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db.collection.mapReduce()

db.collection.mapReduce(map, reduce, {<out>, <query>, <sort>, <limit>, <finalize>, <scope>, <jsMode>, <verbose>})

mongo Shell Method

This page documents the mongo shell method, and does not refer to the MongoDB Node.js driver (or any other driver) method. For corresponding MongoDB driver API, refer to your specific MongoDB driver documentation instead.

Note

Starting in version 4.2, MongoDB deprecates:

  • The map-reduce option to create a new sharded collection as well as 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.
  • The explicit specification of nonAtomic: false option.

The db.collection.mapReduce() method provides a wrapper around the mapReduce command.

Note

Views do not support map-reduce operations.

db.collection.mapReduce() has the following syntax:

db.collection.mapReduce(
                         <map>,
                         <reduce>,
                         {
                           out: <collection>,
                           query: <document>,
                           sort: <document>,
                           limit: <number>,
                           finalize: <function>,
                           scope: <document>,
                           jsMode: <boolean>,
                           verbose: <boolean>,
                           bypassDocumentValidation: <boolean>
                         }
                       )

db.collection.mapReduce() takes the following parameters:

Parameter Type Description
map function

A JavaScript function that associates or “maps” a value with a key and emits the key and value pair.

See Requirements for the map Function for more information.

reduce function

A JavaScript function that “reduces” to a single object all the values associated with a particular key.

See Requirements for the reduce Function for more information.

options document A document that specifies additional parameters to db.collection.mapReduce().

The following table describes additional arguments that db.collection.mapReduce() can accept.

Field Type Description
out string or document

Specifies the location of the result of the map-reduce operation. You can output to a collection, output to a collection with an action, or output inline. You may output to a collection when performing map-reduce operations on the primary members of the set; on secondary members you may only use the inline output.

See out Options for more information.

query document Specifies the selection criteria using query operators for determining the documents input to the map function.
sort document Sorts the input documents. This option is useful for optimization. For example, specify the sort key to be the same as the emit key so that there are fewer reduce operations. The sort key must be in an existing index for this collection.
limit number Specifies a maximum number of documents for the input into the map function.
finalize function

Optional. Follows the reduce method and modifies the output.

See Requirements for the finalize Function for more information.

scope document Specifies global variables that are accessible in the map, reduce and finalize functions.
jsMode boolean

Specifies whether to convert intermediate data into BSON format between the execution of the map and reduce functions.

Defaults to false.

If false:

  • Internally, MongoDB converts the JavaScript objects emitted by the map function to BSON objects. These BSON objects are then converted back to JavaScript objects when calling the reduce function.
  • The map-reduce operation places the intermediate BSON objects in temporary, on-disk storage. This allows the map-reduce operation to execute over arbitrarily large data sets.

If true:

  • Internally, the JavaScript objects emitted during map function remain as JavaScript objects. There is no need to convert the objects for the reduce function, which can result in faster execution.
  • You can only use jsMode for result sets with fewer than 500,000 distinct key arguments to the mapper’s emit() function.
verbose boolean

Specifies whether to include the timing information in the result information. Set verbose to true to include the timing information.

Defaults to false.

collation document

Optional.

Specifies the collation to use for the operation.

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

The collation option has the following syntax:

collation: {
   locale: <string>,
   caseLevel: <boolean>,
   caseFirst: <string>,
   strength: <int>,
   numericOrdering: <boolean>,
   alternate: <string>,
   maxVariable: <string>,
   backwards: <boolean>
}

When specifying collation, the locale field is mandatory; all other collation fields are optional. For descriptions of the fields, see Collation Document.

If the collation is unspecified but the collection has a default collation (see db.createCollection()), the operation uses the collation specified for the collection.

If no collation is specified for the collection or for the operations, MongoDB uses the simple binary comparison used in prior versions for string comparisons.

You cannot specify multiple collations for an operation. For example, you cannot specify different collations per field, or if performing a find with a sort, you cannot use one collation for the find and another for the sort.

New in version 3.4.

bypassDocumentValidation boolean

Optional. Enables mapReduce to bypass document validation during the operation. This lets you insert documents that do not meet the validation requirements.

New in version 3.2.

Note

map-reduce operations and $where operator expressions cannot access certain global functions or properties, such as db, that are available in the mongo shell.

The following JavaScript functions and properties are available to map-reduce operations and $where operator expressions:

Available Properties Available Functions  
args
MaxKey
MinKey
assert()
BinData()
DBPointer()
DBRef()
doassert()
emit()
gc()
HexData()
hex_md5()
isNumber()
isObject()
ISODate()
isString()
Map()
MD5()
NumberInt()
NumberLong()
ObjectId()
print()
printjson()
printjsononeline()
sleep()
Timestamp()
tojson()
tojsononeline()
tojsonObject()
UUID()
version()

Requirements for the map Function

The map function is responsible for transforming each input document into zero or more documents. It can access the variables defined in the scope parameter, and has the following prototype:

function() {
   ...
   emit(key, value);
}

The map function has the following requirements:

  • In the map function, reference the current document as this within the function.
  • The map function should not access the database for any reason.
  • The map function should be pure, or have no impact outside of the function (i.e. side effects.)
  • A single emit can only hold half of MongoDB’s maximum BSON document size.
  • The map function may optionally call emit(key,value) any number of times to create an output document associating key with value.
  • Starting in version 4.2.1, MongoDB deprecates the use of BSON type JavaScript code with scope (BSON type 15) for the map function. Using JavaScript code with scope with the map function will print a warning to the log. Instead, use BSON type String (BSON type 2) or BSON type JavaScript (BSON type 13). To pass constant values which will be accessible in the map function, use the scope parameter.

The following map function will call emit(key,value) either 0 or 1 times depending on the value of the input document’s status field:

function() {
    if (this.status == 'A')
        emit(this.cust_id, 1);
}

The following map function may call emit(key,value) multiple times depending on the number of elements in the input document’s items field:

function() {
    this.items.forEach(function(item){ emit(item.sku, 1); });
}

Requirements for the reduce Function

The reduce function has the following prototype:

function(key, values) {
   ...
   return result;
}

The reduce function exhibits the following behaviors:

  • The reduce function should not access the database, even to perform read operations.
  • The reduce function should not affect the outside system.
  • MongoDB will not call the reduce function for a key that has only a single value. The values argument is an array whose elements are the value objects that are “mapped” to the key.
  • MongoDB can invoke the reduce function more than once for the same key. In this case, the previous output from the reduce function for that key will become one of the input values to the next reduce function invocation for that key.
  • The reduce function can access the variables defined in the scope parameter.
  • The inputs to reduce must not be larger than half of MongoDB’s maximum BSON document size. This requirement may be violated when large documents are returned and then joined together in subsequent reduce steps.
  • Starting in version 4.2.1, MongoDB deprecates the use of BSON type JavaScript code with scope (BSON type 15) for the reduce function. Using JavaScript code with scope with the reduce function will print a warning to the log. Instead, use BSON type String (BSON type 2) or BSON type JavaScript (BSON type 13). To pass constant values which will be accessible in the reduce function, use the scope parameter.

Because it is possible to invoke the reduce function more than once for the same key, the following properties need to be true:

  • the type of the return object must be identical to the type of the value emitted by the map function.

  • the reduce function must be associative. The following statement must be true:

    reduce(key, [ C, reduce(key, [ A, B ]) ] ) == reduce( key, [ C, A, B ] )
    
  • the reduce function must be idempotent. Ensure that the following statement is true:

    reduce( key, [ reduce(key, valuesArray) ] ) == reduce( key, valuesArray )
    
  • the reduce function should be commutative: that is, the order of the elements in the valuesArray should not affect the output of the reduce function, so that the following statement is true:

    reduce( key, [ A, B ] ) == reduce( key, [ B, A ] )
    

out Options

You can specify the following options for the out parameter:

Output to a Collection

This option outputs to a new collection, and is not available on secondary members of replica sets.

out: <collectionName>

Output to a Collection with an Action

Note

Starting in version 4.2, MongoDB deprecates:

  • The map-reduce option to create a new sharded collection as well as 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.
  • The explicit specification of nonAtomic: false option.

This option is only available when passing a collection that already exists to out. It is not available on secondary members of replica sets.

out: { <action>: <collectionName>
        [, db: <dbName>]
        [, sharded: <boolean> ]
        [, nonAtomic: <boolean> ] }

When you output to a collection with an action, the out has the following parameters:

  • <action>: Specify one of the following actions:

    • replace

      Replace the contents of the <collectionName> if the collection with the <collectionName> exists.

    • merge

      Merge the new result with the existing result if the output collection already exists. If an existing document has the same key as the new result, overwrite that existing document.

    • reduce

      Merge the new result with the existing result if the output collection already exists. If an existing document has the same key as the new result, apply the reduce function to both the new and the existing documents and overwrite the existing document with the result.

  • db:

    Optional. The name of the database that you want the map-reduce operation to write its output. By default this will be the same database as the input collection.

  • sharded:

    Note

    Starting in version 4.2, the use of the sharded option is deprecated.

    Optional. If true and you have enabled sharding on output database, the map-reduce operation will shard the output collection using the _id field as the shard key.

    If true and collectionName is an existing unsharded collection, map-reduce fails.

  • nonAtomic:

    Note

    Starting in MongoDB 4.2, explicitly setting nonAtomic to false is deprecated.

    Optional. Specify output operation as non-atomic. This applies only to the merge and reduce output modes, which may take minutes to execute.

    By default nonAtomic is false, and the map-reduce operation locks the database during post-processing.

    If nonAtomic is true, the post-processing step prevents MongoDB from locking the database: during this time, other clients will be able to read intermediate states of the output collection.

Output Inline

Perform the map-reduce operation in memory and return the result. This option is the only available option for out on secondary members of replica sets.

out: { inline: 1 }

The result must fit within the maximum size of a BSON document.

Requirements for the finalize Function

The finalize function has the following prototype:

function(key, reducedValue) {
   ...
   return modifiedObject;
}

The finalize function receives as its arguments a key value and the reducedValue from the reduce function. Be aware that:

  • The finalize function should not access the database for any reason.
  • The finalize function should be pure, or have no impact outside of the function (i.e. side effects.)
  • The finalize function can access the variables defined in the scope parameter.
  • Starting in version 4.2.1, MongoDB deprecates the use of BSON type JavaScript code with scope (BSON type 15) for the finalize function. Using JavaScript code with scope with the finalize function will print a warning to the log. Instead, use BSON type String (BSON type 2) or BSON type JavaScript (BSON type 13). To pass constant values which will be accessible in the finalize function, use the scope parameter.

Map-Reduce Examples

Aggregation Pipeline as Alternative

Aggregation pipeline provides better performance and a more coherent interface than map-reduce.

Various map-reduce expressions can be rewritten using aggregation pipeline operators, such as $group, $merge, etc.

The example below includes aggregation pipeline alternatives.

Create a sample collection orders with these documents:

db.orders.insertMany([
   { _id: 1, cust_id: "Ant O. Knee", ord_date: new Date("2020-03-01"), price: 25, items: [ { sku: "oranges", qty: 5, price: 2.5 }, { sku: "apples", qty: 5, price: 2.5 } ], status: "A" },
   { _id: 2, cust_id: "Ant O. Knee", ord_date: new Date("2020-03-08"), price: 70, items: [ { sku: "oranges", qty: 8, price: 2.5 }, { sku: "chocolates", qty: 5, price: 10 } ], status: "A" },
   { _id: 3, cust_id: "Busby Bee", ord_date: new Date("2020-03-08"), price: 50, items: [ { sku: "oranges", qty: 10, price: 2.5 }, { sku: "pears", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 4, cust_id: "Busby Bee", ord_date: new Date("2020-03-18"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 5, cust_id: "Busby Bee", ord_date: new Date("2020-03-19"), price: 50, items: [ { sku: "chocolates", qty: 5, price: 10 } ], status: "A"},
   { _id: 6, cust_id: "Cam Elot", ord_date: new Date("2020-03-19"), price: 35, items: [ { sku: "carrots", qty: 10, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 7, cust_id: "Cam Elot", ord_date: new Date("2020-03-20"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 8, cust_id: "Don Quis", ord_date: new Date("2020-03-20"), price: 75, items: [ { sku: "chocolates", qty: 5, price: 10 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 9, cust_id: "Don Quis", ord_date: new Date("2020-03-20"), price: 55, items: [ { sku: "carrots", qty: 5, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 }, { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },
   { _id: 10, cust_id: "Don Quis", ord_date: new Date("2020-03-23"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" }
])

Return the Total Price Per Customer

Perform the map-reduce operation on the orders collection to group by the cust_id, and calculate the sum of the price for each cust_id:

  1. Define the map function to process each input document:

    • In the function, this refers to the document that the map-reduce operation is processing.
    • The function maps the price to the cust_id for each document and emits the cust_id and price.
    var mapFunction1 = function() {
       emit(this.cust_id, this.price);
    };
    
  2. Define the corresponding reduce function with two arguments keyCustId and valuesPrices:

    • The valuesPrices is an array whose elements are the price values emitted by the map function and grouped by keyCustId.
    • The function reduces the valuesPrice array to the sum of its elements.
    var reduceFunction1 = function(keyCustId, valuesPrices) {
       return Array.sum(valuesPrices);
    };
    
  3. Perform map-reduce on all documents in the orders collection using the mapFunction1 map function and the reduceFunction1 reduce function:

    db.orders.mapReduce(
       mapFunction1,
       reduceFunction1,
       { out: "map_reduce_example" }
    )
    

    This operation outputs the results to a collection named map_reduce_example. If the map_reduce_example collection already exists, the operation will replace the contents with the results of this map-reduce operation.

  4. Query the map_reduce_example collection to verify the results:

    db.map_reduce_example.find().sort( { _id: 1 } )
    

    The operation returns these documents:

    { "_id" : "Ant O. Knee", "value" : 95 }
    { "_id" : "Busby Bee", "value" : 125 }
    { "_id" : "Cam Elot", "value" : 60 }
    { "_id" : "Don Quis", "value" : 155 }
    

Aggregation Alternative

Using the available aggregation pipeline operators, you can rewrite the map-reduce operation without defining custom functions:

db.orders.aggregate([
   { $group: { _id: "$cust_id", value: { $sum: "$price" } } },
   { $out: "agg_alternative_1" }
])
  1. The $group stage groups by the cust_id and calculates the value field using $sum. The value field contains the total price for each cust_id.

    This stage outputs these documents to the next stage:

    { "_id" : "Don Quis", "value" : 155 }
    { "_id" : "Ant O. Knee", "value" : 95 }
    { "_id" : "Cam Elot", "value" : 60 }
    { "_id" : "Busby Bee", "value" : 125 }
    
  2. Then, the $out writes the output to the collection agg_alternative_1. Alternatively, you could use $merge instead of $out.

  3. Query the agg_alternative_1 collection to verify the results:

    db.agg_alternative_1.find().sort( { _id: 1 } )
    

    The operation returns these documents:

    { "_id" : "Ant O. Knee", "value" : 95 }
    { "_id" : "Busby Bee", "value" : 125 }
    { "_id" : "Cam Elot", "value" : 60 }
    { "_id" : "Don Quis", "value" : 155 }
    

Calculate Order and Total Quantity with Average Quantity Per Item

In the following example, you will see a map-reduce operation on the orders collection for all documents that have an ord_date value greater than or equal to 2020-03-01.

The operation in the example:

  1. Groups by the item.sku field, and calculates the number of orders and the total quantity ordered for each sku.
  2. Calculates the average quantity per order for each sku value and merges the results into the output collection.

When merging results, if an existing document has the same key as the new result, the operation overwrites the existing document. If there is no existing document with the same key, the operation inserts the document.

Example steps:

  1. Define the map function to process each input document:

    • In the function, this refers to the document that the map-reduce operation is processing.
    • For each item, the function associates the sku with a new object value that contains the count of 1 and the item qty for the order and emits the sku (stored in the key) and the value.
     var mapFunction2 = function() {
        for (var idx = 0; idx < this.items.length; idx++) {
           var key = this.items[idx].sku;
           var value = { count: 1, qty: this.items[idx].qty };
    
           emit(key, value);
        }
    };
    
  2. Define the corresponding reduce function with two arguments keySKU and countObjVals:

    • countObjVals is an array whose elements are the objects mapped to the grouped keySKU values passed by map function to the reducer function.
    • The function reduces the countObjVals array to a single object reducedValue that contains the count and the qty fields.
    • In reducedVal, the count field contains the sum of the count fields from the individual array elements, and the qty field contains the sum of the qty fields from the individual array elements.
    var reduceFunction2 = function(keySKU, countObjVals) {
       reducedVal = { count: 0, qty: 0 };
    
       for (var idx = 0; idx < countObjVals.length; idx++) {
           reducedVal.count += countObjVals[idx].count;
           reducedVal.qty += countObjVals[idx].qty;
       }
    
       return reducedVal;
    };
    
  3. Define a finalize function with two arguments key and reducedVal. The function modifies the reducedVal object to add a computed field named avg and returns the modified object:

    var finalizeFunction2 = function (key, reducedVal) {
      reducedVal.avg = reducedVal.qty/reducedVal.count;
      return reducedVal;
    };
    
  4. Perform the map-reduce operation on the orders collection using the mapFunction2, reduceFunction2, and finalizeFunction2 functions:

    db.orders.mapReduce(
       mapFunction2,
       reduceFunction2,
       {
         out: { merge: "map_reduce_example2" },
         query: { ord_date: { $gte: new Date("2020-03-01") } },
         finalize: finalizeFunction2
       }
     );
    

    This operation uses the query field to select only those documents with ord_date greater than or equal to new Date("2020-03-01"). Then it outputs the results to a collection map_reduce_example2.

    If the map_reduce_example2 collection already exists, the operation will merge the existing contents with the results of this map-reduce operation. That is, if an existing document has the same key as the new result, the operation overwrites the existing document. If there is no existing document with the same key, the operation inserts the document.

  5. Query the map_reduce_example2 collection to verify the results:

    db.map_reduce_example2.find().sort( { _id: 1 } )
    

    The operation returns these documents:

    { "_id" : "apples", "value" : { "count" : 4, "qty" : 35, "avg" : 8.75 } }
    { "_id" : "carrots", "value" : { "count" : 2, "qty" : 15, "avg" : 7.5 } }
    { "_id" : "chocolates", "value" : { "count" : 3, "qty" : 15, "avg" : 5 } }
    { "_id" : "oranges", "value" : { "count" : 7, "qty" : 63, "avg" : 9 } }
    { "_id" : "pears", "value" : { "count" : 1, "qty" : 10, "avg" : 10 } }
    

Aggregation Alternative

Using the available aggregation pipeline operators, you can rewrite the map-reduce operation without defining custom functions:

db.orders.aggregate( [
   { $match: { ord_date: { $gte: new Date("2020-03-01") } } },
   { $unwind: "$items" },
   { $group: { _id: "$items.sku", qty: { $sum: "$items.qty" }, orders_ids: { $addToSet: "$_id" } }  },
   { $project: { value: { count: { $size: "$orders_ids" }, qty: "$qty", avg: { $divide: [ "$qty", { $size: "$orders_ids" } ] } } } },
   { $merge: { into: "agg_alternative_3", on: "_id", whenMatched: "replace",  whenNotMatched: "insert" } }
] )
  1. The $match stage selects only those documents with ord_date greater than or equal to new Date("2020-03-01").

  2. The $unwinds stage breaks down the document by the items array field to output a document for each array element. For example:

    { "_id" : 1, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-01T00:00:00Z"), "price" : 25, "items" : { "sku" : "oranges", "qty" : 5, "price" : 2.5 }, "status" : "A" }
    { "_id" : 1, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-01T00:00:00Z"), "price" : 25, "items" : { "sku" : "apples", "qty" : 5, "price" : 2.5 }, "status" : "A" }
    { "_id" : 2, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 70, "items" : { "sku" : "oranges", "qty" : 8, "price" : 2.5 }, "status" : "A" }
    { "_id" : 2, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 70, "items" : { "sku" : "chocolates", "qty" : 5, "price" : 10 }, "status" : "A" }
    { "_id" : 3, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 50, "items" : { "sku" : "oranges", "qty" : 10, "price" : 2.5 }, "status" : "A" }
    { "_id" : 3, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 50, "items" : { "sku" : "pears", "qty" : 10, "price" : 2.5 }, "status" : "A" }
    { "_id" : 4, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-18T00:00:00Z"), "price" : 25, "items" : { "sku" : "oranges", "qty" : 10, "price" : 2.5 }, "status" : "A" }
    { "_id" : 5, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-19T00:00:00Z"), "price" : 50, "items" : { "sku" : "chocolates", "qty" : 5, "price" : 10 }, "status" : "A" }
    ...
    
  3. The $group stage groups by the items.sku, calculating for each sku:

    • The qty field. The qty field contains the total qty ordered per each items.sku using $sum.
    • The orders_ids array. The orders_ids field contains an array of distinct order _id’s for the items.sku using $addToSet.
    { "_id" : "chocolates", "qty" : 15, "orders_ids" : [ 2, 5, 8 ] }
    { "_id" : "oranges", "qty" : 63, "orders_ids" : [ 4, 7, 3, 2, 9, 1, 10 ] }
    { "_id" : "carrots", "qty" : 15, "orders_ids" : [ 6, 9 ] }
    { "_id" : "apples", "qty" : 35, "orders_ids" : [ 9, 8, 1, 6 ] }
    { "_id" : "pears", "qty" : 10, "orders_ids" : [ 3 ] }
    
  4. The $project stage reshapes the output document to mirror the map-reduce’s output to have two fields _id and value. The $project sets:

    • the value.count to the size of the orders_ids array using $size.
    • the value.qty to the qty field of input document.
    • the value.avg to the average number of qty per order using $divide and $size.
    { "_id" : "apples", "value" : { "count" : 4, "qty" : 35, "avg" : 8.75 } }
    { "_id" : "pears", "value" : { "count" : 1, "qty" : 10, "avg" : 10 } }
    { "_id" : "chocolates", "value" : { "count" : 3, "qty" : 15, "avg" : 5 } }
    { "_id" : "oranges", "value" : { "count" : 7, "qty" : 63, "avg" : 9 } }
    { "_id" : "carrots", "value" : { "count" : 2, "qty" : 15, "avg" : 7.5 } }
    
  5. Finally, the $merge writes the output to the collection agg_alternative_3. If an existing document has the same key _id as the new result, the operation overwrites the existing document. If there is no existing document with the same key, the operation inserts the document.

  6. Query the agg_alternative_3 collection to verify the results:

    db.agg_alternative_3.find().sort( { _id: 1 } )
    

    The operation returns these documents:

    { "_id" : "apples", "value" : { "count" : 4, "qty" : 35, "avg" : 8.75 } }
    { "_id" : "carrots", "value" : { "count" : 2, "qty" : 15, "avg" : 7.5 } }
    { "_id" : "chocolates", "value" : { "count" : 3, "qty" : 15, "avg" : 5 } }
    { "_id" : "oranges", "value" : { "count" : 7, "qty" : 63, "avg" : 9 } }
    { "_id" : "pears", "value" : { "count" : 1, "qty" : 10, "avg" : 10 } }
    

Output

The output of the db.collection.mapReduce() method is identical to that of the mapReduce command. See the Output section of the mapReduce command for information on the db.collection.mapReduce() output.

Restrictions

MongoDB drivers automatically set afterClusterTime for operations associated with causally consistent sessions. Starting in MongoDB 4.2, the db.collection.mapReduce() no longer support afterClusterTime. As such, db.collection.mapReduce() cannot be associatd with causally consistent sessions.