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Aggregation Pipeline Stages

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Aggregation Pipeline Stages

$group (expression operator)

This version of the documentation is archived and no longer supported. To upgrade your 6.0 deployment, see the MongoDB 7.0 upgrade procedures.

Definition

$group

The $group stage combines multiple documents with the same field, fields, or expression into a single document according to a group key. The result is one document per 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.

Compatibility

You can use $group for deployments hosted in the following environments:

MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB

Syntax

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

Performance

$group is a blocking stage, which causes the pipeline to wait for all input data to be retrieved for the blocking stage before processing the data. A blocking stage may reduce performance because it reduces parallel processing for a pipeline with multiple stages. A blocking stage may also use substantial amounts of memory for large data sets.

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 a value from the first document for each group. Order is only defined if the documents are sorted. Distinct from the `$first` array operator. Changed in version 5.0: Available in the `$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.
`$last`	Returns a value from the last document for each group. Order is only defined if the documents are sorted. Distinct from the `$last` array operator. 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.
`$minN`	Returns an aggregation of the `n` minimum valued elements in a group. Distinct from the `$minN` array operator. New in version 5.2. Available in `$group`, `$setWindowFields` and as an expression.
`$push`	Returns an array of expression values for documents in each group. Changed in version 5.0: Available in the `$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

If the $group stage exceeds 100 megabytes of RAM, MongoDB writes data to temporary files. However, if the allowDiskUse option is set to false, $group returns an error. For more information, refer to Aggregation Pipeline Limits.

`$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 Document of Each Group

If a pipeline sorts and groups by the same field and the $group stage only uses the $first 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 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

Note

Starting in version 6.0.18, the slot-based execution query engine is no longer enabled by default for patch versions of 6.0. If you want your queries to use the slot-based execution query engine, please upgrade to version 8.0, where it is enabled by default.

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" : Decimal128("10"), "quantity" : Int32("2"), "date" : ISODate("2014-03-01T08:00:00Z") },
 { "_id" : 2, "item" : "jkl", "price" : Decimal128("20"), "quantity" : Int32("1"), "date" : ISODate("2014-03-01T09:00:00Z") },
 { "_id" : 3, "item" : "xyz", "price" : Decimal128("5"), "quantity" : Int32( "10"), "date" : ISODate("2014-03-15T09:00:00Z") },
 { "_id" : 4, "item" : "xyz", "price" : Decimal128("5"), "quantity" : Int32("20") , "date" : ISODate("2014-04-04T11:21:39.736Z") },
 { "_id" : 5, "item" : "abc", "price" : Decimal128("10"), "quantity" : Int32("10") , "date" : ISODate("2014-04-04T21:23:13.331Z") },
 { "_id" : 6, "item" : "def", "price" : Decimal128("7.5"), "quantity": Int32("5" ) , "date" : ISODate("2015-06-04T05:08:13Z") },
 { "_id" : 7, "item" : "def", "price" : Decimal128("7.5"), "quantity": Int32("10") , "date" : ISODate("2015-09-10T08:43:00Z") },
 { "_id" : 8, "item" : "abc", "price" : Decimal128("10"), "quantity" : Int32("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" }

Note

When you use $group to retrieve distinct values in a sharded collection, if the operation results in a DISTINCT_SCAN, the result might contain orphaned documents.

The only semantically correct pipeline that is impacted is effectively a logical equivalent of a distinct command, where there is a $group stage at or near the beginning of the pipeline and the $group is not preceded by a $sort stage.

For example, $group operations of the following form can result in a DISTINCT_SCAN:

{ $group : { _id : "$<field>" } }

For more information on behavior for retrieving distinct values, see the distinct command behavior.

To see whether your operation results in a DISTINCT_SCAN, check your operation's explain results.

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" : Decimal128("170") }
{ "_id" : "xyz", "totalSaleAmount" : Decimal128("150") }
{ "_id" : "def", "totalSaleAmount" : Decimal128("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

$match

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" : Decimal128("10"), "quantity" : Int32("2"), "date" : ISODate("2014-03-01T08:00:00Z") },
 { "_id" : 2, "item" : "jkl", "price" : Decimal128("20"), "quantity" : Int32("1"), "date" : ISODate("2014-03-01T09:00:00Z") },
 { "_id" : 3, "item" : "xyz", "price" : Decimal128("5"), "quantity" : Int32( "10"), "date" : ISODate("2014-03-15T09:00:00Z") },
 { "_id" : 4, "item" : "xyz", "price" : Decimal128("5"), "quantity" : Int32("20") , "date" : ISODate("2014-04-04T11:21:39.736Z") },
 { "_id" : 5, "item" : "abc", "price" : Decimal128("10"), "quantity" : Int32("10") , "date" : ISODate("2014-04-04T21:23:13.331Z") },
 { "_id" : 6, "item" : "def", "price" : Decimal128("7.5"), "quantity": Int32("5" ) , "date" : ISODate("2015-06-04T05:08:13Z") },
 { "_id" : 7, "item" : "def", "price" : Decimal128("7.5"), "quantity": Int32("10") , "date" : ISODate("2015-09-10T08:43:00Z") },
 { "_id" : 8, "item" : "abc", "price" : Decimal128("10"), "quantity" : Int32("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" : Decimal128("200"),
 "averageQuantity" : 15, "count" : 2
}
{
 "_id" : "2014-03-15",
 "totalSaleAmount" : Decimal128("50"),
 "averageQuantity" : 10, "count" : 1
}
{
 "_id" : "2014-03-01",
 "totalSaleAmount" : Decimal128("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

$match
$sort
db.collection.countDocuments() which wraps the $group aggregation stage with a $sum expression.

Group by `null`

The following aggregation operation specifies a group _id of null, calculating the total sale amount, average quantity, and count of all documents in the collection.

db.sales.aggregate([
 {
 $group : {
 _id : null,
 totalSaleAmount: { $sum: { $multiply: [ "$price", "$quantity" ] } },
 averageQuantity: { $avg: "$quantity" },
 count: { $sum: 1 }
 }
 }
 ])

The operation returns the following result:

{
 "_id" : null,
 "totalSaleAmount" : Decimal128("452.5"),
 "averageQuantity" : 7.875,
 "count" : 8
}

This aggregation operation is equivalent to the following SQL statement:

SELECT Sum(price * quantity) AS totalSaleAmount,
 Avg(quantity) AS averageQuantity,
 Count(*) AS Count
FROM sales

Tip

$count
db.collection.countDocuments() which wraps the $group aggregation stage with a $sum expression.

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 mebibytes.

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
}

The C# examples on this page use the sample_mflix database from the Atlas sample datasets. To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see Get Started in the MongoDB .NET/C# Driver documentation.

The following Movie class models the documents in the sample_mflix.movies collection:

public class Movie
{
 public ObjectId Id { get; set; }
 public int Runtime { get; set; }
 
 public string Title { get; set; }
 public string Rated { get; set; }
 public List<string> Genres { get; set; }
 public string Plot { get; set; }
 
 public ImdbData Imdb { get; set; }
 public int Year { get; set; }
 public int Index { get; set; }
 
 public string[] Comments { get; set; }
 
 [BsonElement("lastupdated")]
 public DateTime LastUpdated { get; set; }
}

Note

ConventionPack for Pascal Case

The C# classes on this page use Pascal case for their property names, but the field names in the MongoDB collection use camel case. To account for this difference, you can use the following code to register a ConventionPack when your application starts:

var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);

To use the MongoDB .NET/C# driver to add a $group stage to an aggregation pipeline, call the Group() method on a PipelineDefinition object.

The following example creates a pipeline stage that groups documents by the value of their Rated field. Each group's rating is shown in a field named Rating in each output document. Each output document also contains a field named TotalRuntime, whose value is the total runtime of all movies in the group.

var pipeline = new EmptyPipelineDefinition<Movie>()
 .Group(
 id: m => m.Rated,
 group: g => new
 {
 Rating = g.Key,
 TotalRuntime = g.Sum(m => m.Runtime)
 }
 );

The Node.js examples on this page use the sample_mflix database from the Atlas sample datasets. To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see Get Started in the MongoDB Node.js driver documentation.

To use the MongoDB Node.js driver to add a $group stage to an aggregation pipeline, use the $group operator in a pipeline object.

The following example creates a pipeline stage that groups documents by the value of their rated field. Each output document contains a rating field that stores each group's rating. Each output document also contains a field named totalRuntime that stores the total runtime of all movies in the group. The example then runs the aggregation pipeline:

const pipeline = [
 {
 $group: {
 _id: "$rated",
 rating: { $first: "$rated" },
 totalRuntime: { $sum: "$runtime" }
 }
 }
];
const cursor = collection.aggregate(pipeline);
return cursor;

Learn More

The Group and Total Data tutorial provides an extensive example of the $group operator in a common use case.

To learn more about related pipeline stages, see the $addFields guide.

Back

$graphLookup

$indexStats

Definition

Note

Compatibility

Syntax

Considerations

Performance

Accumulator Operator

$group and Memory Restrictions

$group Performance Optimizations

Optimization to Return the First Document of Each Group

Example

Slot-Based Query Execution Engine

Note

Examples

Count the Number of Documents in a Collection

Tip

Retrieve Distinct Values

Note

Group by Item Having

Tip

Calculate Count, Sum, and Average

Group by Day of the Year

Tip

Group by null

Tip

Pivot Data

Group title by author

Group Documents by author

Note

Note

ConventionPack for Pascal Case

Learn More

Earn a Skill Badge

`$group` and Memory Restrictions

`$group` Performance Optimizations

Group by `null`

Group `title` by `author`

Group Documents by `author`