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$match (aggregation)

On this page

  • Definition
  • Compatibility
  • Syntax
  • Behavior
  • Pipeline Optimization
  • Restrictions
  • Filter Data on Atlas by Using Atlas Search
  • Examples
  • Equality Match
  • Perform a Count
  • Additional Information
$match

Filters the documents to pass only the documents that match the specified condition(s) to the next pipeline stage.

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

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

The $match stage has the following prototype form:

{ $match: { <query> } }

$match takes a document that specifies the query conditions. The query syntax is identical to the read operation query syntax; i.e. $match does not accept raw aggregation expressions. Instead, use a $expr query expression to include aggregation expression in $match.

  • The $match query syntax is identical to the read operation query syntax; i.e. $match does not accept raw aggregation expressions. To include aggregation expression in $match, use a $expr query expression:

    { $match: { $expr: { <aggregation expression> } } }
  • You cannot use $where in $match queries as part of the aggregation pipeline.

  • You cannot use $near or $nearSphere in $match queries as part of the aggregation pipeline. As an alternative, you can either:

  • To use $text in the $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.

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.

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 }

The following operation uses $match to perform a simple 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 }

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 }

Refer to the following pages for more information and use cases on aggregation.

← $lookup (aggregation)