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$exists (query predicate operator)

Definition

$exists

The $exists operator matches documents that contain or do not contain a specified field, including documents where the field value is null.

Note

MongoDB $exists does not correspond to SQL operator exists. For SQL exists, see $in.

For MongoDB Search exists, see exists (MongoDB Search Operator) in the Atlas documentation.

Compatibility

You can use $exists for deployments hosted in the following environments:

  • MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

Note

Expressions do not support the $exists operator. To check for the existence of a field in an expression, you can use the $type aggregation operator to check if a field has a type of missing.

For more information, see $type Existence Check.

Syntax

To specify an $exists expression, use the following prototype:

{ field: { $exists: <boolean> } }

When <boolean> is true, $exists matches the documents that contain the field, including documents where the field value is null. If <boolean> is false, the query returns only the documents that do not contain the field.

Query Data on Atlas by Using MongoDB Search

For data stored in MongoDB Atlas, you can use the MongoDB Search exists (MongoDB Search Operator) operator when running $search queries. Running $exists after $search is less performant than running $search with the exists (MongoDB Search Operator) operator.

To learn more about the MongoDB Search version of this operator, see the exists (MongoDB Search Operator) operator in the Atlas documentation.

Examples

The examples on this page use data from the sample_mflix sample dataset. For details on how to load this dataset into your self-managed MongoDB deployment, see Load the sample dataset. If you made any modifications to the sample databases, you may need to drop and recreate the databases to run the examples on this page.

Exists and Not Equal To

Consider the following example:

db.movies.find( { rated: { $exists: true, $nin: [ "R", "PG-13" ] } } ).limit(5)

This query selects 5 documents in the movies collection where the rated field exists and its value does not equal "R" or "PG-13".

Null Values

The movies collection in the sample_mflix database contains documents where some fields are present and others are missing. For example, the rated field exists in 11,455 documents and is absent from the remaining 9,894 documents.

$exists: true

The following query specifies the query predicate rated: { $exists: true }:

db.movies.find( { rated: { $exists: true } }, { _id: 0, title: 1, rated: 1 } ).limit( 3 )

The results consist of three documents that contain the field rated:

[
  { title: 'The Great Train Robbery', rated: 'TV-G' },
  { title: 'A Corner in Wheat', rated: 'G' },
  { title: 'Traffic in Souls', rated: 'TV-PG' }
]

$exists: false

The following query specifies the query predicate rated: { $exists: false }:

db.movies.find( { rated: { $exists: false } }, { _id: 0, title: 1, year: 1 } ).limit( 3 )

The results consist of three documents that do not contain the field rated:

[
  {
    title: 'Winsor McCay, the Famous Cartoonist of the N.Y. Herald and His Moving Comics',
    year: 1911
  },
  { title: 'Gertie the Dinosaur', year: 1914 },
  { title: 'In the Land of the Head Hunters', year: 1914 }
]

Use a Sparse Index to Improve $exists Performance

The following table compares $exists query performance using sparse and non-sparse indexes:

$exists Query
Using a Sparse Index
Using a Non-Sparse Index

{ $exists: true }

Most efficient. MongoDB can make an exact match and does not require a FETCH.

More efficient than queries without an index, but still requires a FETCH.

{ $exists: false }

Cannot use the index and requires a COLLSCAN.

Requires a FETCH.

Queries that use { $exists: true } on fields that use a non-sparse index or that use { $exists: true } on fields that are not indexed examine all documents in a collection. To improve performance, create a sparse index on the field as shown in the following scenario:

  1. The movies collection contains documents where the metacritic field is present in some documents and absent from others. Of the 21,349 documents, 6,964 have the metacritic field and 14,385 do not.

  2. Create a sparse index on the metacritic field:

    db.movies.createIndex(
       { metacritic: 1 },
       { name: "metacriticSparseIndex", sparse: true }
    )

  3. The following example counts the documents where the metacritic field has a value (including null) and uses the sparse index:

    db.movies.countDocuments( { metacritic: { $exists: true } } )

    The example returns 6964. The operation does not count the documents that are missing the metacritic field.

Tip

If you only need documents where the field has a non-null value, you:

  • Can use $ne: null instead of $exists: true.

  • Do not need a sparse index on the field.

For example, using the movies collection:

db.stockSales.countDocuments( { auditDate: { $ne: null } } )

The example returns 6964. Documents that are missing the metacritic value or have a null metacritic value are not counted.

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