Use Views with Atlas Search

Requirements

You must use MongoDB 8.0 or higher.

• On MongoDB v8.0+:

  • Use the Atlas UI or the Atlas Administration API to create Atlas Search indexes on Views.

  • Run the Atlas Search queries against the source collection. Reference the Atlas Search index that was created on the View. These queries return the original documents as they appear in the source collection.

• On MongoDB v8.1+, you can additionally:

  • Use mongosh and Driver methods, db.collection.createSearchIndex(), db.collection.updateSearchIndex(), db.collection.dropSearchIndex(), and $listSearchIndexes to create and manage Atlas Search indexes on Views.

  • Run the Atlas Search queries against the View.

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

Limitations

• Atlas Search supports Views only for $expr in the following stages:

  • $addFields

  • $set

  • $match

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

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

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

Example: Filter Documents

To create a View, you must have the createCollection privilege.

You can partially index a collection to filter documents. 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.

use sample_mflix

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

use sample_mflix

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db.db.movies_ReleasedAfter2000.aggregate([
2
  {
3
    $search: {
4
      index: "releasedAfter2000Index",
5
      text: {
6
        path: "title",
7
        query: "foo"
8
      },
9
      sort: {
10
        released: 1
11
      }
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    }
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  }
14
])

[
  {
    _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')
    }
  }
]

Example: Add or Modify Fields

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 Atlas Search doesn't support Decimal128 types, we transform the values to Double.

use sample_airbnb

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

use sample_airbnb

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db.listingsAndReviews_totalPrice.aggregate([
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  {
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    $search: {
4
      index: "totalPriceIndex",
5
      range: {
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        path: "totalPrice",
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        lte: 300
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      },
9
      returnStoredSource: true
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    }
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  }
12
])

[
  { _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 }
]

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, Atlas Search automatically detects the change in the View definition and performs a reindexing with no downtime.

Return the Pipelines for a View

The following example returns the pipelines on the movies_ReleasedAfter2000 View.

1
db.getCollectionInfos({ name: "movies_ReleasedAfter2000" })[0].options.pipeline

[
  {
    '$match': {
      '$expr': { '$gt': [ '$released', ISODate('2000-01-01T00:00:00.000Z') ] }
    }
  }
]

Performance Considerations

For indexes created on views, Atlas Search applies the transformations or filters that you define in the view pipeline at query-time and at replication-time. While trivial transformations or filters in the view pipeline typically result in negligible performance impact, complex transformations or highly selective filters in the view pipeline might significantly slow down querying or replication. If you want to perform a heavy transformation on the documents or apply a highly selective filter, consider creating a materialized view with already transformed or filtered documents. You can evaluate view performance by comparing query latency executed against the view and against its source collection.

Troubleshoot

Index Process

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

1. Creates Atlas 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 Atlas Search indexes.

3. Processes Atlas 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 Atlas Search index on a View, the View definition is applied during Step 1 and 2, and the transformed documents are stored in the Atlas Search index on disk.

Learn More

To learn more about Views, see Views.

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