Aggregation framework operations process data records and return computed results. Aggregation operations group values from multiple documents together, and can perform a variety of operations on the grouped data to return a single result.

The Aggregation Pipeline

The aggregation pipeline is a framework for data aggregation modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into aggregated results.

For a full explanation and a complete list of pipeline stages and operators, see the manual.

The following example uses the aggregation pipeline on the restaurants sample dataset to find a list of the total number of 5-star restaurants, grouped by restaurant category.

client =[ '' ], :database => 'test')
coll = client['restaurants']
aggregation = coll.aggregate([
       { '$match'=> { 'stars'=> 5 } },
       { '$unwind'=> '$categories'},
       { '$group'=> { '_id'=> '$categories', 'fiveStars'=> { '$sum'=> 1 } } }

aggregation.each do |doc|
  #=> Yields a BSON::Document.

Inside the aggregate method, the first pipeline stage filters out all documents except those with 5 in the stars field. The second stage unwinds the categories field, which is an array, and treats each item in the array as a separate document. The third stage groups the documents by category and adds up the number of matching 5-star results.

Aggregation pipeline stages have a maximum memory use limit. To handle large datasets, set the allowDiskUse option to true to enable writing data to temporary files.

  • You can call the allow_disk_use method the aggregation object to get a new object with the option set:
aggregation = coll.aggregate([ <aggregration pipeline expressions> ])
aggregation_with_disk_use = aggregation.allow_disk_use(true)
  • Or you can pass an option to the aggregate method:
aggregation = coll.aggregate([ <aggregration pipeline expressions> ],
                              :allow_disk_use => true)

Single Purpose Aggregation Operations

MongoDB provides helper methods for some aggregation functions, including count and distinct.


The following example demonstrates how to use the count method to find the total number of documents which have the exact array [ 'Chinese', 'Seafood' ] in the categories field.

client =[ '' ], :database => 'test')
coll = client['restaurants']
aggregation = coll.count({ 'categories': [ 'Chinese', 'Seafood' ] })

count = coll.count({ 'categories' => [ 'Chinese', 'Seafood' ] })


The distinct helper method eliminates results which contain values and returns one record for each unique value.

The following example returns a list of unique values for the categories field in the restaurants collection:

client =[ '' ], :database => 'test')
coll = client['restaurants']
aggregation = coll.distinct('categories')

aggregation.each do |doc|
  #=> Yields a BSON::Document.