Navigation
This is an upcoming (i.e. in progress) version of the manual.

$map (aggregation)

On this page

Definition

$map

Applies an expression to each item in an array and returns an array with the applied results.

The $map expression has the following syntax:

{ $map: { input: <expression>, as: <string>, in: <expression> } }
Field Specification
input An expression that resolves to an array.
as Optional. A name for the variable that represents each individual element of the input array. If no name is specified, the variable name defaults to this.
in An expression that is applied to each element of the input array. The expression references each element individually with the variable name specified in as.

For more information on expressions, see Expressions.

Examples

Add to each element of an array using $map

From the mongo shell, create a sample collection named grades with the following documents:

db.grades.insertMany([
  { _id: 1, quizzes: [ 5, 6, 7 ] },
  { _id: 2, quizzes: [ ] },
  { _id: 3, quizzes: [ 3, 8, 9 ] }
])

The following aggregation operation uses $map with the $add expression to increment each element in the quizzes array by 2.

db.grades.aggregate(
   [
      { $project:
         { adjustedGrades:
            {
              $map:
                 {
                   input: "$quizzes",
                   as: "grade",
                   in: { $add: [ "$$grade", 2 ] }
                 }
            }
         }
      }
   ]
)

This operation returns the following results:

{ "_id" : 1, "adjustedGrades" : [ 7, 8, 9 ] }
{ "_id" : 2, "adjustedGrades" : [ ] }
{ "_id" : 3, "adjustedGrades" : [ 5, 10, 11 ] }

Truncate each array element with $map

From the mongo shell, create a sample collection named deliveries with the following documents:

db.deliveries.insertMany([
  { "_id" : 1, "city" : "Bakersfield", "distances" : [ 34.57, 81.96, 44.24 ] },
  { "_id" : 2, "city" : "Barstow", "distances" : [ 73.28, 9.67, 124.36 ] },
  { "_id" : 3, "city" : "San Bernadino", "distances" : [ 16.04, 3.25, 6.82 ] }
])

The following aggregation operation uses $map to truncate each element in the distances array to its integer.

db.deliveries.aggregate(
   [
      { $project:
         {  city: "$city",
            integerValues:
               { $map:
                  {
                     input: "$distances",
                     as: "decimalValue",
                     in: { $trunc: "$$decimalValue" }
                  }
            }
         }
      }
   ]
)

This operation returns the following results:

{ "_id" : 1, "city" : "Bakersfield", "integerValues" : [ 34, 81, 44 ] }
{ "_id" : 2, "city" : "Barstow", "integerValues" : [ 73, 9, 124 ] }
{ "_id" : 3, "city" : "San Bernadino", "integerValues" : [ 16, 3, 6 ] }

Convert Celsius Temperatures to Fahrenheit Using $map

From the mongo shell, create a sample collection named temperatures with the following documents:

db.temperatures.insertMany([
  { "_id" : 1, "date" : ISODate("2019-06-23"), "tempsC" : [ 4, 12, 17 ] },
  { "_id" : 2, "date" : ISODate("2019-07-07"), "tempsC" : [ 14, 24, 11 ] },
  { "_id" : 3, "date" : ISODate("2019-10-30"), "tempsC" : [ 18, 6, 8 ] }
])

The following aggregation operation uses the $addFields stage to add a new field to the documents called tempsF which contains Fahrenheit equivalents of the elements in the tempsC array. To convert from Celsius to Fahrenheit, the operation uses $map to $multiply the Celsius values by 9/5 and then $add 32.

 db.temperatures.aggregate( [
   { $addFields:
      {
         "tempsF":
            { $map:
               {
                  input: "$tempsC",
                  as: "tempInCelsius",
                  in: { $add: [ { $multiply: [ "$$tempInCelsius", 9/5 ] }, 32 ] }
               }
            }
       }
    }
] )

This operation returns the following results:

{ "_id" : 1, "date" : ISODate("2019-06-23T00:00:00Z"), "tempsC : [ 4, 12, 17 ], "tempsF" : [ 39.2, 53.6, 62.6 ] }
{ "_id" : 2, "date" : ISODate("2019-07-07T00:00:00Z"), "tempsC" : [ 14, 24, 11 ], "tempsF" : [ 57.2, 75.2, 51.8 ] }
{ "_id" : 3, "date" : ISODate("2019-10-30T00:00:00Z"), "tempsC" : [ 18, 6, 8 ], "tempsF" : [ 64.4, 42.8, 46.4 ] }

See also