Mongo Aggregation Pipeline

Aggregation 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. MongoDB provides three ways to perform aggregation: the aggregation pipeline, the map-reduce function, and single purpose aggregation methods.

Aggregation Pipeline¶

MongoDB’s aggregation framework is modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into an aggregated result. For example:

db.orders.aggregate([

   { $match: { status: "A" } },

   { $group: { _id: "$cust_id", total: { $sum: "$amount" } } }

])

First Stage: The $match stage filters the documents by the status field and passes to the next stage those documents that have status equal to "A".

Second Stage: The $group stage groups the documents by the cust_id field to calculate the sum of the amount for each unique cust_id.

The pipeline provides efficient data aggregation using native operations within MongoDB, and is the preferred method for data aggregation in MongoDB.

The aggregation pipeline can operate on a sharded collection.

The aggregation pipeline can use indexes to improve its performance during some of its stages. In addition, the aggregation pipeline has an internal optimization phase. See Pipeline Operators and Indexes and Aggregation Pipeline Optimization for details.

references:

https://docs.mongodb.com/manual/aggregation/#aggregation-pipeline