$exists
On this page
Definition
$exists
The
$exists
operator matches documents that contain or do not contain a specified field, including documents where the field value isnull
.
Compatibility
You can use $exists
for deployments hosted in the following
environments:
MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud
MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB
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. [1]
[1] | Starting in MongoDB 4.2, users can no longer use the query filter
$type: 0 as a synonym for
$exists:false . To query for null or missing fields, see
Query for Null or Missing Fields. |
Query Data on Atlas by Using Atlas Search
For data stored in MongoDB Atlas, you can use the
Atlas Search exists
operator when running $search
queries. Running
$exists
after $search
is less performant
than running $search
with the exists
operator.
To learn more about the Atlas Search version of this operator, see the exists operator in the Atlas documentation.
Examples
Exists and Not Equal To
Consider the following example:
db.inventory.find( { qty: { $exists: true, $nin: [ 5, 15 ] } } )
This query will select all documents in the inventory
collection
where the qty
field exists and its value does not equal 5
or
15
.
Null Values
The following examples uses a collection named spices
with the
following documents:
db.spices.insertMany( [ { saffron: 5, cinnamon: 5, mustard: null }, { saffron: 3, cinnamon: null, mustard: 8 }, { saffron: null, cinnamon: 3, mustard: 9 }, { saffron: 1, cinnamon: 2, mustard: 3 }, { saffron: 2, mustard: 5 }, { saffron: 3, cinnamon: 2 }, { saffron: 4 }, { cinnamon: 2, mustard: 4 }, { cinnamon: 2 }, { mustard: 6 } ] )
$exists: true
The following query specifies the query predicate saffron: { $exists: true }
:
db.spices.find( { saffron: { $exists: true } } )
The results consist of those documents that contain the field saffron
,
including the document whose field saffron
contains a null value:
{ saffron: 5, cinnamon: 5, mustard: null } { saffron: 3, cinnamon: null, mustard: 8 } { saffron: null, cinnamon: 3, mustard: 9 } { saffron: 1, cinnamon: 2, mustard: 3 } { saffron: 2, mustard: 5 } { saffron: 3, cinnamon: 2 } { saffron: 4 }
$exists: false
The following query specifies the query predicate cinnamon: { $exists: false }
:
db.spices.find( { cinnamon: { $exists: false } } )
The results consist of those documents that do not contain the field
cinnamon
:
{ saffron: 2, mustard: 5 } { saffron: 4 } { mustard: 6 }
Starting in MongoDB 4.2, users can no longer use the query filter
$type: 0
as a synonym for
$exists:false
. To query for null or missing fields, see
Query for Null or Missing Fields.
Use a Sparse Index to Improve $exists
Performance
The following scenario is not optimal because all of the collection's documents are examined:
You use a query to retrieve or count documents, and
use
field: { $exists: true }
, andthe
field
has a non-sparse index or does not have an index.
To improve performance, create a sparse index
on the field
as shown in the following scenario:
Create a
stockSales
collection:db.stockSales.insertMany( [ { _id: 0, symbol: "MDB", auditDate: new Date( "2021-05-18T16:12:23Z" ) }, { _id: 1, symbol: "MDB", auditDate: new Date( "2021-04-21T11:34:45Z" ) }, { _id: 2, symbol: "MSFT", auditDate: new Date( "2021-02-24T15:11:32Z" ) }, { _id: 3, symbol: "MSFT", auditDate: null }, { _id: 4, symbol: "MSFT", auditDate: new Date( "2021-07-13T18:32:54Z" ) }, { _id: 5, symbol: "AAPL" } ] ) The document with an
_id
of:3
has a nullauditDate
value.5
is missing theauditDate
value.
Create a sparse index on the
auditDate
field:db.getCollection( "stockSales" ).createIndex( { auditDate: 1 }, { name: "auditDateSparseIndex", sparse: true } ) The following example counts the documents where the
auditDate
field has a value (including null) and uses the sparse index:db.stockSales.countDocuments( { auditDate: { $exists: true } } ) The example returns 5. The document that is missing the
auditDate
value is not counted.
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 stockSales
collection:
db.stockSales.countDocuments( { auditDate: { $ne: null } } )
The example returns 4. Documents that are missing the auditDate
value or have a null auditDate
value are not counted.