Aggregation Commands Comparison¶

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Aggregation Commands Comparison Table

Tip

Starting in version 4.4, MongoDB adds the $accumulator and $function aggregation operators. Using $accumulator and $function , mapReduce expressions can be re-written using the aggregation operators.

Even before version 4.4, some map-reduce expressions could also be rewritten using other aggregation pipeline operators, such as $group, $merge, etc.

Aggregation Commands Comparison Table¶

The following table provides a brief overview of the features of the MongoDB aggregation commands.

	`aggregate` / `db.collection.aggregate()`	`mapReduce` / `db.collection.mapReduce()`
Description	Designed with specific goals of improving performance and usability for aggregation tasks. Uses a “pipeline” approach where objects are transformed as they pass through a series of pipeline operators such as `$group`, `$match`, and `$sort`. See Aggregation Pipeline Operators for more information on the pipeline operators.	Implements the Map-Reduce aggregation for processing large data sets.
Key Features	Pipeline operators can be repeated as needed. Pipeline operators need not produce one output document for every input document. Can also generate new documents or filter out documents. With the addition of `$merge` in version 4.2, can create on-demand materialized views, where the content of the output collection can be updated incrementally the pipeline is run. `$merge` can incorporate results (insert new documents, merge documents, replace documents, keep existing documents, fail the operation, process documents with a custom update pipeline) into an existing collection.	In addition to grouping operations, can perform complex aggregation tasks as well as perform incremental aggregation on continuously growing datasets. See Map-Reduce Examples and Perform Incremental Map-Reduce.
Flexibility	Starting in version 4.4, can define custom aggregation expressions with `$accumulator` and `$function`. In previous versions, can only use operators and expressions supported by the aggregation pipeline. However, can add computed fields, create new virtual sub-objects, and extract sub-fields into the top-level of results by using the `$project` pipeline operator. See `$project` for more information as well as Aggregation Pipeline Operators for more information on all the available pipeline operators.	Custom `map`, `reduce` and `finalize` JavaScript functions offer flexibility to aggregation logic. See `mapReduce` for details and restrictions on the functions.
Output Results	Returns results as a cursor. If the pipeline includes the `$out` stage or `$merge` stage, the cursor is empty. With `$out`, you can replace an existing output collection completely or output to a new collection. See `$out` for details. With `$merge`, you can output to a new or existing collection. For existing cllections, you can specify how to incorporate the results into the output collection (insert new documents, merge documents, replace documents, keep existing documents, fail the operation, process documents with a custom update pipeline). See `$merge` for details.	Returns results in various options (inline, new collection, merge, replace, reduce). See `mapReduce` for details on the output options.
Sharding	Supports non-sharded and sharded input collections. `$merge` can output to a non-sharded or sharded collection.	Supports non-sharded and sharded input collections.
More Information	Aggregation Pipeline `db.collection.aggregate()` `aggregate`	Map-Reduce `db.collection.mapReduce()` `mapReduce`.