Mongodb Aggregation Pipeline
- Mongodb Aggregation Pipeline consist of stages and each stage transforms the document. It is a multi-stage pipeline and in each state and the documents are taken as input to produce the resultant set of documents.
- In the next stage (ID available) the resultant documents are taken as input to produce output, this process continues till the last stage.
- The basic pipeline stages are defined below:
- filters that will operate like queries.
- the document transformation that modifies the resultant document.
- provide pipeline provides tools for grouping and sorting documents.
- Aggregation pipeline can also be used in sharded collection.
Let us discuss the aggregation pipeline with the help of an example:
Explanation:
In the above example of a collection of “train fares”. $match stage filters the documents by the value in class field i.e. class: “first-class” in the first stage and passes the document to the second stage.
In the Second Stage, the $group stage groups the documents by the id field to calculate the sum of fare for each unique id.
Here, the aggregate() function is used to perform aggregation. It can have three operators stages , expression and accumulator. These operators work together to achieve final desired outcome.
Aggregation in MongoDB
Aggregation in MongoDB is a powerful feature that allows for complex data transformations and computations on collections of documents. It enables users to group, filter, and manipulate data to produce summarized results.
It is typically performed using the MongoDB Aggregation Pipeline which is a framework for data aggregation modeled on the concept of data processing pipelines. In this article, We will learn about Aggregation in MongoDB in detail by covering various aspects related to MongoDB Aggregation.