The reason why these are not simply called the match stage and the group stage is because in Javascript the ‘$’ sign signifies an operation.

The first two stages are known as the ‘$match’ and ‘$group’ stages. This is a multi-step process which filters, groups, sorts, and transforms data. The aggregation pipeline defines a sequence of data processing stages that transform and manipulate documents in a collection. Aggregation relies on a function known as the aggregation pipeline.

The aggregation framework, in the case of MongoDB, is a set of processes which transform data into a structure which can be analyzed. MongoDB Query Language Aggregation Framework MongoDB specifically includes many functions for their unique indexing and geospatial analysis in MQL, greatly simplifying the querying process for these tools. MQL is the basis for aggregation pipelines which restructures data through a multi-step process detailed here. MQL provides powerful aggregation capabilities to process and analyze data in MongoDB. Multiple bitwise operators such as $and, $or, and $not can also be used to create complex queries. It allows users to specify conditions using operators such as $eq (equals), $ne (not equals), $gt (greater than), $lt (less than), and many others.

The syntax of MQL is designed to be simple and intuitive. MongoDB’s decision to develop its own query language is a result of their commitment to tailoring querying functionality to their unique requirements and the diverse needs of their customers. It empowers users to fetch documents that align with precise criteria, execute aggregations, make updates to documents, and perform document deletions. MQL, short for MongoDB Query Language, is MongoDB’s proprietary language designed for data retrieval.

Mongodb json query