$match (aggregation)

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 other db.collection.find() or db.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 $geoNear stage instead of the $match stage.

  • 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.

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 }