So, by the end of the query pipeline, we will have achieved all that we wanted. This way, we can break down a complex query into easier stages, in each of which we complete a different operation on the data. The pipeline then performs successive transformations on the data until our goal is achieved. The input of the pipeline can be a single collection, where others can be merged later down the pipeline. $sort stage – sorts the resulting documents the way we require (ascending or descending).$group stage – does the aggregation job.$match stage – filters those documents we need to work with, those that fit our needs.Here is a diagram to illustrate a typical MongoDB aggregation pipeline. ![]() MongoDB have deprecated How does the MongoDB aggregation pipeline work? The map-reduce framework on MongoDB is a predecessor of the aggregation framework and much more complex to use. ![]() There are what are called single purpose methods like estimatedDocumentCount(), count(), and distinct() which are appended to a find() query making them quick to use but limited in scope. While there are other methods of obtaining aggregate data in MongoDB, the aggregation framework is the recommended approach for most work. MongoDB Aggregation goes further though and can also perform relational-like joins, reshape documents, create new and update existing collections, and so on. This is similar to the basic aggregation available in SQL with the GROUP BY clause and COUNT, SUM and AVG functions. ![]() One of the most common use cases of Aggregation is to calculate aggregate values for groups of documents. The stages in a pipeline can filter, sort, group, reshape and modify documents that pass through the pipeline. The stages make up what is known as a pipeline. Aggregation is a way of processing a large number of documents in a collection by means of passing them through different stages.
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