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$reduce (aggregation)

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Definition

$reduce

New in version 3.4.

Applies an expression to each element in an array and combines them into a single value.

$reduce has the following syntax:

copy 
{
    $reduce: {
        input: <array>,
        initialValue: <expression>,
        in: <expression>
    }
}
Field Type Description
input array

Can be any valid expression that resolves to an array. For more information on expressions, see Expressions.

If the argument resolves to a value of null or refers to a missing field, $reduce returns null.

If the argument does not resolve to an array or null nor refers to a missing field, $reduce returns an error.
initialValue expression The initial cumulative value set before in is applied to the first element of the input array.
in expression

A valid expression that $reduce applies to each element in the input array in left-to-right order. Wrap the input value with $reverseArray to yield the equivalent of applying the combining expression from right-to-left.

During evaluation of the in expression, two variables will be available:

  • value is the variable that represents the cumulative value of the expression.
  • this is the variable that refers to the element being processed.

If input resolves to an empty array, $reduce returns initialValue.

Example Results 
 {
    $reduce: {
       input: ["a", "b", "c"],
       initialValue: "",
       in: { $concat : ["$$value", "$$this"] }
     }
 }
"abc" 
{
   $reduce: {
      input: [ 1, 2, 3, 4 ],
      initialValue: { sum: 5, product: 2 },
      in: {
         sum: { $add : ["$$value.sum", "$$this"] },
         product: { $multiply: [ "$$value.product", "$$this" ] }
      }
   }
}
{ "sum" : 15, "product" : 48 } 
{
   $reduce: {
      input: [ [ 3, 4 ], [ 5, 6 ] ],
      initialValue: [ 1, 2 ],
      in: { $concatArrays : ["$$value", "$$this"] }
   }
}
[ 1, 2, 3, 4, 5, 6 ]

Examples

Multiplication

Probability

A collection named events contains the events of a probability experiment. Each experiment can have multiple events, such as rolling a die several times or drawing several cards (without replacement) in succession to achieve a desired result. In order to obtain the overall probability of the experiment, we will need to multiply the probability of each event in the experiment.

copy 
{_id:1, "type":"die", "experimentId":"r5", "description":"Roll a 5", "eventNum":1, "probability":0.16666666666667}
{_id:2, "type":"card", "experimentId":"d3rc", "description":"Draw 3 red cards", "eventNum":1, "probability":0.5}
{_id:3, "type":"card", "experimentId":"d3rc", "description":"Draw 3 red cards", "eventNum":2, "probability":0.49019607843137}
{_id:4, "type":"card", "experimentId":"d3rc", "description":"Draw 3 red cards", "eventNum":3, "probability":0.48}
{_id:5, "type":"die", "experimentId":"r16", "description":"Roll a 1 then a 6", "eventNum":1, "probability":0.16666666666667}
{_id:6, "type":"die", "experimentId":"r16", "description":"Roll a 1 then a 6", "eventNum":2, "probability":0.16666666666667}
{_id:7, "type":"card", "experimentId":"dak", "description":"Draw an ace, then a king", "eventNum":1, "probability":0.07692307692308}
{_id:8, "type":"card", "experimentId":"dak", "description":"Draw an ace, then a king", "eventNum":2, "probability":0.07843137254902}

Steps:

  1. Use $group to group by the experimentId and use $push to create an array with the probability of each event.
  2. Use $reduce with $multiply to multiply and combine the elements of probabilityArr into a single value and project it.
copy 
db.probability.aggregate(
  [
    {
      $group: {
        _id: "$experimentId",
        "probabilityArr": { $push: "$probability" }
      }
    },
    {
      $project: {
        "description": 1,
        "results": {
          $reduce: {
            input: "$probabilityArr",
            initialValue: 1,
            in: { $multiply: [ "$$value", "$$this" ] }
          }
        }
      }
    }
  ]
)

The operation returns the following:

copy 
{ "_id" : "dak", "results" : 0.00603318250377101 }
{ "_id" : "r5", "results" : 0.16666666666667 }
{ "_id" : "r16", "results" : 0.027777777777778886 }
{ "_id" : "d3rc", "results" : 0.11764705882352879 }

Discounted Merchandise

A collection named clothes contains the following documents:

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{ "_id" : 1, "productId" : "ts1", "description" : "T-Shirt", "color" : "black", "size" : "M", "price" : 20, "discounts" : [ 0.5, 0.1 ] }
{ "_id" : 2, "productId" : "j1", "description" : "Jeans", "color" : "blue", "size" : "36", "price" : 40, "discounts" : [ 0.25, 0.15, 0.05 ] }
{ "_id" : 3, "productId" : "s1", "description" : "Shorts", "color" : "beige", "size" : "32", "price" : 30, "discounts" : [ 0.15, 0.05 ] }
{ "_id" : 4, "productId" : "ts2", "description" : "Cool T-Shirt", "color" : "White", "size" : "L", "price" : 25, "discounts" : [ 0.3 ] }
{ "_id" : 5, "productId" : "j2", "description" : "Designer Jeans", "color" : "blue", "size" : "30", "price" : 80, "discounts" : [ 0.1, 0.25 ] }

Each document contains a discounts array containing the currently available percent-off coupons for each item. If each discount can be applied to the product once, we can calculate the lowest price by using $reduce to apply the following formula for each element in the discounts array: (1 - discount) * price.

copy 
db.clothes.aggregate(
  [
    {
      $project: {
        "discountedPrice": {
          $reduce: {
            input: "$discounts",
            initialValue: "$price",
            in: { $multiply: [ "$$value", { $subtract: [ 1, "$$this" ] } ] }
          }
        }
      }
    }
  ]
)

The operation returns the following:

copy 
{ "_id" : ObjectId("57c893067054e6e47674ce01"), "discountedPrice" : 9 }
{ "_id" : ObjectId("57c9932b7054e6e47674ce12"), "discountedPrice" : 24.224999999999998 }
{ "_id" : ObjectId("57c993457054e6e47674ce13"), "discountedPrice" : 24.224999999999998 }
{ "_id" : ObjectId("57c993687054e6e47674ce14"), "discountedPrice" : 17.5 }
{ "_id" : ObjectId("57c993837054e6e47674ce15"), "discountedPrice" : 54 }

String Concatenation

A collection named people contains the following documents:

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{ "_id" : 1, "name" : "Melissa", "hobbies" : [ "softball", "drawing", "reading" ] }
{ "_id" : 2, "name" : "Brad", "hobbies" : [ "gaming", "skateboarding" ] }
{ "_id" : 3, "name" : "Scott", "hobbies" : [ "basketball", "music", "fishing" ] }
{ "_id" : 4, "name" : "Tracey", "hobbies" : [ "acting", "yoga" ] }
{ "_id" : 5, "name" : "Josh", "hobbies" : [ "programming" ] }
{ "_id" : 6, "name" : "Claire" }

The following example reduces the hobbies array of strings into a single string bio:

copy 
db.people.aggregate(
   [
     // Filter to return only non-empty arrays
     { $match: { "hobbies": { $gt: [ ] } } },
     {
       $project: {
         "name": 1,
         "bio": {
           $reduce: {
             input: "$hobbies",
             initialValue: "My hobbies include:",
             in: {
               $concat: [
                 "$$value",
                 {
                   $cond: {
                     if: { $eq: [ "$$value", "My hobbies include:" ] },
                     then: " ",
                     else: ", "
                   }
                 },
                 "$$this"
               ]
             }
           }
         }
       }
     }
   ]
)

The operation returns the following:

copy 
{ "_id" : 1, "name" : "Melissa", "bio" : "My hobbies include: softball, drawing, reading" }
{ "_id" : 2, "name" : "Brad", "bio" : "My hobbies include: gaming, skateboarding" }
{ "_id" : 3, "name" : "Scott", "bio" : "My hobbies include: basketball, music, fishing" }
{ "_id" : 4, "name" : "Tracey", "bio" : "My hobbies include: acting, yoga" }
{ "_id" : 5, "name" : "Josh", "bio" : "My hobbies include: programming" }

Array Concatenation

A collection named matrices contains the following documents:

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{ "_id" : 1, "arr" : [ [ 24, 55, 79 ], [ 14, 78, 35 ], [ 84, 90, 3 ], [ 50, 89, 70 ] ] }
{ "_id" : 2, "arr" : [ [ 39, 32, 43, 7 ], [ 62, 17, 80, 64 ], [ 17, 88, 11, 73 ] ] }
{ "_id" : 3, "arr" : [ [ 42 ], [ 26, 59 ], [ 17 ], [ 72, 19, 35 ] ] }
{ "_id" : 4 }

Computing a Single Reduction

The following example collapses the two dimensional arrays into a single array collapsed:

copy 
db.arrayconcat.aggregate(
  [
    {
      $project: {
        "collapsed": {
          $reduce: {
            input: "$arr",
            initialValue: [ ],
            in: { $concatArrays: [ "$$value", "$$this" ] }
          }
        }
      }
    }
  ]
)

The operation returns the following:

copy 
{ "_id" : 1, "collapsed" : [ 24, 55, 79, 14, 78, 35, 84, 90, 3, 50, 89, 70 ] }
{ "_id" : 2, "collapsed" : [ 39, 32, 43, 7, 62, 17, 80, 64, 17, 88, 11, 73 ] }
{ "_id" : 3, "collapsed" : [ 42, 26, 59, 17, 72, 19, 35 ] }
{ "_id" : 4, "collapsed" : null }

Computing a Multiple Reductions

The following example performs the same two dimensional array collapse as the example above, but also creates a new array containing only the first element of each array.

copy 
db.arrayconcat.aggregate(
  [
    {
      $project: {
        "results": {
          $reduce: {
            input: "$arr",
            initialValue: [ ],
            in: {
              "collapsed": {
                $concatArrays: [ "$$value.collapsed", "$$this" ]
              },
              "firstValues": {
                $concatArrays: [ "$$value.firstValues", { $slice: [ "$$this", 1 ] } ]
              }
            }
          }
        }
      }
    }
  ]
)

The operation returns the following:

copy 
{ "_id" : 1, "results" : { "collapsed" : [ 24, 55, 79, 14, 78, 35, 84, 90, 3, 50, 89, 70 ], "firstValues" : [ 24, 14, 84, 50 ] } }
{ "_id" : 2, "results" : { "collapsed" : [ 39, 32, 43, 7, 62, 17, 80, 64, 17, 88, 11, 73 ], "firstValues" : [ 39, 62, 17 ] } }
{ "_id" : 3, "results" : { "collapsed" : [ 42, 26, 59, 17, 72, 19, 35 ], "firstValues" : [ 42, 26, 17, 72 ] } }
{ "_id" : 4, "results" : null }

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