What is the typical use of the $group stage in an aggregation pipeline?

The $group stage in an aggregation pipeline plays a crucial role in data analysis by facilitating calculations of aggregate values. This stage is designed to accumulate data that share a common field, allowing you to perform operations such as sums, averages, counts, minimums, and maximums. By grouping documents based on specified key fields, you can summarize large datasets into meaningful metrics.

When implementing $group, you typically define a key that determines how the documents will be grouped, and you specify the aggregation operations to perform on the grouped data. For instance, if you want to find the total sales per product type, you can group the documents by the product type and calculate the sum of sales for each group.

The other choices represent functions that $group does not directly perform. Reshaping document structures pertains more to stages like $project or $replaceRoot, limiting and filtering documents are usually the domain of the $match stage, and sorting is managed by the $sort stage. Each of these stages has its specific purpose and is designed for different types of operations within the aggregation framework, distinct from the soul of what $group performs.