MongoDB Aggregation Pipeline: Data Analysis Methods for Beginners to Understand

MongoDB aggregation pipeline is a "pipeline" for data processing, enabling complex data analysis through multi-stage processing. At its core, it consists of multiple "stages," where each stage processes the output of the previous stage, sequentially performing operations such as filtering, projection, and grouping statistics. Key stages include: `$match` (filtering, similar to SQL WHERE), `$project` (projection, similar to SELECT), `$group` (group statistics, e.g., average score, total count, similar to GROUP BY), `$sort` (sorting), and `$limit` (limiting the number of results). In practice, multi-stage combinations can achieve complex analyses: for example, filtering math scores of class 1 and projecting names and scores (`$match + $project`), grouping by subject to calculate average scores (`$group + $sort`), or counting average scores and number of students by class and subject (composite grouping). Common operators also include `$sum` (summing) and `$avg` (averaging). Its advantage is the ability to efficiently complete analysis through pipeline combinations without manually exporting data. It is recommended to start with simple stages, gradually practice multi-stage nesting, and familiarize oneself with the role of each stage to master the aggregation pipeline.