MongoDB Aggregation

 MongoDB Aggregation

 MongoDB Aggregation is a powerful framework that allows you to perform complex data processing tasks on your MongoDB collections. It enables you to perform operations such as filtering, grouping, transforming, and analyzing data within the database.

Aggregation in MongoDB works with a pipeline concept. A pipeline is an ordered sequence of stages, where each stage performs a specific data processing operation. The output of one stage serves as the input to the next stage. MongoDB provides various stages that can be used in the aggregation pipeline to shape and manipulate data.

Here are some of the common stages used in MongoDB Aggregation:

$match: This stage filters the documents in the collection based on specific criteria, similar to the find method. It uses the MongoDB query syntax to define the matching conditions.

$group: This stage groups documents together based on a specified key and allows you to perform aggregate functions on grouped data. You can calculate sums, averages, counts, and more on grouped data.

$project: This stage reshapes the documents in the collection by including or excluding fields. It allows you to specify the fields to include, rename fields, create computed fields, and more.

$sort: This stage sorts the documents based on one or more fields in ascending or descending order.

$limit: This stage limits the number of documents passed to the next stage in the pipeline.

$skip: This stage skips a specified number of documents and passes the remaining documents to the next stage in the pipeline.

$unwind: This stage deconstructs an array field from the input documents and outputs a document for each element in the array. It is useful when you want to perform operations on array elements individually.

$lookup: This stage performs a left outer join between two collections based on a common field. It can retrieve related documents from another collection and include them in the aggregation pipeline.