While handy, these lack the flexibility and capability of the aggregation pipeline. Single Purpose Aggregation Methods: From the Collection class, the methods estimatedDocumentCount(), count() and distinct() aggregate from a single collection.Aggregation pipeline is now the preferred approach since it offers better performance and usability. Map-Reduce Function: This legacy approach was deprecated in MongoDB 5.0.Some stages can use indexes for better performance. The aggregation pipeline can operate on a sharded collection. Other stages do sorting, grouping, string concatenation, array processing, and so on. Among the basic stages are filters that obtain a subset of matching documents and transforms that reshape data into a suitable output form. Each stage does specific processing on the data. Aggregation Pipeline: We can think of data as moving along a multi-stage pipeline.There are three ways to do aggregation in MongoDB: What are the ways to do aggregation in MongoDB?.While being flexible and intuitive for developers, MongoDB's aggregation is not as performant as some other databases or tools. These are good reasons for using MongoDB's aggregations. Sometimes we don't want to copy data and process it elsewhere. We wish to perform some computations on the data or report on live data. Often just retrieving data is inadequate. Therefore it's really a Turing complete functional language. MongoDB's aggregation framework can do any kind of processing. Aggregation framework or pipeline is the manner in which MongoDB does data processing in the database layer. Source: Develop Paper 2019.ĭata can be processed in the application layer or in the database layer.
An example aggregation pipeline of two stages.
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