Advanced MongoDB Aggregation Pipeline: Using $lookup for Multi-Collection Joins
In MongoDB aggregation pipelines, `$lookup` is used for multi-collection association queries, similar to JOIN operations in relational databases. It requires specifying the target collection (`from`), the matching field in the current collection (`localField`), the matching field in the target collection (`foreignField`), and the field to store the result (`as`). The matching results are stored in the `as` field as an array. For example, to associate the `users` collection with the `orders` collection, using `localField: "_id"` and `foreignField: "userId"` for matching allows retrieving each user's order list. Advanced usage can combine stages like `$match` (for initial filtering) and `$unwind` (for array expansion). For instance, to count the number of orders for users over 25 years old. When using `$lookup`, note the following: ensure consistent field types, create an index on the `foreignField` of the target collection (to avoid full table scans), and un-matched data will return an empty array. `$lookup` is a core tool for multi-collection associations. Mastering its parameters and basic usage, combined with other aggregation stages, enables efficient handling of complex association scenarios.
Read MoreMongoDB 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.
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