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db.collection.aggregate()

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  • Definition
  • Compatibility
  • Syntax
  • Behavior
  • Examples
db.collection.aggregate(pipeline, options)

Important

mongosh Method

This page documents a mongosh method. This is not the documentation for database commands or language-specific drivers, such as Node.js.

For the database command, see the aggregate command.

For MongoDB API drivers, refer to the language-specific MongoDB driver documentation.

For the legacy mongo shell documentation, refer to the documentation for the corresponding MongoDB Server release:

mongo shell v4.4

Calculates aggregate values for the data in a collection or a view.

Returns:
  • A cursor for the documents produced by the final stage of the aggregation pipeline.
  • If the pipeline includes the explain option, the query returns a document that provides details on the processing of the aggregation operation.
  • If the pipeline includes the $out or $merge operators, the query returns an empty cursor.

You can use db.collection.aggregate() for deployments hosted in the following environments:

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

The aggregate() method has the following form:

db.collection.aggregate( <pipeline>, <options> )

The aggregate() method takes the following parameters:

Parameter
Type
Description
pipeline
array

A sequence of data aggregation operations or stages. See the aggregation pipeline operators for details.

The method can still accept the pipeline stages as separate arguments instead of as elements in an array; however, if you do not specify the pipeline as an array, you cannot specify the options parameter.

options
document
Optional. Additional options that aggregate() passes to the aggregate command. Available only if you specify the pipeline as an array. To see available options, see AggregateOptions.

If an error occurs, the aggregate() helper throws an exception.

In mongosh, if the cursor returned from the db.collection.aggregate() is not assigned to a variable using the var keyword, then mongosh automatically iterates the cursor up to 20 times. See Iterate a Cursor in mongosh for handling cursors in mongosh.

Cursors returned from aggregation only supports cursor methods that operate on evaluated cursors (i.e. cursors whose first batch has been retrieved), such as the following methods:

Tip

For cursors created inside a session, you cannot call getMore outside the session.

Similarly, for cursors created outside of a session, you cannot call getMore inside a session.

MongoDB drivers and mongosh associate all operations with a server session, with the exception of unacknowledged write operations. For operations not explicitly associated with a session (i.e. using Mongo.startSession()), MongoDB drivers and mongosh create an implicit session and associate it with the operation.

If a session is idle for longer than 30 minutes, the MongoDB server marks that session as expired and may close it at any time. When the MongoDB server closes the session, it also kills any in-progress operations and open cursors associated with the session. This includes cursors configured with noCursorTimeout() or a maxTimeMS() greater than 30 minutes.

For operations that return a cursor, if the cursor may be idle for longer than 30 minutes, issue the operation within an explicit session using Mongo.startSession() and periodically refresh the session using the refreshSessions command. See Session Idle Timeout for more information.

db.collection.aggregate() can be used inside distributed transactions.

However, the following stages are not allowed within transactions:

You also cannot specify the explain option.

  • For cursors created outside of a transaction, you cannot call getMore inside the transaction.

  • For cursors created in a transaction, you cannot call getMore outside the transaction.

Important

In most cases, a distributed transaction incurs a greater performance cost over single document writes, and the availability of distributed transactions should not be a replacement for effective schema design. For many scenarios, the denormalized data model (embedded documents and arrays) will continue to be optimal for your data and use cases. That is, for many scenarios, modeling your data appropriately will minimize the need for distributed transactions.

For additional transactions usage considerations (such as runtime limit and oplog size limit), see also Production Considerations.

For db.collection.aggregate() operation that do not include the $out or $merge stages:

Starting in MongoDB 4.2, if the client that issued db.collection.aggregate() disconnects before the operation completes, MongoDB marks db.collection.aggregate() for termination using killOp.

The following examples use the collection orders that contains the following documents:

db.orders.insertMany( [
{ _id: 1, cust_id: "abc1", ord_date: ISODate("2012-11-02T17:04:11.102Z"), status: "A", amount: 50 },
{ _id: 2, cust_id: "xyz1", ord_date: ISODate("2013-10-01T17:04:11.102Z"), status: "A", amount: 100 },
{ _id: 3, cust_id: "xyz1", ord_date: ISODate("2013-10-12T17:04:11.102Z"), status: "D", amount: 25 },
{ _id: 4, cust_id: "xyz1", ord_date: ISODate("2013-10-11T17:04:11.102Z"), status: "D", amount: 125 },
{ _id: 5, cust_id: "abc1", ord_date: ISODate("2013-11-12T17:04:11.102Z"), status: "A", amount: 25 }
] )

The following aggregation operation selects documents with status equal to "A", groups the matching documents by the cust_id field and calculates the total for each cust_id field from the sum of the amount field, and sorts the results by the total field in descending order:

db.orders.aggregate( [
{ $match: { status: "A" } },
{ $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
{ $sort: { total: -1 } }
] )

The operation returns a cursor with the following documents:

[
{ _id: "xyz1", total: 100 },
{ _id: "abc1", total: 75 }
]

mongosh iterates the returned cursor automatically to print the results. See Iterate a Cursor in mongosh for handling cursors manually in mongosh.

The following example uses db.collection.explain() to view detailed information regarding the execution plan of the aggregation pipeline.

db.orders.explain().aggregate( [
{ $match: { status: "A" } },
{ $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
{ $sort: { total: -1 } }
] )

The operation returns a document that details the processing of the aggregation pipeline. For example, the document may show, among other details, which index, if any, the operation used. [1] If the orders collection is a sharded collection, the document also shows the division of labor between the shards and the merge operation, and for targeted queries, the targeted shards.

Note

The intended readers of the explain output document are humans, and not machines, and the output format is subject to change between releases.

You can view more verbose explain output by passing the executionStats or allPlansExecution explain modes to the db.collection.explain() method.

[1] Index Filters can affect the choice of index used. See Index Filters for details.

Starting in MongoDB 6.0, pipeline stages that require more than 100 megabytes of memory to execute write temporary files to disk by default. These temporary files last for the duration of the pipeline execution and can influence storage space on your instance. In earlier versions of MongoDB, you must pass { allowDiskUse: true } to individual find and aggregate commands to enable this behavior.

Individual find and aggregate commands can override the allowDiskUseByDefault parameter by either:

  • Using { allowDiskUse: true } to allow writing temporary files out to disk when allowDiskUseByDefault is set to false

  • Using { allowDiskUse: false } to prohibit writing temporary files out to disk when allowDiskUseByDefault is set to true

The profiler log messages and diagnostic log messages includes a usedDisk indicator if any aggregation stage wrote data to temporary files due to memory restrictions.

To specify an initial batch size for the cursor, use the following syntax for the cursor option:

cursor: { batchSize: <int> }

For example, the following aggregation operation specifies the initial batch size of 0 for the cursor:

db.orders.aggregate(
[
{ $match: { status: "A" } },
{ $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
{ $sort: { total: -1 } },
{ $limit: 2 }
],
{
cursor: { batchSize: 0 }
}
)

The { cursor: { batchSize: 0 } } document, which specifies the size of the initial batch size, indicates an empty first batch. This batch size is useful for quickly returning a cursor or failure message without doing significant server-side work.

To specify batch size for subsequent getMore operations (after the initial batch), use the batchSize field when running the getMore command.

mongosh iterates the returned cursor automatically to print the results. See Iterate a Cursor in mongosh for handling cursors manually in mongosh.

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

A collection restaurants has the following documents:

db.restaurants.insertMany( [
{ _id: 1, category: "café", status: "A" },
{ _id: 2, category: "cafe", status: "a" },
{ _id: 3, category: "cafE", status: "a" }
] )

The following aggregation operation includes the collation option:

db.restaurants.aggregate(
[ { $match: { status: "A" } }, { $group: { _id: "$category", count: { $sum: 1 } } } ],
{ collation: { locale: "fr", strength: 1 } }
);

Note

If performing an aggregation that involves multiple views, such as with $lookup or $graphLookup, the views must have the same collation.

For descriptions on the collation fields, see Collation Document.

Create a collection food with the following documents:

db.food.insertMany( [
{ _id: 1, category: "cake", type: "chocolate", qty: 10 },
{ _id: 2, category: "cake", type: "ice cream", qty: 25 },
{ _id: 3, category: "pie", type: "boston cream", qty: 20 },
{ _id: 4, category: "pie", type: "blueberry", qty: 15 }
] )

Create the following indexes:

db.food.createIndex( { qty: 1, type: 1 } );
db.food.createIndex( { qty: 1, category: 1 } );

The following aggregation operation includes the hint option to force the usage of the specified index:

db.food.aggregate(
[ { $sort: { qty: 1 }}, { $match: { category: "cake", qty: 10 } }, { $sort: { type: -1 } } ],
{ hint: { qty: 1, category: 1 } }
)

Use the readConcern option to specify the read concern for the operation.

You cannot use the $out or the $merge stage in conjunction with read concern "linearizable". That is, if you specify "linearizable" read concern for db.collection.aggregate(), you cannot include either stages in the pipeline.

The following operation on a replica set specifies a Read Concern of "majority" to read the most recent copy of the data confirmed as having been written to a majority of the nodes.

Note

  • To ensure that a single thread can read its own writes, use "majority" read concern and "majority" write concern against the primary of the replica set.

  • You can specify read concern level "majority" for an aggregation that includes an $out stage.

  • Regardless of the read concern level, the most recent data on a node may not reflect the most recent version of the data in the system.

db.restaurants.aggregate(
[ { $match: { rating: { $lt: 5 } } } ],
{ readConcern: { level: "majority" } }
)

A collection named movies contains documents formatted as such:

db.movies.insertOne(
{
_id: ObjectId("599b3b54b8ffff5d1cd323d8"),
title: "Jaws",
year: 1975,
imdb: "tt0073195"
}
)

The following aggregation operation finds movies created in 1995 and includes the comment option to provide tracking information in the logs, the db.system.profile collection, and db.currentOp.

db.movies.aggregate( [ { $match: { year : 1995 } } ], { comment : "match_all_movies_from_1995" } ).pretty()

On a system with profiling enabled, you can then query the system.profile collection to see all recent similar aggregations, as shown below:

db.system.profile.find( { "command.aggregate": "movies", "command.comment" : "match_all_movies_from_1995" } ).sort( { ts : -1 } ).pretty()

This will return a set of profiler results in the following format:

{
"op" : "command",
"ns" : "video.movies",
"command" : {
"aggregate" : "movies",
"pipeline" : [
{
"$match" : {
"year" : 1995
}
}
],
"comment" : "match_all_movies_from_1995",
"cursor" : {
},
"$db" : "video"
},
...
}

An application can encode any arbitrary information in the comment in order to more easily trace or identify specific operations through the system. For instance, an application might attach a string comment incorporating its process ID, thread ID, client hostname, and the user who issued the command.

New in version 5.0.

To define variables that you can access elsewhere in the command, use the let option.

Note

To filter results using a variable in a pipeline $match stage, you must access the variable within the $expr operator.

Create a collection cakeSales containing sales for cake flavors:

db.cakeSales.insertMany( [
{ _id: 1, flavor: "chocolate", salesTotal: 1580 },
{ _id: 2, flavor: "strawberry", salesTotal: 4350 },
{ _id: 3, flavor: "cherry", salesTotal: 2150 }
] )

The following example:

  • retrieves the cake that has a salesTotal greater than 3000, which is the cake with an _id of 2

  • defines a targetTotal variable in let, which is referenced in $gt as $$targetTotal

db.cakeSales.aggregate(
[
{ $match: {
$expr: { $gt: [ "$salesTotal", "$$targetTotal" ] }
} }
],
{ let: { targetTotal: 3000 } }
)

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