Skip to main content

Aggregation

Aggregation in MongoDB is the process of transforming and combining data to derive computed results. It offers a variety of operations that can be applied to a collection of documents, yielding a single cumulative result. In this tutorial, we'll dig into the various aspects of MongoDB's aggregation framework, including its pipeline, stages, and operators.

Aggregation Pipeline​

The aggregation pipeline is a framework for data aggregation modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into an aggregated result. The most basic pipeline stages provide filters that operate like queries and document transformations that modify the form of the output document.

db.collection.aggregate([
   { <stage> },
   { <stage> },
   ...
])

The pipeline offers conditional expressions using field path identifiers (e.g., $fieldname) and system variables (e.g., $$CURRENT).

Pipeline Stages​

Each stage in the pipeline processes the documents as they pass along the pipeline. MongoDB provides a suite of stages, each with a unique purpose, such as filtering, projecting, sorting, etc. Here are a few commonly used stages:

  • $match: Filters the documents to pass only documents that match the specified condition to the next pipeline stage.
db.collection.aggregate([
   { $match: { <query> } }
])
  • $group: Groups documents by some specified expression and outputs a document for each distinct grouping.
db.collection.aggregate([
   { $group: { _id: <expression>, <field1>: { <accumulator1> : <expression1> }, ... } }
])
  • $sort: Reorders the document stream by a specified sort key.
db.collection.aggregate([
   { $sort: { <field1>: <sort order>, <field2>: <sort order> ... } }
])
  • $project: Passes along the documents with the requested fields to the next stage in the pipeline.
db.collection.aggregate([
   { $project: { <field1>: <value>, <field2>: <value> ... } }
])

Pipeline Operators​

Operators perform transformation operations on the documents in the pipeline. They can be categorized into expression operators and stage operators.

Expression operators operate on data items and return results. They include arithmetic, array, comparison, date, conditional, string, and type operators.

Stage operators affect the pipeline flow. They include $match, $group, $sort, $project, etc.

Conclusion​

Aggregation in MongoDB provides a powerful mechanism for data analysis and reporting. It's a flexible and efficient way to process and transform data directly within the database. As we've seen, the aggregation framework provides various stages and operators to perform complex transformations and computations. With practice, you'll become adept at using these tools to manipulate your data as needed.

Remember to experiment with different stages and operators to understand their effects. Happy coding!