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Design Search for Your Data Model

Use Views with MongoDB Search

You can create a MongoDB Search index on a View to transform documents and collections. This enables you to partially index a collection, support incompatible data types or data models, and more.

The following examples use the sample_mflix and sample_airbnb sample databases.

Note

Disambiguation

This page discusses standard views. To learn about on-demand materialized views, see On-Demand Materialized Views.

To learn about the differences between the view types, see Comparison with On-Demand Materialized Views.

Requirements

You must use MongoDB 8.0 or higher.

To edit a View, you must have a User Admin role and use the collMod database command.

Limitations

  • MongoDB Search supports Views with the following stages:

  • Index names must be unique across a source collection and all of its Views.

  • MongoDB Search doesn't support view definitions with operators that produce dynamic results, such as the $$USER_ROLES system variable and the $rand aggregation operator.

  • MongoDB Search queries return the original documents as they appear in the source collection.

  • To retrieve the transformed document, use the storedSource option.

Permissions Required

To create a View, your role must have the createCollection privilege.

Examples

The following examples demonstrate how to create a View, partially index documents, and run queries against the index. The examples query the index by running the .aggregate command against the source collection or the View. MongoDB Search supports queries on View directly only on MongoDB v8.1 or later.

Filter Documents

Filter documents to partially index a collection.

You can filter documents to partially index a collection. The following example creates a View on the sample_mflix.movies collection so that you can search for only movies released after January 1, 2000.

1

Connect to your cluster using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_mflix database.

use sample_mflix
3

Create a movies_ReleasedAfter2000 View.

db.createView(
  "movies_ReleasedAfter2000",
  "movies",
  [
    {
      $match: {
        $expr: {
          $gt: [
            "$released",
            ISODate("2000-01-01")
          ]
        }
      }
    }
  ]
)
4

In Atlas, go to the Clusters page for your project.

  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Search & Vector Search under the Database heading.

    • If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

    • If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Search.

    The Search & Vector Search page displays.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Data Explorer under the Database heading.

  4. Expand the database and select the collection.

  5. Click the Indexes tab for the collection.

  6. Click the Search and Vector Search link in the banner.

    The Search & Vector Search page displays.

6

Click Create Search Index.

7

Start your index configuration.

Make the following selections on the page and then click Next.

Search Type

Select the MongoDB Search index type.

Index Name and Data Source

Specify the following information:

  • Index Name: releasedAfter2000Index

  • Database and Collection:

    • sample_mflix
    • movies_ReleasedAfter2000

Configuration Method

For a guided experience, select Visual Editor.

To edit the raw index definition, select JSON Editor.

IMPORTANT: Your |fts| index is named ``autoembed_index` by default. If you are creating multiple indexes, we recommend that you maintain a consistent, descriptive naming convention across your indexes.

8

Click Create Search Index.

Atlas displays a Toast (brief, non-interactive notification) to let you know your index is building.

9

Check the status.

The newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

IMPORTANT: Larger collections take longer to index. You will receive an email notification when your index is finished building.

10

Go to the Search Tester.

Click the Query button to the right of the index to query.

11

View and edit the query syntax.

Click Edit Query to view a default query syntax sample in JSON format.

12

Query the releasedAfter2000Index partial index.

[
  {
    "$search": {
      "index": "releasedAfter2000Index",
      "text": {
        "path": "title",
        "query": "foo"
      },
      "sort": {
        "released": 1
      }
    }
  }
]
SCORE: 3.120296001434326    _id:  “573a13d2f29313caabd929f8”
    awards: Object
    cast: Array (4)
    countries: Array (1)
    directors: Array (1)
    fullplot: "Rising from the ashes of Nirvana, the Foo Fighters became a Grammy-win…"
    genres: Array (2)
    imdb: Object
    languages: Array (1)
    lastupdated: "2015-08-19 00:00:25.937000000"
    num_mflix_comments: 0
    plot: "Rising from the ashes of Nirvana, the Foo Fighters became a Grammy-win…"
    poster: "https://m.media-amazon.com/images/M/MV5BMzE4OTczMTgxM15BMl5BanBnXkFtZT…"
    released: 2011-04-05T00:00:00.000+00:00
    runtime: 150
    title: "Foo Fighters: Back and Forth"
    tomatoes: Object
    type: "movie"
    year: 2011
1

Connect to your MongoDB Deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_mflix database.

use sample_mflix
3

Create a movies_ReleasedAfter2000 View.

db.createView(
  "movies_ReleasedAfter2000",
  "movies",
  [
    {
      $match: {
        $expr: {
          $gt: [
            "$released",
            ISODate("2000-01-01")
          ]
        }
      }
    }
  ]
)
4

Create a MongoDB Search index on the View.

db.movies_ReleasedAfter2000.createSearchIndex(
  "releasedAfter2000Index",
  {
    "mappings": {
      "dynamic": true
    }
  }
)
5

Query the releasedAfter2000Index partial index.

db.movies_ReleasedAfter2000.aggregate([
  {
    $search: {
      index: "releasedAfter2000Index",
      text: {
        path: "title",
        query: "foo"
      },
      sort: {
        released: 1
      }
    }
  }
])
[
  {
    _id: ObjectId('573a13d2f29313caabd929f8'),
    plot: "Rising from the ashes of Nirvana, the Foo Fighters became a Grammy-winning sensation on their own. Sixteen years of the band's history comes to life in this documentary, from their demo ...",
    genres: [ 'Documentary', 'Music' ],
    runtime: 150,
    cast: [
      'Shawn Cloninger',
      'William Goldsmith',
      'Jessy Greene',
      'Dave Grohl'
    ],
    num_mflix_comments: 0,
    poster: 'https://m.media-amazon.com/images/M/MV5BMzE4OTczMTgxM15BMl5BanBnXkFtZTcwNTU1NjQxOA@@._V1_SY1000_SX677_AL_.jpg',
    title: 'Foo Fighters: Back and Forth',
    fullplot: `Rising from the ashes of Nirvana, the Foo Fighters became a Grammy-winning sensation on their own. Sixteen years of the band's history comes to life in this documentary, from their demo tapes through the creation of their 2011 album, "Wasting Light."`,
    languages: [ 'English' ],
    released: ISODate('2011-04-05T00:00:00.000Z'),
    directors: [ 'James Moll' ],
    awards: { wins: 1, nominations: 1, text: '1 win & 1 nomination.' },
    lastupdated: '2015-08-19 00:00:25.937000000',
    year: 2011,
    imdb: { rating: 8.4, votes: 3745, id: 1853563 },
    countries: [ 'USA' ],
    type: 'movie',
    tomatoes: {
      viewer: { rating: 4.4, numReviews: 857, meter: 96 },
      dvd: ISODate('2011-08-08T00:00:00.000Z'),
      website: 'http://us.foofightersfilm.com/',
      production: 'Cinedigm Digital Cinema',
      lastUpdated: ISODate('2015-09-12T18:42:01.000Z')
    }
  }
]

Add or Modify Fields

Add of modify fields using a View.

The following example lets you search the sample_airbnb.listingsAndReviews collection for accomodatations based on a new totalPrice field, which is the sum of the price and cleaningFee fields. Also, since MongoDB Search doesn't support Decimal128 types, we transform the values to Double.

1

Connect to the cluster using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a listingsAndReviews_totalPrice View.

db.createView(
  "listingsAndReviews_totalPrice",
  "listingsAndReviews",
  [
    {
      $addFields: {
        totalPrice: {
          $add: [
            {
              $ifNull: [{ $toDouble: "$price" }, 0]
            },
            {
              $ifNull: [{ $toDouble: "$cleaning_fee" }, 0]
            }
          ]
        }
      }
    }
  ]
)
4

In Atlas, go to the Clusters page for your project.

  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Search & Vector Search under the Database heading.

    • If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

    • If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Search.

    The Search & Vector Search page displays.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Data Explorer under the Database heading.

  4. Expand the database and select the collection.

  5. Click the Indexes tab for the collection.

  6. Click the Search and Vector Search link in the banner.

    The Search & Vector Search page displays.

6

Click Create Search Index to start your index configuration.

Make the following selections on the page and then click Next.

Search Type

Select the MongoDB Search index type.

Index Name and Data Source

Specify the following information:

  • Index Name: listingsSearchablePrice

  • Database and Collection:

    • sample_airbnb
    • listings_SearchablePrice

Configuration Method

Select JSON Editor to edit the raw index definition.

7

Edit the index definition.

Copy and paste the following index definition to replace the default index definition in the UI:

{
    "mappings": {
        "dynamic": true
    },
    "storedSource": {
        "include": [
        "totalPrice"
        ]
    }
}
8

Click Create Search Index.

Atlas displays a Toast (brief, non-interactive notification) to let you know your index is building.

9

Check the status.

The newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

IMPORTANT: Larger collections take longer to index. You will receive an email notification when your index is finished building.

10

Go to the Search Tester.

Click the Query button to the right of the index to query.

11

View and edit the query syntax.

Click Edit Query to view a default query syntax sample in JSON format.

12

Run a query on the totalPriceIndex index.

[
  {
    "$search": {
      "index": "totalPriceIndex",
      "range": {
        "path": "totalPrice",
        "lte": 300
      },
      "returnStoredSource": true
    }
  }
]
SCORE: 1    _id:  "10006546"
    totalPrice: 115
SCORE: 1    _id:  "1001265"
    totalPrice: 215
SCORE: 1    _id:  "10021707"
    totalPrice: 40
SCORE: 1    _id:  "1003530"
    totalPrice: 270
SCORE: 1    _id:  "10038496"
    totalPrice: 269
SCORE: 1    _id:  "10051164"
    totalPrice: 250
SCORE: 1    _id:  "10057447"
    totalPrice: 50
SCORE: 1    _id:  "10057826"
    totalPrice: 205
SCORE: 1    _id:  "10059244"
    totalPrice: 43
SCORE: 1    _id:  "10066928"
    totalPrice: 140
1

Connect to your MongoDB deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a listingsAndReviews_totalPrice View.

db.createView(
  "listingsAndReviews_totalPrice",
  "listingsAndReviews",
  [
    {
      $addFields: {
        totalPrice: {
          $add: [
            {
              $ifNull: [{ $toDouble: "$price" }, 0]
            },
            {
              $ifNull: [{ $toDouble: "$cleaning_fee" }, 0]
            }
          ]
        }
      }
    }
  ]
)
4

Create a MongoDB Search index on the View.

db.listingsAndReviews_totalPrice.createSearchIndex(
  "totalPriceIndex",
  {
    "mappings": {
      "dynamic": true
    },
    "storedSource": {
      "include": [
        "totalPrice"
      ]
    }
  }
)
5

Run a query on the totalPriceIndex index.

db.listingsAndReviews_totalPrice.aggregate([
  {
    $search: {
      index: "totalPriceIndex",
      range: {
        path: "totalPrice",
        lte: 300
      },
      returnStoredSource: true
    }
  }
])
[
  { _id: '10006546', totalPrice: 115 },
  { _id: '1001265', totalPrice: 215 },
  { _id: '10021707', totalPrice: 40 },
  { _id: '1003530', totalPrice: 270 },
  { _id: '10038496', totalPrice: 269 },
  { _id: '10051164', totalPrice: 250 },
  { _id: '10057447', totalPrice: 50 },
  { _id: '10057826', totalPrice: 205 },
  { _id: '10059244', totalPrice: 43 },
  { _id: '10066928', totalPrice: 140 },
  { _id: '10082422', totalPrice: 60 },
  { _id: '10083468', totalPrice: 40 },
  { _id: '10084023', totalPrice: 231 },
  { _id: '10091713', totalPrice: 231 },
  { _id: '10092679', totalPrice: 58 },
  { _id: '10096773', totalPrice: 205 },
  { _id: '10112159', totalPrice: 90 },
  { _id: '10117617', totalPrice: 55 },
  { _id: '10120414', totalPrice: 150 },
  { _id: '10133554', totalPrice: 121 }
]

Configure Regex Naming Pattern

Configure dynamic indexing for fields with naming patterns using regex.

To index fields matching a naming pattern, use a View to transform your data so that the fields to index are nested in a sub-document. This enables you to use dynamic mappings for the sub-document path so that you can automatically search all new fields with the suffix _type, without any changes to your index definition.

The following View named listings_SearchableTypes only matches field names that end with _type in the sample_airbnb.listingsAndReviews collection. Specifically, the $set stage adds a new field named searchable_types, which contains the filtered fields with the term _type in the field name. The $arrayToObject contains the filter input (entire document) and condition (regex match for _type).

1

Connect to your cluster using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a View named listings_SearchableTypes.

1db.createView(
2  "listings_SearchableTypes",
3  "listingsAndReviews",
4    [
5      {
6        "$set": {
7          "searchable_types": {
8            "$arrayToObject": {
9              "$filter": {
10                "input": { "$objectToArray": "$$ROOT" },
11                "cond": { 
12                  "$regexMatch": { 
13                    "input": "$$this.k", 
14                    "regex": /_type$/
15                  }
16                }
17              }
18            }
19          }
20        }
21      }
22    ]
23)
4

In Atlas, go to the Clusters page for your project.

  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Search & Vector Search under the Database heading.

    • If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

    • If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Search.

    The Search & Vector Search page displays.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Data Explorer under the Database heading.

  4. Expand the database and select the collection.

  5. Click the Indexes tab for the collection.

  6. Click the Search and Vector Search link in the banner.

    The Search & Vector Search page displays.

6

Click Create Search Index to start your index configuration.

Make the following selections on the page and then click Next.

Search Type

Select the MongoDB Search index type.

Index Name and Data Source

Specify the following information:

  • Index Name: listingsSearchablePrice

  • Database and Collection:

    • sample_airbnb
    • listings_SearchablePrice

Configuration Method

Select JSON Editor to edit the raw index definition.

7

Edit the index definition.

Copy and paste the following index definition to replace the default index definition in the UI:

{
  "analyzer": "lucene.standard",
  "searchAnalyzer": "lucene.standard",
  "mappings": {
    "dynamic": false,
    "fields": {
      "searchable_types": [
        {
          "dynamic": {
            "typeSet": "tokenTypeSet"
          },
          "type": "document"
        }
      ]
    }
  },
  "typeSets": [
    {
      "name": "tokenTypeSet",
      "types": [
        {
          "type": "token"
        }
      ]
    }
  ]
}
8

Click Create Search Index.

Atlas displays a Toast (brief, non-interactive notification) to let you know your index is building.

9

Check the status.

The newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

IMPORTANT: Larger collections take longer to index. You will receive an email notification when your index is finished building.

10

Go to the Search Tester.

Click the Query button to the right of the index to query.

11

View and edit the query syntax.

Click Edit Query to view a default query syntax sample in JSON format.

12

Query the View.

The following query searches the View named listings_SearchableTypes for house with a private room. To run this query, copy and paste the query, and click Search:

[
  {
    "$search": {
      "index": "listingsSearchableTypes",
      "compound": {
        "should": [
          {
            "text": {
              "path": "searchable_types.property_type",
              "query": "House"
            }
          },
          {
            "text": {
              "path": "searchable_types.room_type",
              "query": "Private room"
            }
          }
        ]
      }
    }
  }
]
SCORE: 2.0530142784118652   _id:  "10051164"
    name: "Catete's Colonial Big Hause Room B",
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    
SCORE: 2.0530142784118652   _id:  "1016739"
    name: 'Private Room (2)  in Guest House at Coogee Beach',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "10324377"
    name: 'Suíte em local tranquilo e seguro',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "1073607"
    name: 'Bright Inner West Terrace Value+',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "1077686"
    name: 'BALAT',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "10990260"
    name: 'The Executive  Posh Room',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "10992286"
    name: 'Holoholo Inn: Rain Forest (Priv-2)',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "11396243"
    name: 'Beautiful Ensuite Room -Easy access to city',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "1145567"
    name: 'ROOM + ENSUITE INNER CITY MOD HOUSE',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
SCORE: 2.0530142784118652   _id:  "11464859"
    name: 'The Sassy Lilac Megan Love Room',
    searchable_types: Object
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
1

Connect to your MongoDB Deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a listings_SearchableTypes View.

db.createView(
  "listings_SearchableTypes",
  "listingsAndReviews",
    [
      {
        "$set": {
          "searchable_types": {
            "$arrayToObject": {
              "$filter": {
                "input": { "$objectToArray": "$$ROOT" },
                "cond": { 
                  "$regexMatch": { 
                    "input": "$$this.k", 
                    "regex": /_type$/
                  }
                }
              }
            }
          }
        }
      }
    ]
)
4

Create a MongoDB Search index on the View.

The following index definition on the View named listingsSearchableTypes configures a typeSet to automatically index all fields in the searchable_types document as the string type. If you add new fields that match this pattern, MongoDB Search will automatically index those fields also.

db.listings_SearchableTypes.createSearchIndex(
  "listingsSearchableTypes",
  {
    "analyzer": "lucene.standard",
    "searchAnalyzer": "lucene.standard",
    "mappings": {
      "dynamic": false,
      "fields": {
        "searchable_types": [
          {
            "type": "document",
            "dynamic": {
                "typeSet": "tokenTypeSet"
            }
          }
        ]
      }
    },
    "typeSets": [
      {
        "name": "tokenTypeSet",
        "types": [
          {
            "type": "token"
          }
        ]
      }
    ]
  }
)
5

Run a query on the totalPriceIndex index.

The following query searches the View named listings_SearchableTypes for house with a private room.

db.listings_SearchableTypes.aggregate([
  {
    "$search": {
      "index": "listingsSearchableTypes",
      "compound": {
        "should": [
          {
            "equals": {
              "path": "searchable_types.property_type",
              "value": "House"
            }
          },
          {
            "equals": {
              "path": "searchable_types.room_type",
              "value": "Private room"
            }
          }
        ]
      }
    }
  },
  {
    "$limit": 10
  },
  {
    "$project": {
      "_id": 0,
      "searchable_types": 1,
      "name": 1 
    }
  }
])
[
  {
    name: "Catete's Colonial Big Hause Room B",
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'Private Room (2)  in Guest House at Coogee Beach',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'Suíte em local tranquilo e seguro',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'Bright Inner West Terrace Value+',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'BALAT',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'The Executive  Posh Room',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'Holoholo Inn: Rain Forest (Priv-2)',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'Beautiful Ensuite Room -Easy access to city',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'ROOM + ENSUITE INNER CITY MOD HOUSE',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  },
  {
    name: 'The Sassy Lilac Megan Love Room',
    searchable_types: {
      property_type: 'House',
      room_type: 'Private room',
      bed_type: 'Real Bed'
    }
  }
]

Transform Fields for Faceting

Transform unsupported field types to string to facet on these field types.

To facet on unsupported field types such as boolean, objectId, or UUID, we recommend transforming the values to string type.

The following example creates a View on the sample_airbnb.listingsAndReviews namespace so that you can facet on the boolean type host.host_is_superhost field and the objectID type _id field.

1

Connect to your cluster using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a View named listings_SearchableTypes.

1db.createView(
2  "listings_SearchableTypes",
3  "listingsAndReviews",
4    [
5      {
6        "$addFields": {
7          "idString": { "$toString": "$_id" },
8          "superHostString": { "$toString": "$host.host_is_superhost" }
9        }
10      }
11    ]
12)
4

In Atlas, go to the Clusters page for your project.

  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Search & Vector Search under the Database heading.

    • If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

    • If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Search.

    The Search & Vector Search page displays.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Data Explorer under the Database heading.

  4. Expand the database and select the collection.

  5. Click the Indexes tab for the collection.

  6. Click the Search and Vector Search link in the banner.

    The Search & Vector Search page displays.

6

Click Create Search Index to start your index configuration.

Make the following selections on the page and then click Next.

Search Type

Select the MongoDB Search index type.

Index Name and Data Source

Specify the following information:

  • Index Name: listingsSearchablePrice

  • Database and Collection:

    • sample_airbnb
    • listings_SearchablePrice

Configuration Method

Select JSON Editor to edit the raw index definition.

7

Edit the index definition.

Copy and paste the following index definition to replace the default index definition in the UI:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "idString": {
        "type": "token"
      },
      "superHostString": {
        "type": "token"
      }
    }
  }
}

The preceding index definition indexes the idString and superHostString fields as the token type to support faceting on these fields.

8

Click Create Search Index.

Atlas displays a Toast (brief, non-interactive notification) to let you know your index is building.

9

Check the status.

The newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

IMPORTANT: Larger collections take longer to index. You will receive an email notification when your index is finished building.

10

Go to the Search Tester.

Click the Query button to the right of the index to query.

11

View and edit the query syntax.

Click Edit Query to view a default query syntax sample in JSON format.

12

Query the listingsAndReviews Source Collection.

The following query searches the summary field for ocean view in the listings_SearchableTypes index. The query retrieves the count of the number of properties that belong to each distinct _id and host.host_is_superhost. To run this query, copy and paste the query, and click Search:

[{
  "$searchMeta": {
    "index": "listingsSearchableTypes",
    "facet":{
      "operator": {
        "text": {
          "path": "summary",
          "query": "ocean view"
        }
      },
      "facets": {
        "idFacet" : {
          "type" : "string",
          "path" : "idString",
          "numBuckets" : 10,
        },
        "hostFacet": {
          "type" : "string",
          "path" : "superHostString"
        }
      }
    }
  }
}]
count: Object
    lowerBound: 510
facet: Object
    idFacet: Object
        buckets: Array (10)
            0: Object
                _id: "10082422"
                count: 1
            1: Object
                _id: "10166986"
                count: 1
            2: Object
                _id: "10266175"
                count: 1
            3: Object
                _id: "10267144"
                count: 1
            4: Object
                _id: "10317142"
                count: 1
            5: Object
                _id: "10359729"
                count: 1
            6: Object
                _id: "10392282"
                count: 1
            7: Object
                _id: "10548991"
                count: 1
            8: Object
                _id: "1067052"
                count: 1
            9: Object
                _id: "1069531"
                count: 1
    hostFacet: Object
        buckets: Array (2)
            0: Object
                _id: "false"
                count: 370
            1: Object
                _id: "true"
                count: 140
1

Connect to your MongoDB Deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a listings_SearchableTypes View.

The following View includes the following two fields:

  • idString that contains the value of the _id field after converting it to string type.

  • superHostString that contains the value of the host.host_is_superhost field after converting it to string type.

db.createView(
  "listings_SearchableTypes",
  "listingsAndReviews",
    [
      {
        "$addFields": {
          "idString": { "$toString": "$_id" },
          "superHostString": { "$toString": "$host.host_is_superhost" }
        }
      }
    ]
)
4

Create a MongoDB Search index on the View.

The following index definition indexes the idString and superHostString fields as the token type to support faceting on these fields.

db.listings_SearchableTypes.createSearchIndex(
  "listingsSearchableTypes",
  {
    "mappings": {
      "dynamic": true,
      "fields": {
        "idString": {
          "type": "token"
        },
        "superHostString": {
          "type": "token"
        }
      }
    }
  }
)
5

Query the listingsSearchableTypes partial index.

The following query searches the listings_SearchableTypes index for ocean view in the summary field. The query retrieves the count of the number of properties that belong to each distinct _id and host.host_is_superhost.

db.listings_SearchableTypes.aggregate([{
  "$searchMeta": {
    "index": "listingsSearchableTypes",
    "facet":{
      "operator": {
        "text": {
          "path": "summary",
          "query": "ocean view"
        }
      },
      "facets": {
        "idFacet" : {
          "type" : "string",
          "path" : "idString",
          "numBuckets" : 10,
        },
        "hostFacet": {
          "type" : "string",
          "path" : "superHostString"
        }
      }
    }
  }
}])
[
  {
    count: { lowerBound: Long('510') },
    facet: {
      idFacet: {
        buckets: [
          { _id: '10082422', count: Long('1') },
          { _id: '10166986', count: Long('1') },
          { _id: '10266175', count: Long('1') },
          { _id: '10267144', count: Long('1') },
          { _id: '10317142', count: Long('1') },
          { _id: '10359729', count: Long('1') },
          { _id: '10392282', count: Long('1') },
          { _id: '10548991', count: Long('1') },
          { _id: '1067052', count: Long('1') },
          { _id: '1069531', count: Long('1') }
        ]
      },
      hostFacet: {
        buckets: [
          { _id: 'false', count: Long('370') },
          { _id: 'true', count: Long('140') }
        ]
      }
    }
  }
]

Convert Decimal128 to Double

Convert decimal128 type to double type.

To search on Decimal128 type fields, we recommend converting the values to double type.

The following example creates a View on the sample_airbnb.listingsAndReviews namespace to add a field named totalPrice, which contains the sum of the price field and the cleaning_fee field after converting the values of these fields to double.

1

Connect to your cluster using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a View named listings_SearchablePrice.

The following View includes a field named totalPrice that contains the sum of value of the price and cleaning-fee fields. Before adding the values of these fields, the following code converts the values of the price and cleaning-fee fields to double type.

1db.createView(
2  "listings_SearchablePrice",
3  "listingsAndReviews",
4  [
5    {
6      "$addFields": {
7        "totalPrice": {
8          "$add": [
9            {
10              "$ifNull": [{ "$toDouble": "$price" }, 0]
11            },
12            {
13              "$ifNull": [{ "$toDouble": "$cleaning_fee" }, 0]
14            }
15          ]
16        }
17      }
18    }
19  ]
20)
4

In Atlas, go to the Clusters page for your project.

  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Search & Vector Search under the Database heading.

    • If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

    • If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Search.

    The Search & Vector Search page displays.

  1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

  3. In the sidebar, click Data Explorer under the Database heading.

  4. Expand the database and select the collection.

  5. Click the Indexes tab for the collection.

  6. Click the Search and Vector Search link in the banner.

    The Search & Vector Search page displays.

6

Click Create Search Index to start your index configuration.

Make the following selections on the page and then click Next.

Search Type

Select the MongoDB Search index type.

Index Name and Data Source

Specify the following information:

  • Index Name: listingsSearchablePrice

  • Database and Collection:

    • sample_airbnb
    • listings_SearchablePrice

Configuration Method

Select JSON Editor to edit the raw index definition.

7

Click Create Search Index.

Atlas displays a Toast (brief, non-interactive notification) to let you know your index is building.

8

Check the status.

The newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

IMPORTANT: Larger collections take longer to index. You will receive an email notification when your index is finished building.

9

Go to the Search Tester.

Click the Query button to the right of the index to query.

10

View and edit the query syntax.

Click Edit Query to view a default query syntax sample in JSON format.

11

Query the Source Collection.

The following query searches the index named listingsSearchablePrice for properties that cost between 100 and 200, both inclusive. To run this query, copy and paste the query, and click Search:

[
  {
    "$search": {
      "index": "listingsSearchablePrice",
      "range": {
        "path": "totalPrice",
        "gte": 100,
        "lte": 200
      }
    }
  }
]
SCORE: 1    _id:  "10006546"
    access: "We are always available to help guests. The house is fully available t…"
    accommodates: 8
    address: Object
    price: 80.00
SCORE: 1    _id:  "10066928"
    access: "Le logement sera disponible en entier pour votre séjour."
    accommodates: 6
    address: Object 
    price: 140.00
SCORE: 1    _id:  "10120414"
    access: "You can access to the rooftop where you have a beautiful view of Empir…"
    accommodates: 3
    address: Object
    price: 150.00
SCORE: 1    _id:  "10133554"
    access: "We have bathroom,wc,İnternet in rooms and under rooms we have cafe bar…"
    accommodates: 3
    address: Object
    price: 121.00
SCORE: 1    _id:  "10186755"
    access: ""
    accommodates: 2
    address: Object
    price: 185.00
SCORE: 1    _id:  "10213499"
    access: "Guests have access to backyard saltwater pool"
    accommodates: 2
    address: Object
    price: 117.00
SCORE: 1    _id:  "1022200"
    access: ""
    accommodates: 6
    address: Object
    price: 135.00
SCORE: 1    _id:  "10228731"
    access: "O quarto é privativo, com acesso a todas as acomodações do apartamento…"
    accommodates: 1
    address: Object
    price: 149.00
SCORE: 1    _id:  "10359729"
    access: ""
    accommodates: 4
    address: Object
    price: 105.00
SCORE: 1    _id:  "1036027"
    access: ""
    accommodates: 8
    address: Object
    price: 100.00
1

Connect to your MongoDB Deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_airbnb database.

use sample_airbnb
3

Create a listings_SearchablePrice View.

The following View includes a field named totalPrice that contains the sum of value of the price and cleaning-fee fields. Before adding the values of these fields, the following code converts the values of the price and cleaning-fee fields to double type.

db.createView(
  "listings_SearchablePrice",
  "listingsAndReviews",
  [
    {
      "$addFields": {
        "totalPrice": {
          "$add": [
            {
              "$ifNull": [{ "$toDouble": "$price" }, 0]
            },
            {
              "$ifNull": [{ "$toDouble": "$cleaning_fee" }, 0]
            }
          ]
        }
      }
    }
  ]
)
4

Create a MongoDB Search index on the View.

Note

If your cluster is running MongoDB v8.0, use the Atlas UI or the Atlas Administration API to create the index.

db.listings_SearchablePrice.createSearchIndex(
  "listingsSearchablePrice",
  {
    "mappings": {
      "dynamic": true
    }
  }
)
5

Query the listingsSearchablePrice index.

The following query searches the index named listingsSearchablePrice for properties that cost between 100 and 200, both inclusive.

db.listingsAndReviews.aggregate([
  {
    "$search": {
      "index": "listingsSearchablePrice",
      "range": {
        "path": "totalPrice",
        "gte": 100,
        "lte": 200
      }
    }
  },
  {
    "$project": {
      "_id": 0,
      "totalPrice": 1,
      "price": 1,
      "cleaning_fee": 1
    }
  }
])
[
  { price: Decimal128('80.00'), cleaning_fee: Decimal128('35.00') },
  { price: Decimal128('140.00') },
  { price: Decimal128('150.00') },
  { price: Decimal128('121.00') },
  { price: Decimal128('185.00') },
  { price: Decimal128('117.00'), cleaning_fee: Decimal128('0.00') },
  { price: Decimal128('135.00') },
  { price: Decimal128('149.00'), cleaning_fee: Decimal128('30.00') },
  { price: Decimal128('105.00') },
  { price: Decimal128('100.00') },
  { price: Decimal128('138.00') },
  { price: Decimal128('100.00'), cleaning_fee: Decimal128('80.00') },
  { price: Decimal128('112.00'), cleaning_fee: Decimal128('0.00') },
  { price: Decimal128('80.00'), cleaning_fee: Decimal128('30.00') },
  { price: Decimal128('129.00'), cleaning_fee: Decimal128('60.00') },
  { price: Decimal128('112.00') },
  { price: Decimal128('100.00'), cleaning_fee: Decimal128('100.00') },
  { price: Decimal128('85.00'), cleaning_fee: Decimal128('15.00') },
  { price: Decimal128('85.00'), cleaning_fee: Decimal128('40.00') },
  { price: Decimal128('135.00'), cleaning_fee: Decimal128('50.00') }
]
Type "it" for more

Edit a View

The following example updates the movies_ReleasedAfter2000 MongoDB View for movies before 2000.

db.runCommand(
{
    collMod: "movies_ReleasedAfter2000",
    viewOn: "movies",
    "pipeline": [
      {
        $match: {
          $expr: {
            $lt: [
              "$released",
              ISODate("2000-01-01T00")
            ]
          }
        }
      }
    ]
  }
)

After you run this command, MongoDB Search automatically detects the change in the View definition and performs reindexing with no downtime.

Return the Pipelines for a View

The following example returns the pipelines on the movies_ReleasedAfter2000 View.

db.getCollectionInfos({ name: "movies_ReleasedAfter2000" })[0].options.pipeline
[
  {
    '$match': {
      '$expr': { '$gt': [ '$released', ISODate('2000-01-01T00:00:00.000Z') ] }
    }
  }
]

Performance Considerations

Highly complex view transformations can increase indexing and query time. This is because the mongod must read the view definition when it filters and transforms the oplog entries during indexing (initial sync and steady state replication), and when it applies those transformations to the returned documents at query-time.

Consider creating a materialized view to avoid extra replication load on Atlas. You can also query the source collection directly to avoid query latency from the view transformation.

Troubleshoot

Indexes Change to FAILED

Indexes change to the FAILED status in the following scenarios:

  • You create an index on a View that is incompatible with MongoDB Search.

  • You edit a View in a way that does not meet the MongoDB Search compatibility requirements.

  • You remove or change a View's source collection.

    For example, if one View is created on another View, and you change the parent View source to another collection.

    Note

    This limitation also applies if a View is a descendent of other Views. For example, you can't change or remove the source collection that all descendents originate from.

Indexes Change to STALE

Indexes change to the STALE status in the following scenarios:

Warning

If the aggregation pipeline defined in your View is incompatible with the documents in the collection, search replication fails. For example, if a $toDouble expression operates on a document field that contains an array, the replication fails. Ensure your View works with all documents in the collection without errors.

  • If the View definition causes an aggregation failure while an index is READY, the index becomes STALE. The index will return to READY after you resolve the document or change the view definition so that it doesn't fail anymore. When STALE, the index remains queryable. If the index falls off the oplog, an index rebuild is triggered.

  • If the View definition causes an aggregation pipeline failure while the index is BUILDING, the index build is stuck until you fix the document. The index will return to READY after you resolve the document or change the view definition so that it doesn't fail anymore.

You can view index statuses in the Atlas UI on the index status details page.

Error: $search is only valid as the first stage in a pipeline

This error appears when you query a view using a MongoDB version before 8.1.

  • If you use a MongoDB version before 8.0, we recommend you upgrade to 8.1+ to query the view directly. You can upgrade to 8.0 to query the source collection.

  • If you use MongoDB 8.0, you must query the view index against the source collection. For example, run .aggregate() on the collection instead of the view.

Index Process

When you create an MongoDB Search index on a View, the mongot process performs the same tasks as when you create an MongoDB Search index on a regular collection. The mongot process:

  1. Creates MongoDB Search indexes based on the rules in the index definition for the collection.

  2. Monitors change streams for the current state of the documents and indexes for the collections for which you defined the MongoDB Search indexes.

  3. Processes MongoDB Search queries and returns the document IDs and other search metadata for the matching documents to mongod, which then does a full document lookup and returns the results to the client.

When you create an MongoDB Search index on a View, the View definition is applied during Step 1 and 2, and the transformed documents are indexed based on the search index definition, then stored on disk.

Learn More

To learn more about Views, see Views.

To create a MongoDB Vector Search index on a View, see Use Views with MongoDB Vector Search.

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