db.collection.mapReduce() (mongosh method)

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

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> ] }

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.

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.

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 }

db.orders.aggregate([
   { $group: { _id: "$cust_id", value: { $sum: "$price" } } },
   { $out: "agg_alternative_1" }
])

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 } }

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" } }
] )