Aggregation Pipeline

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Complete Aggregation Pipeline Examples

Additional Aggregation Pipeline Stage Details
Aggregation Pipeline Expressions
Run an Aggregation Pipeline
Update Documents Using an Aggregation Pipeline
Other Considerations
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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:
- Total order value using $sum and $multiply.
- Average order quantity using $avg.
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

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.