mongodb-aggregation-pipeline

To build a MongoDB aggregation pipeline, you construct multi-stage sequences using operators like $match, $group, $project, and $lookup to reshape documents and join collections into structured analytics outputs.

The best way to optimize MongoDB aggregation queries is by filtering early with $match to leverage indexes, avoiding unnecessary $project stages, and using $facet to generate multiple parallel result views for reporting.

The $lookup operator in a MongoDB aggregation pipeline works by joining documents from another collection into the current pipeline, allowing you to combine related data from separate collections into a single analytics output.

Yes, you can reshape document arrays in MongoDB using the $unwind stage and compute aggregates like sum, avg, min, and max with the $group stage, while deriving new fields with expressions such as $concat and $cond.

A MongoDB aggregation pipeline performs slowly on large collections when early stages fail to use $match with indexed fields, causing unnecessary document processing before grouping, joining, or sorting operations occur.

You should use $facet in a MongoDB aggregation pipeline when you need to process multi-view results in a single stage, allowing parallel branches to generate different aggregations from the same input documents simultaneously.