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Map-Reduce

Note
Aggregation Pipeline as Alternative

An aggregation pipeline provides better performance and usability than a map-reduce operation.

Map-reduce operations can be rewritten using aggregation pipeline operators, such as $group, $merge, and others.

For map-reduce operations that require custom functionality, MongoDB provides the $accumulator and $function aggregation operators starting in version 4.4. Use these operators to define custom aggregation expressions in JavaScript.

For examples of aggregation pipeline alternatives to map-reduce operations, see Map-Reduce to Aggregation Pipeline and Map-Reduce Examples.

Map-reduce is a data processing paradigm for condensing large volumes of data into useful aggregated results. To perform map-reduce operations, MongoDB provides the mapReduce database command.

Consider the following map-reduce operation:

Diagram of the annotated map-reduce operation.

In this map-reduce operation, MongoDB applies the map phase to each input document (i.e. the documents in the collection that match the query condition). The map function emits key-value pairs. For those keys that have multiple values, MongoDB applies the reduce phase, which collects and condenses the aggregated data. MongoDB then stores the results in a collection. Optionally, the output of the reduce function may pass through a finalize function to further condense or process the results of the aggregation.

All map-reduce functions in MongoDB are JavaScript and run within the mongod process. Map-reduce operations take the documents of a single collection as the input and can perform any arbitrary sorting and limiting before beginning the map stage. mapReduce can return the results of a map-reduce operation as a document, or may write the results to collections.

In MongoDB, map-reduce operations use custom JavaScript functions to map, or associate, values to a key. If a key has multiple values mapped to it, the operation reduces the values for the key to a single object.

The use of custom JavaScript functions provide flexibility to map-reduce operations. For instance, when processing a document, the map function can create more than one key and value mapping or no mapping. Map-reduce operations can also use a custom JavaScript function to make final modifications to the results at the end of the map and reduce operation, such as perform additional calculations.

Note

Starting in MongoDB 4.4, mapReduce no longer supports the deprecated BSON type JavaScript code with scope (BSON type 15) for its functions. The map, reduce, and finalize functions must be either BSON type String (BSON type 2) or BSON type JavaScript (BSON type 13). To pass constant values which will be accessible in the map, reduce, and finalize functions, use the scope parameter.

The use of JavaScript code with scope for the mapReduce functions has been deprecated since version 4.2.1.

In MongoDB, the map-reduce operation can write results to a collection or return the results inline. If you write map-reduce output to a collection, you can perform subsequent map-reduce operations on the same input collection that merge replace, merge, or reduce new results with previous results. See mapReduce and Perform Incremental Map-Reduce for details and examples.

When returning the results of a map-reduce operation inline, the result documents must be within the BSON Document Size limit, which is currently 16 megabytes. For additional information on limits and restrictions on map-reduce operations, see the mapReduce reference page.

MongoDB supports map-reduce operations on sharded collections.

However, starting in version 4.2, MongoDB deprecates the map-reduce option to create a new sharded collection and the use of the sharded option for map-reduce. To output to a sharded collection, create the sharded collection first. MongoDB 4.2 also deprecates the replacement of an existing sharded collection.

See Map-Reduce and Sharded Collections.

Views do not support map-reduce operations.

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  • Map-Reduce JavaScript Functions
  • Map-Reduce Results
  • Sharded Collections
  • Views