$group (aggregation)

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Definition

Considerations
Examples
Additional Resources

Definition

$group

The $group stage separates documents into groups according to a "group key". The output is one document for each unique group key.

A group key is often a field, or group of fields. The group key can also be the result of an expression. Use the _id field in the $group pipeline stage to set the group key. See below for usage examples.

In the $group stage output, the _id field is set to the group key for that document.

The output documents can also contain additional fields that are set using accumulator expressions.

Note

$group does not order its output documents.

The $group stage has the following prototype form:

{
  $group:
    {
      _id: <expression>, // Group key
      <field1>: { <accumulator1> : <expression1> },
      ...
    }
 }

Field	Description
`_id`	Required. The `_id` expression specifies the group key. If you specify an `_id` value of null, or any other constant value, the `$group` stage returns a single document that aggregates values across all of the input documents. See the Group by Null example.
`field`	Optional. Computed using the accumulator operators.

The _id and the accumulator operators can accept any valid expression. For more information on expressions, see Expressions.

Considerations

Accumulator Operator

The <accumulator> operator must be one of the following accumulator operators:

Changed in version 5.0.

Name	Description
`$accumulator`	Returns the result of a user-defined accumulator function.
`$addToSet`	Returns an array of unique expression values for each group. Order of the array elements is undefined. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$avg`	Returns an average of numerical values. Ignores non-numeric values. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$bottom`	Returns the bottom element within a group according to the specified sort order. New in version 5.2. Available in the `$group` and `$setWindowFields` stages.
`$bottomN`	Returns an aggregation of the bottom `n` fields within a group, according to the specified sort order. New in version 5.2. Available in the `$group` and `$setWindowFields` stages.
`$count`	Returns the number of documents in a group. Distinct from the `$count` pipeline stage. New in version 5.0: Available in the `$group` and `$setWindowFields` stages.
`$first`	Returns the result of an expression for the first document in a group. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$firstN`	Returns an aggregation of the first `n` elements within a group. Only meaningful when documents are in a defined order. Distinct from the `$firstN` array operator. New in version 5.2: Available in the `$group`, expression and `$setWindowFields` stages.
`$last`	Returns the result of an expression for the last document in a group. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$lastN`	Returns an aggregation of the last `n` elements within a group. Only meaningful when documents are in a defined order. Distinct from the `$lastN` array operator. New in version 5.2: Available in the `$group`, expression and `$setWindowFields` stages.
`$max`	Returns the highest expression value for each group. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$maxN`	Returns an aggregation of the `n` maximum valued elements in a group. Distinct from the `$maxN` array operator. New in version 5.2. Available in `$group`, `$setWindowFields` and as an expression.
`$mergeObjects`	Returns a document created by combining the input documents for each group.
`$min`	Returns the lowest expression value for each group. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$push`	Returns an array of expression values for documents in each group. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$stdDevPop`	Returns the population standard deviation of the input values. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$stdDevSamp`	Returns the sample standard deviation of the input values. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$sum`	Returns a sum of numerical values. Ignores non-numeric values. Changed in version 5.0: Available in the `$setWindowFields` stage.
`$top`	Returns the top element within a group according to the specified sort order. New in version 5.2. Available in the `$group` and `$setWindowFields` stages.
`$topN`	Returns an aggregation of the top `n` fields within a group, according to the specified sort order. New in version 5.2. Available in the `$group` and `$setWindowFields` stages.

`$group` and Memory Restrictions

The $group stage has a limit of 100 megabytes of RAM. By default, if the stage exceeds this limit, $group returns an error. To allow more space for stage processing, use the allowDiskUse option to enable aggregation pipeline stages to write data to temporary files.

Tip

`$group` Performance Optimizations

This section describes optimizations to improve the performance of $group. There are optimizations that you can make manually and optimizations MongoDB makes internally.

Optimization to Return the First or Last Document of Each Group

If a pipeline sorts and groups by the same field and the $group stage only uses the $first or $last accumulator operator, consider adding an index on the grouped field which matches the sort order. In some cases, the $group stage can use the index to quickly find the first or last document of each group.

Example

If a collection named foo contains an index { x: 1, y: 1 }, the following pipeline can use that index to find the first document of each group:

db.foo.aggregate([
  {
    $sort:{ x : 1, y : 1 }
  },
  {
    $group: {
      _id: { x : "$x" },
      y: { $first : "$y" }
    }
  }
])

Slot-Based Query Execution Engine

Starting in version 5.2, MongoDB uses the slot-based execution query engine to execute $group stages if either:

$group is the first stage in the pipeline.
All preceding stages in the pipeline can also be executed by the slot-based engine.

For more information, see $group Optimization.

Examples

Count the Number of Documents in a Collection

In mongosh, create a sample collection named sales with the following documents:

db.sales.insertMany([
  { "_id" : 1, "item" : "abc", "price" : NumberDecimal("10"), "quantity" : NumberInt("2"), "date" : ISODate("2014-03-01T08:00:00Z") },
  { "_id" : 2, "item" : "jkl", "price" : NumberDecimal("20"), "quantity" : NumberInt("1"), "date" : ISODate("2014-03-01T09:00:00Z") },
  { "_id" : 3, "item" : "xyz", "price" : NumberDecimal("5"), "quantity" : NumberInt( "10"), "date" : ISODate("2014-03-15T09:00:00Z") },
  { "_id" : 4, "item" : "xyz", "price" : NumberDecimal("5"), "quantity" :  NumberInt("20") , "date" : ISODate("2014-04-04T11:21:39.736Z") },
  { "_id" : 5, "item" : "abc", "price" : NumberDecimal("10"), "quantity" : NumberInt("10") , "date" : ISODate("2014-04-04T21:23:13.331Z") },
  { "_id" : 6, "item" : "def", "price" : NumberDecimal("7.5"), "quantity": NumberInt("5" ) , "date" : ISODate("2015-06-04T05:08:13Z") },
  { "_id" : 7, "item" : "def", "price" : NumberDecimal("7.5"), "quantity": NumberInt("10") , "date" : ISODate("2015-09-10T08:43:00Z") },
  { "_id" : 8, "item" : "abc", "price" : NumberDecimal("10"), "quantity" : NumberInt("5" ) , "date" : ISODate("2016-02-06T20:20:13Z") },
])

The following aggregation operation uses the $group stage to count the number of documents in the sales collection:

db.sales.aggregate( [
  {
    $group: {
       _id: null,
       count: { $count: { } }
    }
  }
] )

The operation returns the following result:

{ "_id" : null, "count" : 8 }

This aggregation operation is equivalent to the following SQL statement:

SELECT COUNT(*) AS count FROM sales

Tip

Retrieve Distinct Values

The following aggregation operation uses the $group stage to retrieve the distinct item values from the sales collection:

db.sales.aggregate( [ { $group : { _id : "$item" } } ] )

The operation returns the following result:

{ "_id" : "abc" }
{ "_id" : "jkl" }
{ "_id" : "def" }
{ "_id" : "xyz" }

Group by Item Having

The following aggregation operation groups documents by the item field, calculating the total sale amount per item and returning only the items with total sale amount greater than or equal to 100:

db.sales.aggregate(
  [
    // First Stage
    {
      $group :
        {
          _id : "$item",
          totalSaleAmount: { $sum: { $multiply: [ "$price", "$quantity" ] } }
        }
     },
     // Second Stage
     {
       $match: { "totalSaleAmount": { $gte: 100 } }
     }
   ]
 )

First Stage:: The $group stage groups the documents by item to retrieve the distinct item values. This stage returns the totalSaleAmount for each item.
Second Stage:: The $match stage filters the resulting documents to only return items with a totalSaleAmount greater than or equal to 100.

The operation returns the following result:

{ "_id" : "abc", "totalSaleAmount" : NumberDecimal("170") }
{ "_id" : "xyz", "totalSaleAmount" : NumberDecimal("150") }
{ "_id" : "def", "totalSaleAmount" : NumberDecimal("112.5") }

This aggregation operation is equivalent to the following SQL statement:

SELECT item,
   Sum(( price * quantity )) AS totalSaleAmount
FROM   sales
GROUP  BY item
HAVING totalSaleAmount >= 100

Tip

Calculate Count, Sum, and Average

In mongosh, create a sample collection named sales with the following documents:

db.sales.insertMany([
  { "_id" : 1, "item" : "abc", "price" : NumberDecimal("10"), "quantity" : NumberInt("2"), "date" : ISODate("2014-03-01T08:00:00Z") },
  { "_id" : 2, "item" : "jkl", "price" : NumberDecimal("20"), "quantity" : NumberInt("1"), "date" : ISODate("2014-03-01T09:00:00Z") },
  { "_id" : 3, "item" : "xyz", "price" : NumberDecimal("5"), "quantity" : NumberInt( "10"), "date" : ISODate("2014-03-15T09:00:00Z") },
  { "_id" : 4, "item" : "xyz", "price" : NumberDecimal("5"), "quantity" :  NumberInt("20") , "date" : ISODate("2014-04-04T11:21:39.736Z") },
  { "_id" : 5, "item" : "abc", "price" : NumberDecimal("10"), "quantity" : NumberInt("10") , "date" : ISODate("2014-04-04T21:23:13.331Z") },
  { "_id" : 6, "item" : "def", "price" : NumberDecimal("7.5"), "quantity": NumberInt("5" ) , "date" : ISODate("2015-06-04T05:08:13Z") },
  { "_id" : 7, "item" : "def", "price" : NumberDecimal("7.5"), "quantity": NumberInt("10") , "date" : ISODate("2015-09-10T08:43:00Z") },
  { "_id" : 8, "item" : "abc", "price" : NumberDecimal("10"), "quantity" : NumberInt("5" ) , "date" : ISODate("2016-02-06T20:20:13Z") },
])

Group by Day of the Year

The following pipeline calculates the total sales amount, average sales quantity, and sale count for each day in the year 2014:

db.sales.aggregate([
  // First Stage
  {
    $match : { "date": { $gte: new ISODate("2014-01-01"), $lt: new ISODate("2015-01-01") } }
  },
  // Second Stage
  {
    $group : {
       _id : { $dateToString: { format: "%Y-%m-%d", date: "$date" } },
       totalSaleAmount: { $sum: { $multiply: [ "$price", "$quantity" ] } },
       averageQuantity: { $avg: "$quantity" },
       count: { $sum: 1 }
    }
  },
  // Third Stage
  {
    $sort : { totalSaleAmount: -1 }
  }
 ])

First Stage:: The $match stage filters the documents to only pass documents from the year 2014 to the next stage.
Second Stage:: The $group stage groups the documents by date and calculates the total sale amount, average quantity, and total count of the documents in each group.
Third Stage:: The $sort stage sorts the results by the total sale amount for each group in descending order.

The operation returns the following results:

{ "_id" : "2014-04-04", "totalSaleAmount" : NumberDecimal("200"), "averageQuantity" : 15, "count" : 2 }
{ "_id" : "2014-03-15", "totalSaleAmount" : NumberDecimal("50"), "averageQuantity" : 10, "count" : 1 }
{ "_id" : "2014-03-01", "totalSaleAmount" : NumberDecimal("40"), "averageQuantity" : 1.5, "count" : 2 }

This aggregation operation is equivalent to the following SQL statement:

SELECT date,
       Sum(( price * quantity )) AS totalSaleAmount,
       Avg(quantity)             AS averageQuantity,
       Count(*)                  AS Count
FROM   sales
WHERE date >= '01/01/2014' AND date < '01/01/2015'
GROUP  BY date
ORDER  BY totalSaleAmount DESC

Tip

Pivot Data

In mongosh, create a sample collection named books with the following documents:

db.books.insertMany([
  { "_id" : 8751, "title" : "The Banquet", "author" : "Dante", "copies" : 2 },
  { "_id" : 8752, "title" : "Divine Comedy", "author" : "Dante", "copies" : 1 },
  { "_id" : 8645, "title" : "Eclogues", "author" : "Dante", "copies" : 2 },
  { "_id" : 7000, "title" : "The Odyssey", "author" : "Homer", "copies" : 10 },
  { "_id" : 7020, "title" : "Iliad", "author" : "Homer", "copies" : 10 }
])

Group `title` by `author`

The following aggregation operation pivots the data in the books collection to have titles grouped by authors.

db.books.aggregate([
   { $group : { _id : "$author", books: { $push: "$title" } } }
 ])

The operation returns the following documents:

{ "_id" : "Homer", "books" : [ "The Odyssey", "Iliad" ] }
{ "_id" : "Dante", "books" : [ "The Banquet", "Divine Comedy", "Eclogues" ] }

Group Documents by `author`

The following aggregation operation groups documents by author:

db.books.aggregate([
   // First Stage
   {
     $group : { _id : "$author", books: { $push: "$$ROOT" } }
   },
   // Second Stage
   {
     $addFields:
       {
         totalCopies : { $sum: "$books.copies" }
       }
   }
 ])

First Stage:

$group uses the $$ROOT system variable to group the entire documents by authors. This stage passes the following documents to the next stage:

{ "_id" : "Homer",
  "books" :
    [
       { "_id" : 7000, "title" : "The Odyssey", "author" : "Homer", "copies" : 10 },
       { "_id" : 7020, "title" : "Iliad", "author" : "Homer", "copies" : 10 }
    ]
 },
 { "_id" : "Dante",
   "books" :
     [
       { "_id" : 8751, "title" : "The Banquet", "author" : "Dante", "copies" : 2 },
       { "_id" : 8752, "title" : "Divine Comedy", "author" : "Dante", "copies" : 1 },
       { "_id" : 8645, "title" : "Eclogues", "author" : "Dante", "copies" : 2 }
     ]
 }

Second Stage:

$addFields adds a field to the output containing the total copies of books for each author.

Note

The resulting documents must not exceed the BSON Document Size limit of 16 megabytes.

The operation returns the following documents:

{
  "_id" : "Homer",
  "books" :
     [
       { "_id" : 7000, "title" : "The Odyssey", "author" : "Homer", "copies" : 10 },
       { "_id" : 7020, "title" : "Iliad", "author" : "Homer", "copies" : 10 }
     ],
   "totalCopies" : 20
}
{
  "_id" : "Dante",
  "books" :
     [
       { "_id" : 8751, "title" : "The Banquet", "author" : "Dante", "copies" : 2 },
       { "_id" : 8752, "title" : "Divine Comedy", "author" : "Dante", "copies" : 1 },
       { "_id" : 8645, "title" : "Eclogues", "author" : "Dante", "copies" : 2 }
     ],
   "totalCopies" : 5
}

Tip

Additional Resources

The Aggregation with the Zip Code Data Set tutorial provides an extensive example of the $group operator in a common use case.

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$graphLookup (aggregation)

$indexStats (aggregation)

$group (aggregation)

Definition

Note

Considerations

Accumulator Operator

`$group` and Memory Restrictions

Tip

See also:

`$group` Performance Optimizations

Optimization to Return the First or Last Document of Each Group

Example

Slot-Based Query Execution Engine

Examples

Count the Number of Documents in a Collection

Tip

See also:

Retrieve Distinct Values

Group by Item Having

Tip

See also:

Calculate Count, Sum, and Average

Group by Day of the Year

Tip

See also:

Group by `null`

Tip

See also:

Pivot Data

Group `title` by `author`

Group Documents by `author`

Note

Tip

See also:

Additional Resources

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Definition

Note

Considerations

Accumulator Operator

$group and Memory Restrictions

Tip

See also:

$group Performance Optimizations

Optimization to Return the First or Last Document of Each Group

Example

Slot-Based Query Execution Engine

Examples

Count the Number of Documents in a Collection

Tip

See also:

Retrieve Distinct Values

Group by Item Having

Tip

See also:

Calculate Count, Sum, and Average

Group by Day of the Year

Tip

See also:

Group by null

Tip

See also:

Pivot Data

Group title by author

Group Documents by author

Note

Tip

See also:

Additional Resources

$group (aggregation)

`$group` and Memory Restrictions

`$group` Performance Optimizations

Group by `null`

Group `title` by `author`

Group Documents by `author`