Aggregations 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 and commands.
You can download this section in PDF form as MongoDB Aggregation and Data Processing.
- Aggregation Introduction
- A high-level introduction to aggregation.
- Aggregation Concepts
Introduces the use and operation of the data aggregation modalities available in MongoDB.
- Aggregation Pipeline
- The aggregation pipeline is a framework for performing aggregation tasks, modeled on the concept of data processing pipelines. Using this framework, MongoDB passes the documents of a single collection through a pipeline. The pipeline transforms the documents into aggregated results, and is accessed through the aggregate database command.
- Map-reduce is a generic multi-phase data aggregation modality for processing quantities of data. MongoDB provides map-reduce with the mapReduce database command.
- Single Purpose Aggregation Operations
- MongoDB provides a collection of specific data aggregation operations to support a number of common data aggregation functions. These operations include returning counts of documents, distinct values of a field, and simple grouping operations.
- Aggregation Mechanics
- Details internal optimization operations, limits, support for sharded collections, and concurrency concerns.
- Aggregation Examples
- Examples and tutorials for data aggregation operations in MongoDB.
- Aggregation Reference
- References for all aggregation operations material for all data aggregation methods in MongoDB.