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Updates with Aggregation Pipeline

On this page

  • Create an Update Aggregation Pipeline in Atlas
  • Access the Aggregation Pipeline Builder.
  • Create an aggregation pipeline to perform updates.
  • Export the aggregation pipeline.
  • Examples
  • updateOne with $set
  • updateMany with $replaceRoot and $set
  • updateMany with $set
  • updateOne with $set
  • updateMany with $addFields
  • Additional Examples

Starting in MongoDB 4.2, you can use the aggregation pipeline for update operations. You can build and execute aggregation pipelines to perform updates in MongoDB Atlas, MongoDB Compass, MongoDB Shell, or Drivers.

With the update operations, the aggregation pipeline can consist of the following stages:

Using the aggregation pipeline allows for a more expressive update statement, such as expressing conditional updates based on current field values or updating one field using the value of another field(s).

You can use the MongoDB Atlas UI to build an aggregation pipeline to perform updates. To create and execute aggregation pipelines in the MongoDB Atlas UI, you must have the Project Data Access Read Only role or higher.

1
1

The main panel and Namespaces on the left side list the collections in the database.

2

Select the collection on the left-hand side or in the main panel. The main panel displays the Find, Indexes, and Aggregation views.

3

When you first open the Aggregation view, Atlas displays an empty aggregation pipeline.

2
1

Select an aggregation stage from the Select drop-down menu in the bottom-left panel.

The toggle to the right of the drop-down menu dictates whether the stage is enabled.

To perform updates with an aggregation, use one of these stages:

2

Fill in your stage with the appropriate values. If Comment Mode is enabled, the pipeline builder provides syntactic guidelines for your selected stage.

As you modify your stage, Atlas updates the preview documents on the right based on the results of the current stage.

For examples of what you might include in your aggregation stage, see the examples on this page.

Add stages as needed. For more information on creating aggregation pipelines in Atlas, refer to Create an Aggregation Pipeline.

3
1

You can find this button at the top of the pipeline builder.

2

In the Export Pipeline To menu, select your desired language.

The My Pipeline pane on the left displays your pipeline in MongoDB Shell syntax. You can copy this directly to execute your pipeline in the MongoDB Shell.

The pane on the right displays your pipeline in the selected language. Select your preferred language.

3

(Optional): Check the Include Import Statements option to include the required import statements for the language selected.

(Optional): Check the Include Driver Syntax option to include Driver-specific code to:

  • Initialize the client

  • Specify the database and collection

  • Perform the aggregation operation

4

Click the Copy button at the top-right of the pipeline to copy the pipeline for the selected language to your clipboard. Paste the copied pipeline into your application.

The following examples demonstrate how to use the aggregation pipeline stages $set, $replaceRoot, and $addFields to perform updates.

Create an example students collection (if the collection does not currently exist, insert operations will create the collection):

db.students.insertMany([
{ _id: 1, test1: 95, test2: 92, test3: 90, modified: new Date("01/05/2020") },
{ _id: 2, test1: 98, test2: 100, test3: 102, modified: new Date("01/05/2020") },
{ _id: 3, test1: 95, test2: 110, modified: new Date("01/04/2020") }
])

To verify, query the collection:

db.students.find()

The following db.collection.updateOne() operation uses an aggregation pipeline to update the document with _id: 3:

db.students.updateOne( { _id: 3 }, [ { $set: { "test3": 98, modified: "$$NOW"} } ] )

Specifically, the pipeline consists of a $set stage which adds the test3 field (and sets its value to 98) to the document and sets the modified field to the current datetime. The operation uses the aggregation variable NOW for the current datetime. To access the variable, prefix with $$ and enclose in quotes.

To verify the update, you can query the collection:

db.students.find().pretty()

Create an example students2 collection (if the collection does not currently exist, insert operations will create the collection):

db.students2.insertMany([
{ "_id" : 1, quiz1: 8, test2: 100, quiz2: 9, modified: new Date("01/05/2020") },
{ "_id" : 2, quiz2: 5, test1: 80, test2: 89, modified: new Date("01/05/2020") },
])

To verify, query the collection:

db.students2.find()

The following db.collection.updateMany() operation uses an aggregation pipeline to standardize the fields for the documents (i.e. documents in the collection should have the same fields) and update the modified field:

db.students2.updateMany( {},
[
{ $replaceRoot: { newRoot:
{ $mergeObjects: [ { quiz1: 0, quiz2: 0, test1: 0, test2: 0 }, "$$ROOT" ] }
} },
{ $set: { modified: "$$NOW"} }
]
)

Specifically, the pipeline consists of:

  • a $replaceRoot stage with a $mergeObjects expression to set default values for the quiz1, quiz2, test1 and test2 fields. The aggregation variable ROOT refers to the current document being modified. To access the variable, prefix with $$ and enclose in quotes. The current document fields will override the default values.

  • a $set stage to update the modified field to the current datetime. The operation uses the aggregation variable NOW for the current datetime. To access the variable, prefix with $$ and enclose in quotes.

To verify the update, you can query the collection:

db.students2.find()

Create an example students3 collection (if the collection does not currently exist, insert operations will create the collection):

db.students3.insert([
{ "_id" : 1, "tests" : [ 95, 92, 90 ], "modified" : ISODate("2019-01-01T00:00:00Z") },
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "modified" : ISODate("2019-01-01T00:00:00Z") },
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "modified" : ISODate("2019-01-01T00:00:00Z") }
]);

To verify, query the collection:

db.students3.find()

The following db.collection.updateMany() operation uses an aggregation pipeline to update the documents with the calculated grade average and letter grade.

db.students3.updateMany(
{ },
[
{ $set: { average : { $trunc: [ { $avg: "$tests" }, 0 ] }, modified: "$$NOW" } },
{ $set: { grade: { $switch: {
branches: [
{ case: { $gte: [ "$average", 90 ] }, then: "A" },
{ case: { $gte: [ "$average", 80 ] }, then: "B" },
{ case: { $gte: [ "$average", 70 ] }, then: "C" },
{ case: { $gte: [ "$average", 60 ] }, then: "D" }
],
default: "F"
} } } }
]
)

Specifically, the pipeline consists of:

  • a $set stage to calculate the truncated average value of the tests array elements and to update the modified field to the current datetime. To calculate the truncated average, the stage uses the $avg and $trunc expressions. The operation uses the aggregation variable NOW for the current datetime. To access the variable, prefix with $$ and enclose in quotes.

  • a $set stage to add the grade field based on the average using the $switch expression.

To verify the update, you can query the collection:

db.students3.find()

Create an example students4 collection (if the collection does not currently exist, insert operations will create the collection):

db.students4.insertMany([
{ "_id" : 1, "quizzes" : [ 4, 6, 7 ] },
{ "_id" : 2, "quizzes" : [ 5 ] },
{ "_id" : 3, "quizzes" : [ 10, 10, 10 ] }
])

To verify, query the collection:

db.students4.find()

The following db.collection.updateOne() operation uses an aggregation pipeline to add quiz scores to the document with _id: 2:

db.students4.updateOne( { _id: 2 },
[ { $set: { quizzes: { $concatArrays: [ "$quizzes", [ 8, 6 ] ] } } } ]
)

To verify the update, query the collection:

db.students4.find()

Create an example temperatures collection that contains temperatures in Celsius (if the collection does not currently exist, insert operations will create the collection):

db.temperatures.insertMany([
{ "_id" : 1, "date" : ISODate("2019-06-23"), "tempsC" : [ 4, 12, 17 ] },
{ "_id" : 2, "date" : ISODate("2019-07-07"), "tempsC" : [ 14, 24, 11 ] },
{ "_id" : 3, "date" : ISODate("2019-10-30"), "tempsC" : [ 18, 6, 8 ] }
])

To verify, query the collection:

db.temperatures.find()

The following db.collection.updateMany() operation uses an aggregation pipeline to update the documents with the corresponding temperatures in Fahrenheit:

db.temperatures.updateMany( { },
[
{ $addFields: { "tempsF": {
$map: {
input: "$tempsC",
as: "celsius",
in: { $add: [ { $multiply: ["$$celsius", 9/5 ] }, 32 ] }
}
} } }
]
)

Specifically, the pipeline consists of an $addFields stage to add a new array field tempsF that contains the temperatures in Fahrenheit. To convert each celsius temperature in the tempsC array to Fahrenheit, the stage uses the $map expression with $add and $multiply expressions.

To verify the update, you can query the collection:

db.temperatures.find()

See also the various update method pages for additional examples:

←  Update DocumentsUpdate Methods →