MongoDB aggregation is a powerful feature that allows you to perform data transformation and analysis on your collections. In this post, we’ll explore the fundamentals of aggregation operators and how they can be used to manipulate and process data.
$match operator is used to filter documents based on specified criteria. It works similar to the
find method but is a crucial part of the aggregation framework. Learn how to use
$match to narrow down your data set before applying further aggregation stages.
$group operator is essential for grouping documents by a specified key and performing aggregate functions on grouped data. Discover how to use
$group to summarize data and gain insights into patterns within your MongoDB collections.
$project for Shaping Output
$project operator allows you to reshape documents in your aggregation pipeline. Learn how to include or exclude specific fields, create computed fields, and generally tailor the output of your aggregation.
Sorting Results with
Ordering the results is often crucial, and the
$sort operator comes in handy for this purpose. Find out how to use
$sort to arrange documents in ascending or descending order based on one or more fields.
Unwind Arrays with
When dealing with arrays in your documents, the
$unwind operator becomes valuable. It deconstructs arrays, creating a new document for each element. Explore how to use
$unwind to simplify your data for further analysis.
Combining Stages with
$facet operator allows you to run multiple pipelines within a single aggregation stage. This is particularly useful when you need to execute different computations on the same set of data. Learn how to harness the power of
$facet for more complex aggregation scenarios.
Applying Conditional Logic with
Conditional operations are made possible with the
$cond operator. This allows you to perform if-else-like evaluations within your aggregation pipeline. Discover how to incorporate conditional logic to handle various cases during data processing.
$lookup for Join Operations
$lookup operator enables you to perform left outer joins between documents from two collections. Dive into examples of how to use
$lookup to combine data from different sources for a more comprehensive analysis.