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Aggregation Operators

$avg (aggregation)

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  • Definition
  • Syntax
  • Behavior
  • Examples

Definition

Changed in version 5.0.

$avg

Returns the average value of the numeric values. $avg ignores non-numeric values.

$avg is available in these stages:

  • $addFields

  • $bucket

  • $bucketAuto

  • $group

  • $match stage that includes an $expr expression

  • $project

  • $replaceRoot

  • $replaceWith

  • $set

  • $setWindowFields (Available starting in MongoDB 5.0)

Syntax

When used in the $bucket, $bucketAuto, $group, and $setWindowFields stages, $avg has this syntax:

{ $avg: <expression> }

When used in other supported stages, $avg has one of two syntaxes:

  • $avg has one specified expression as its operand:

    { $avg: <expression> }

  • $avg has a list of specified expressions as its operand:

    { $avg: [ <expression1>, <expression2> ... ]  }

For more information on expressions, see Expression Operators.

Behavior

Non-numeric or Missing Values

$avg ignores non-numeric values, including missing values. If all of the operands for the average are non-numeric, $avg returns null since the average of zero values is undefined.

Array Operand

In the $group stage, if the expression resolves to an array, $avg treats the operand as a non-numerical value.

In the other supported stages:

  • With a single expression as its operand, if the expression resolves to an array, $avg traverses into the array to operate on the numerical elements of the array to return a single value.

  • With a list of expressions as its operand, if any of the expressions resolves to an array, $avg does not traverse into the array but instead treats the array as a non-numerical value.

Examples

Use in $group Stage

Consider a sales collection with the following documents:

{ "_id" : 1, "item" : "abc", "price" : 10, "quantity" : 2, "date" : ISODate("2014-01-01T08:00:00Z") }
{ "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1, "date" : ISODate("2014-02-03T09:00:00Z") }
{ "_id" : 3, "item" : "xyz", "price" : 5, "quantity" : 5, "date" : ISODate("2014-02-03T09:05:00Z") }
{ "_id" : 4, "item" : "abc", "price" : 10, "quantity" : 10, "date" : ISODate("2014-02-15T08:00:00Z") }
{ "_id" : 5, "item" : "xyz", "price" : 5, "quantity" : 10, "date" : ISODate("2014-02-15T09:12:00Z") }

Grouping the documents by the item field, the following operation uses the $avg accumulator to compute the average amount and average quantity for each grouping.

db.sales.aggregate(
   [
     {
       $group:
         {
           _id: "$item",
           avgAmount: { $avg: { $multiply: [ "$price", "$quantity" ] } },
           avgQuantity: { $avg: "$quantity" }
         }
     }
   ]
)

The operation returns the following results:

{ "_id" : "xyz", "avgAmount" : 37.5, "avgQuantity" : 7.5 }
{ "_id" : "jkl", "avgAmount" : 20, "avgQuantity" : 1 }
{ "_id" : "abc", "avgAmount" : 60, "avgQuantity" : 6 }

Use in $project Stage

A collection students contains the following documents:

{ "_id": 1, "quizzes": [ 10, 6, 7 ], "labs": [ 5, 8 ], "final": 80, "midterm": 75 }
{ "_id": 2, "quizzes": [ 9, 10 ], "labs": [ 8, 8 ], "final": 95, "midterm": 80 }
{ "_id": 3, "quizzes": [ 4, 5, 5 ], "labs": [ 6, 5 ], "final": 78, "midterm": 70 }

The following example uses the $avg in the $project stage to calculate the average quiz scores, the average lab scores, and the average of the final and the midterm:

db.students.aggregate([
   { $project: { quizAvg: { $avg: "$quizzes"}, labAvg: { $avg: "$labs" }, examAvg: { $avg: [ "$final", "$midterm" ] } } }
])

The operation results in the following documents:

{ "_id" : 1, "quizAvg" : 7.666666666666667, "labAvg" : 6.5, "examAvg" : 77.5 }
{ "_id" : 2, "quizAvg" : 9.5, "labAvg" : 8, "examAvg" : 87.5 }
{ "_id" : 3, "quizAvg" : 4.666666666666667, "labAvg" : 5.5, "examAvg" : 74 }

Use in $setWindowFields Stage

New in version 5.0.

Create a cakeSales collection that contains cake sales in the states of California (CA) and Washington (WA):

db.cakeSales.insertMany( [
   { _id: 0, type: "chocolate", orderDate: new Date("2020-05-18T14:10:30Z"),
     state: "CA", price: 13, quantity: 120 },
   { _id: 1, type: "chocolate", orderDate: new Date("2021-03-20T11:30:05Z"),
     state: "WA", price: 14, quantity: 140 },
   { _id: 2, type: "vanilla", orderDate: new Date("2021-01-11T06:31:15Z"),
     state: "CA", price: 12, quantity: 145 },
   { _id: 3, type: "vanilla", orderDate: new Date("2020-02-08T13:13:23Z"),
     state: "WA", price: 13, quantity: 104 },
   { _id: 4, type: "strawberry", orderDate: new Date("2019-05-18T16:09:01Z"),
     state: "CA", price: 41, quantity: 162 },
   { _id: 5, type: "strawberry", orderDate: new Date("2019-01-08T06:12:03Z"),
     state: "WA", price: 43, quantity: 134 }
] )

This example uses $avg in the $setWindowFields stage to output the moving average for the cake sales quantity for each state:

db.cakeSales.aggregate( [
   {
      $setWindowFields: {
         partitionBy: "$state",
         sortBy: { orderDate: 1 },
         output: {
            averageQuantityForState: {
               $avg: "$quantity",
               window: {
                  documents: [ "unbounded", "current" ]
               }
            }
         }
      }
   }
] )

In the example:

  • partitionBy: "$state" partitions the documents in the collection by state. There are partitions for CA and WA.

  • sortBy: { orderDate: 1 } sorts the documents in each partition by orderDate in ascending order (1), so the earliest orderDate is first.

  • output sets the averageQuantityForState field to the moving average quantity using $avg for the documents in a documents window.

    The window contains documents between an unbounded lower limit and the current document in the output. This means $avg returns the moving average quantity for the documents between the beginning of the partition and the current document.

In this output, the moving average quantity for CA and WA is shown in the averageQuantityForState field:

{ "_id" : 4, "type" : "strawberry", "orderDate" : ISODate("2019-05-18T16:09:01Z"),
  "state" : "CA", "price" : 41, "quantity" : 162, "averageQuantityForState" : 162 }
{ "_id" : 0, "type" : "chocolate", "orderDate" : ISODate("2020-05-18T14:10:30Z"),
  "state" : "CA", "price" : 13, "quantity" : 120, "averageQuantityForState" : 141 }
{ "_id" : 2, "type" : "vanilla", "orderDate" : ISODate("2021-01-11T06:31:15Z"),
  "state" : "CA", "price" : 12, "quantity" : 145, "averageQuantityForState" : 142.33333333333334 }
{ "_id" : 5, "type" : "strawberry", "orderDate" : ISODate("2019-01-08T06:12:03Z"),
  "state" : "WA", "price" : 43, "quantity" : 134, "averageQuantityForState" : 134 }
{ "_id" : 3, "type" : "vanilla", "orderDate" : ISODate("2020-02-08T13:13:23Z"),
  "state" : "WA", "price" : 13, "quantity" : 104, "averageQuantityForState" : 119 }
{ "_id" : 1, "type" : "chocolate", "orderDate" : ISODate("2021-03-20T11:30:05Z"),
  "state" : "WA", "price" : 14, "quantity" : 140, "averageQuantityForState" : 126 }

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