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Aggregation Operations

Aggregation Pipeline

On this page

  • Complete Aggregation Pipeline Examples
  • Additional Aggregation Pipeline Stage Details
  • Aggregation Pipeline Expressions
  • Run an Aggregation Pipeline
  • Update Documents Using an Aggregation Pipeline
  • Other Considerations
  • Learn More

An aggregation pipeline consists of one or more stages that process documents:

  • Each stage performs an operation on the input documents. For example, a stage can filter documents, group documents, and calculate values.

  • The documents that are output from a stage are passed to the next stage.

  • An aggregation pipeline can return results for groups of documents. For example, return the total, average, maximum, and minimum values.

You can update documents with an aggregation pipeline if you use the stages shown in Updates with Aggregation Pipeline.

Note

Aggregation pipelines run with the db.collection.aggregate() method do not modify documents in a collection, unless the pipeline contains a $merge or $out stage.

You can run aggregation pipelines in the UI for deployments hosted in MongoDB Atlas.

When you run aggregation pipelines on MongoDB Atlas deployments in the MongoDB Atlas UI, you can preview the results at each stage.

Complete Aggregation Pipeline Examples

This section shows aggregation pipeline examples that use the following pizza orders collection:

db.orders.insertMany( [
   { _id: 0, name: "Pepperoni", size: "small", price: 19,
     quantity: 10, date: ISODate( "2021-03-13T08:14:30Z" ) },
   { _id: 1, name: "Pepperoni", size: "medium", price: 20,
     quantity: 20, date : ISODate( "2021-03-13T09:13:24Z" ) },
   { _id: 2, name: "Pepperoni", size: "large", price: 21,
     quantity: 30, date : ISODate( "2021-03-17T09:22:12Z" ) },
   { _id: 3, name: "Cheese", size: "small", price: 12,
     quantity: 15, date : ISODate( "2021-03-13T11:21:39.736Z" ) },
   { _id: 4, name: "Cheese", size: "medium", price: 13,
     quantity:50, date : ISODate( "2022-01-12T21:23:13.331Z" ) },
   { _id: 5, name: "Cheese", size: "large", price: 14,
     quantity: 10, date : ISODate( "2022-01-12T05:08:13Z" ) },
   { _id: 6, name: "Vegan", size: "small", price: 17,
     quantity: 10, date : ISODate( "2021-01-13T05:08:13Z" ) },
   { _id: 7, name: "Vegan", size: "medium", price: 18,
     quantity: 10, date : ISODate( "2021-01-13T05:10:13Z" ) }
] )

Calculate Total Order Quantity

The following aggregation pipeline example contains two stages and returns the total order quantity of medium size pizzas grouped by pizza name:

db.orders.aggregate( [
   // Stage 1: Filter pizza order documents by pizza size
   {
      $match: { size: "medium" }
   },
   // Stage 2: Group remaining documents by pizza name and calculate total quantity
   {
      $group: { _id: "$name", totalQuantity: { $sum: "$quantity" } }
   }
] )

The $match stage:

  • Filters the pizza order documents to pizzas with a size of medium.

  • Passes the remaining documents to the $group stage.

The $group stage:

  • Groups the remaining documents by pizza name.

  • Uses $sum to calculate the total order quantity for each pizza name. The total is stored in the totalQuantity field returned by the aggregation pipeline.

Example output:

[
   { _id: 'Cheese', totalQuantity: 50 },
   { _id: 'Vegan', totalQuantity: 10 },
   { _id: 'Pepperoni', totalQuantity: 20 }
]

Calculate Total Order Value and Average Order Quantity

The following example calculates the total pizza order value and average order quantity between two dates:

db.orders.aggregate( [
   // Stage 1: Filter pizza order documents by date range
   {
      $match:
      {
         "date": { $gte: new ISODate( "2020-01-30" ), $lt: new ISODate( "2022-01-30" ) }
      }
   },
   // Stage 2: Group remaining documents by date and calculate results
   {
      $group:
      {
         _id: { $dateToString: { format: "%Y-%m-%d", date: "$date" } },
         totalOrderValue: { $sum: { $multiply: [ "$price", "$quantity" ] } },
         averageOrderQuantity: { $avg: "$quantity" }
      }
   },
   // Stage 3: Sort documents by totalOrderValue in descending order
   {
      $sort: { totalOrderValue: -1 }
   }
 ] )

The $match stage:

  • Filters the pizza order documents to those in a date range specified using $gte and $lt.

  • Passes the remaining documents to the $group stage.

The $group stage:

  • Groups the documents by date using $dateToString.

  • For each group, calculates:

  • Passes the grouped documents to the $sort stage.

The $sort stage:

  • Sorts the documents by the total order value for each group in descending order (-1).

  • Returns the sorted documents.

Example output:

[
   { _id: '2022-01-12', totalOrderValue: 790, averageOrderQuantity: 30 },
   { _id: '2021-03-13', totalOrderValue: 770, averageOrderQuantity: 15 },
   { _id: '2021-03-17', totalOrderValue: 630, averageOrderQuantity: 30 },
   { _id: '2021-01-13', totalOrderValue: 350, averageOrderQuantity: 10 }
]

Tip

See also:

Additional Aggregation Pipeline Stage Details

An aggregation pipeline consists of one or more stages that process documents:

  • A stage does not have to output one document for every input document. For example, some stages may produce new documents or filter out documents.

  • The same stage can appear multiple times in the pipeline with these stage exceptions: $out, $merge, and $geoNear.

  • To calculate averages and perform other calculations in a stage, use aggregation expressions that specify aggregation operators. You will learn more about aggregation expressions in the next section.

For all aggregation stages, see Aggregation Pipeline Stages.

Aggregation Pipeline Expressions

Some aggregation pipeline stages accept an aggregation expression, which:

You can use the $accumulator and $function aggregation operators to define custom aggregation expressions in JavaScript.

For all aggregation expressions, see Expressions.

Field Paths

Field path expressions are used to access fields in input documents. To specify a field path, prefix the field name or the dotted field path (if the field is in an embedded document) with a dollar sign $. For example, "$user" to specify the field path for the user field or "$user.name" to specify the field path to the embedded "user.name" field.

"$<field>" is equivalent to "$$CURRENT.<field>" where the CURRENT is a system variable that defaults to the root of the current object, unless stated otherwise in specific stages.

For more information and examples, see Field Paths.

Run an Aggregation Pipeline

To run an aggregation pipeline, use:

Update Documents Using an Aggregation Pipeline

To update documents with an aggregation pipeline, use:

Command
mongosh Methods

findAndModify

db.collection.findOneAndUpdate()
db.collection.findAndModify()

update

db.collection.updateOne()
db.collection.updateMany()
Bulk.find.update()
Bulk.find.updateOne()
Bulk.find.upsert()

Other Considerations

Aggregation Pipeline Limitations

An aggregation pipeline has limitations on the value types and the result size. See Aggregation Pipeline Limits.

Aggregation Pipelines and Sharded Collections

An aggregation pipeline supports operations on sharded collections. See Aggregation Pipeline and Sharded Collections.

Aggregation Pipelines as an Alternative to Map-Reduce

Starting in MongoDB 5.0, map-reduce is deprecated:

  • Instead of map-reduce, you should use an aggregation pipeline. Aggregation pipelines provide better performance and usability than map-reduce.

  • You can rewrite map-reduce operations using aggregation pipeline stages, such as $group, $merge, and others.

  • For map-reduce operations that require custom functionality, you can use the $accumulator and $function aggregation operators. You can use those operators to define custom aggregation expressions in JavaScript.

For examples of aggregation pipeline alternatives to map-reduce, see:

Learn More

To learn more about aggregation pipelines, see:

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