MongoDB Aggregation Framework: Stages, Pipelines, and Examples

The article introduces MongoDB's aggregation concept, describing how aggregation processes documents through a series of stages in a pipeline, similar to a data flow where each Stage transforms the input and passes results to the next stage.

It lists the 23 built‑in aggregation stages, including $match for filtering, $group for grouping, $project for field selection, $lookup for left‑outer joins, $unwind for array deconstruction, $out for writing results to a collection, and others such as $addFields, $bucket, $sort, etc.

Practical examples demonstrate how to build pipelines using the Mongo shell. For instance, a simple $match pipeline filters documents by author:

db.artic.aggregate([
  { $match: { author: "dave" } }
])

The article also shows how to count documents with $match followed by $group and $sum to obtain the number of records meeting a condition.

Further sections cover the $project stage for selecting or excluding fields, the $lookup stage for performing left‑outer joins between collections, and the $unwind stage for flattening array fields. Each stage includes syntax, parameter explanations, and sample code snippets.

The $out stage is explained as the final stage that writes aggregation results to a specified collection, with examples of creating a new collection or merging into an existing one.

A dedicated portion explains MongoDB's Map‑Reduce framework, detailing the required map, reduce, and optional finalize functions, and shows how to output results using the out option.

Simple aggregation commands such as count, group, and distinct are also described, with their command syntax, supported options, and example usage.

Finally, the article summarizes the key points of using MongoDB's aggregation pipeline, encouraging readers to experiment with the provided examples to become proficient in building complex data processing workflows.