$match (aggregation)
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Definition
$match
Filters documents based on a specified query predicate. Matched documents are passed to the next pipeline stage.
Compatibility
You can use $match
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
{ $match: { <query predicate> } }
The syntax for the $match
query predicate is identical to the syntax
used in the query argument of a
find()
command.
Behavior
Pipeline Optimization
Place the
$match
as early in the aggregation pipeline as possible. Because$match
limits the total number of documents in the aggregation pipeline, earlier$match
operations minimize the amount of processing down the pipe.If you place a
$match
at the very beginning of a pipeline, the query can take advantage of indexes like any otherdb.collection.find()
ordb.collection.findOne()
.
Expressions in Query Predicates
To include expressions in a query
predicate, use the $expr
operator.
Restrictions
You cannot use
$where
in a$match
stage.You cannot use
$near
or$nearSphere
in a$match
stage. As an alternative, you can either:Use the
$geoWithin
query predicate operator with$center
or$centerSphere
in the$match
stage.
To use
$text
in a$match
stage, the$match
stage has to be the first stage of the pipeline.Views do not support
$text
.Note
$text
provides text query capabilities for self-managed (non-Atlas) deployments. For data hosted on MongoDB Atlas, MongoDB offers an improved full-text query solution, Atlas Search.
Filter Data on Atlas by Using Atlas Search
For data stored in MongoDB Atlas,
you can use the Atlas Search
compound operator filter
option to match or filter
documents when running $search
queries. Running
$match
after $search
is less performant
than running $search
with the compound
operator filter
option.
To learn more about the filter
option, see compound
in the Atlas documentation.
Examples
The examples use a collection named articles
with the following
documents:
{ "_id" : ObjectId("512bc95fe835e68f199c8686"), "author" : "dave", "score" : 80, "views" : 100 } { "_id" : ObjectId("512bc962e835e68f199c8687"), "author" : "dave", "score" : 85, "views" : 521 } { "_id" : ObjectId("55f5a192d4bede9ac365b257"), "author" : "ahn", "score" : 60, "views" : 1000 } { "_id" : ObjectId("55f5a192d4bede9ac365b258"), "author" : "li", "score" : 55, "views" : 5000 } { "_id" : ObjectId("55f5a1d3d4bede9ac365b259"), "author" : "annT", "score" : 60, "views" : 50 } { "_id" : ObjectId("55f5a1d3d4bede9ac365b25a"), "author" : "li", "score" : 94, "views" : 999 } { "_id" : ObjectId("55f5a1d3d4bede9ac365b25b"), "author" : "ty", "score" : 95, "views" : 1000 }
Equality Match
The following operation uses $match
to perform an equality
match:
db.articles.aggregate( [ { $match : { author : "dave" } } ] );
The $match
selects the documents where the author
field equals dave
, and the aggregation returns the following:
{ "_id" : ObjectId("512bc95fe835e68f199c8686"), "author" : "dave", "score" : 80, "views" : 100 } { "_id" : ObjectId("512bc962e835e68f199c8687"), "author" : "dave", "score" : 85, "views" : 521 }
Perform a Count
The following example selects documents to process using the
$match
pipeline operator and then pipes the results
to the $group
pipeline operator to compute a count of
the documents:
db.articles.aggregate( [ { $match: { $or: [ { score: { $gt: 70, $lt: 90 } }, { views: { $gte: 1000 } } ] } }, { $group: { _id: null, count: { $sum: 1 } } } ] );
In the aggregation pipeline, $match
selects the documents
where either the score
is greater than 70
and less than 90
or the views
is greater than or equal to 1000
. These documents
are then piped to the $group
to perform a count. The
aggregation returns the following:
{ "_id" : null, "count" : 5 }
Additional Information
Refer to the following pages for more information and use cases on aggregation.