
3
    "lowerBound" : NumberLong(23026)
4
  },
5
  "facet" : {
6
    "genresFacet" : {
7
      "buckets" : [
8
        { "_id" : "Drama", "count" : NumberLong(13527) },
9
        { "_id" : "Comedy", "count" : NumberLong(6922) },
10
        { "_id" : "Romance", "count" : NumberLong(3615) },
11
        { "_id" : "Crime", "count" : NumberLong(2649) },
12
        { "_id" : "Thriller", "count" : NumberLong(2603) },
13
        { "_id" : "Action", "count" : NumberLong(2505) },
14
        { "_id" : "Documentary", "count" : NumberLong(2041) },
15
        { "_id" : "Adventure", "count" : NumberLong(2016) },
16
        { "_id" : "Horror", "count" : NumberLong(1662) },
17
        { "_id" : "Biography", "count" : NumberLong(1373) }
18
      ]
19
    },
20
    "yearFacet" : {
21
      "buckets" : [
22
        { "_id" : 1910, "count" : NumberLong(23) },
23
        { "_id" : 1920, "count" : NumberLong(89) },
24
        { "_id" : 1930, "count" : NumberLong(308) }
25
      ]
26
    }
27
  }
28
}

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 the 1910 bucket
• 1920, exclusive upper bound for the 1910 bucket and inclusive lower bound for the 1920 bucket
• 1930, exclusive upper bound for the 1920 bucket and inclusive lower bound for the 1930 bucket