Aggregation

An aggregation allows us to process data records and return computed results.

• group values from multiple documents and give the ability to perform operations on the grouped data, returning a single result

You can use aggregation operations to:

• Group values from multiple documents together.
• Perform operations on the grouped data to return a single result.
• Analyze data changes over time.

Can be done in 3 ways: more info

• Aggregation pipeline
• Map-reduce function
• single purpose aggregation methods

Aggregation Pipeline

Aggregation pipelines are the preferred way to perform aggregations in Mongo.

An aggregation pipeline consists of one or more stages that process documents

• Each stage performs an operation on the input documents.
  • ex. a stage can filter documents, group documents, and calculate values.
• The documents that are output from a stage are passed to the next stage.
• An aggregation pipeline can return results for groups of documents.
  • ex. return the total, average, maximum, and minimum values.

$facet

A facet processes multiple aggregation pipelines within a single stage (ie. the $facet stage) on the same set of input documents.

• input documents are passed to the $facet stage only once.
• $facet enables various aggregations on the same set of input documents, without needing to retrieve the input documents multiple times.

Similar to the group by function of a SQL database

ex. in Amazon when you make a search, the sidebar will show relevant things to group by, such as brand, price range etc. If their database was built in Mongo, it would be accomplished with facets.

The $facet stage has the following form:

{ $facet:
   {
      <outputField1>: [ <stage1>, <stage2>, ... ],
      <outputField2>: [ <stage1>, <stage2>, ... ],
      ...
   }
}

• docs

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Backlinks

• Change Streams