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- $group (aggregation)
$group (aggregation)¶
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Definition¶
- $group¶
Groups documents by some specified expression and outputs to the next stage a document for each distinct grouping. The output documents contain an _id field which contains the distinct group by key. The output documents can also contain computed fields that hold the values of some accumulator expression grouped by the $group‘s _id field. $group does not order its output documents.
The $group stage has the following prototype form:
{ $group: { _id: <expression>, <field1>: { <accumulator1> : <expression1> }, ... } }
The _id field is mandatory; however, you can specify an _id value of null to calculate accumulated values for all the input documents as a whole.
The remaining computed fields are optional and computed using the <accumulator> operators.
The _id and the <accumulator> expressions 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:
Name | Description |
---|---|
$sum | Returns a sum of numerical values. Ignores non-numeric values. Changed in version 3.2: Available in both $group and $project stages. |
$avg | Returns an average of numerical values. Ignores non-numeric values. Changed in version 3.2: Available in both $group and $project stages. |
$first | Returns a value from the first document for each group. Order is only defined if the documents are in a defined order. Available in $group stage only. |
$last | Returns a value from the last document for each group. Order is only defined if the documents are in a defined order. Available in $group stage only. |
$max | Returns the highest expression value for each group. Changed in version 3.2: Available in both $group and $project stages. |
$min | Returns the lowest expression value for each group. Changed in version 3.2: Available in both $group and $project stages. |
$push | Returns an array of expression values for each group. Available in $group stage only. |
$addToSet | Returns an array of unique expression values for each group. Order of the array elements is undefined. Available in $group stage only. |
$stdDevPop | Returns the population standard deviation of the input values. Changed in version 3.2: Available in both $group and $project stages. |
$stdDevSamp | Returns the sample standard deviation of the input values. Changed in version 3.2: Available in both $group and $project stages. |
$group Operator and Memory¶
The $group stage has a limit of 100 megabytes of RAM. By default, if the stage exceeds this limit, $group will produce an error. However, to allow for the handling of large datasets, set the allowDiskUse option to true to enable $group operations to write to temporary files. See db.collection.aggregate() method and the aggregate command for details.
Changed in version 2.6: MongoDB introduces a limit of 100 megabytes of RAM for the $group stage as well as the allowDiskUse option to handle operations for large datasets.
Examples¶
Calculate Count, Sum, and Average¶
Given a collection sales with the following documents:
{ "_id" : 1, "item" : "abc", "price" : 10, "quantity" : 2, "date" : ISODate("2014-03-01T08:00:00Z") }
{ "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1, "date" : ISODate("2014-03-01T09:00:00Z") }
{ "_id" : 3, "item" : "xyz", "price" : 5, "quantity" : 10, "date" : ISODate("2014-03-15T09:00:00Z") }
{ "_id" : 4, "item" : "xyz", "price" : 5, "quantity" : 20, "date" : ISODate("2014-04-04T11:21:39.736Z") }
{ "_id" : 5, "item" : "abc", "price" : 10, "quantity" : 10, "date" : ISODate("2014-04-04T21:23:13.331Z") }
Group by Month, Day, and Year¶
The following aggregation operation uses the $group stage to group the documents by the month, day, and year and calculates the total price and the average quantity as well as counts the documents per each group:
db.sales.aggregate(
[
{
$group : {
_id : { month: { $month: "$date" }, day: { $dayOfMonth: "$date" }, year: { $year: "$date" } },
totalPrice: { $sum: { $multiply: [ "$price", "$quantity" ] } },
averageQuantity: { $avg: "$quantity" },
count: { $sum: 1 }
}
}
]
)
The operation returns the following results:
{ "_id" : { "month" : 3, "day" : 15, "year" : 2014 }, "totalPrice" : 50, "averageQuantity" : 10, "count" : 1 }
{ "_id" : { "month" : 4, "day" : 4, "year" : 2014 }, "totalPrice" : 200, "averageQuantity" : 15, "count" : 2 }
{ "_id" : { "month" : 3, "day" : 1, "year" : 2014 }, "totalPrice" : 40, "averageQuantity" : 1.5, "count" : 2 }
Group by null¶
The following aggregation operation specifies a group _id of null, calculating the total price and the average quantity as well as counts for all documents in the collection:
db.sales.aggregate(
[
{
$group : {
_id : null,
totalPrice: { $sum: { $multiply: [ "$price", "$quantity" ] } },
averageQuantity: { $avg: "$quantity" },
count: { $sum: 1 }
}
}
]
)
The operation returns the following result:
{ "_id" : null, "totalPrice" : 290, "averageQuantity" : 8.6, "count" : 5 }
Retrieve Distinct Values¶
Given a collection sales with the following documents:
{ "_id" : 1, "item" : "abc", "price" : 10, "quantity" : 2, "date" : ISODate("2014-03-01T08:00:00Z") }
{ "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1, "date" : ISODate("2014-03-01T09:00:00Z") }
{ "_id" : 3, "item" : "xyz", "price" : 5, "quantity" : 10, "date" : ISODate("2014-03-15T09:00:00Z") }
{ "_id" : 4, "item" : "xyz", "price" : 5, "quantity" : 20, "date" : ISODate("2014-04-04T11:21:39.736Z") }
{ "_id" : 5, "item" : "abc", "price" : 10, "quantity" : 10, "date" : ISODate("2014-04-04T21:23:13.331Z") }
The following aggregation operation uses the $group stage to group the documents by the item to retrieve the distinct item values:
db.sales.aggregate( [ { $group : { _id : "$item" } } ] )
The operation returns the following result:
{ "_id" : "xyz" }
{ "_id" : "jkl" }
{ "_id" : "abc" }
Pivot Data¶
A collection books contains the following documents:
{ "_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 uses the $$ROOT system variable to group the documents by authors. The resulting documents must not exceed the BSON Document Size limit.
db.books.aggregate(
[
{ $group : { _id : "$author", books: { $push: "$$ROOT" } } }
]
)
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 }
]
}
{
"_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 }
]
}
See also
The Aggregation with the Zip Code Data Set tutorial provides an extensive example of the $group operator in a common use case.