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$unionWith (aggregation stage)

Definition

$unionWith

Changed in version 8.0.

Combines two aggregations into a single result set. $unionWith outputs the combined result set (including duplicates) to the next stage.

The order in which the combined result set documents are output is unspecified.

Syntax

The $unionWith stage has the following syntax:

{ $unionWith: { coll: "<collection>", pipeline: [ <stage1>, ... ] } }

To include all documents from the specified collection without any processing, you can use the simplified form:

{ $unionWith: "<collection>" }  // Include all documents from the specified collection

The $unionWith stage takes a document with the following fields:

Field	Necessity	Description
coll	Required if `pipeline` is omitted. Otherwise optional.	The collection or view whose pipeline results you wish to include in the result set. If you omit the `coll` field, you must specify a `pipeline` field with a first stage of `$documents`.
pipeline	Required if `coll` is omitted. Otherwise optional.	An aggregation pipeline to apply to the input documents. If you specify `coll`, the pipeline applies to the documents in `coll`. If you omit `coll`, the pipeline applies to the documents in the pipeline's `$documents` stage. For an example, see Create a Union with Specified Documents. The pipeline cannot include the `$out` and `$merge` stages. Starting in v6.0, the `pipeline` can contain the MongoDB Search `$search` stage as the first stage inside the pipeline. To learn more, see MongoDB Search Support.

The $unionWith operation would correspond to the following SQL statement:

SELECT *
FROM Collection1
WHERE ...
UNION ALL
SELECT *
FROM Collection2
WHERE ...

Considerations

Duplicate Results

The combined results from the previous stage and the $unionWith stage can include duplicates.

For example, create a suppliers collection and a warehouses collection:

db.suppliers.insertMany([
  { _id: 1, supplier: "Aardvark and Sons", state: "Texas" },
  { _id: 2, supplier: "Bears Run Amok.", state: "Colorado"},
  { _id: 3, supplier: "Squid Mark Inc. ", state: "Rhode Island" },
])

db.warehouses.insertMany([
  { _id: 1, warehouse: "A", region: "West", state: "California" },
  { _id: 2, warehouse: "B", region: "Central", state: "Colorado"},
  { _id: 3, warehouse: "C", region: "East", state: "Florida" },
])

The following aggregation combines the state field projection results from the suppliers and warehouse collections.

db.suppliers.aggregate([
   { $project: { state: 1, _id: 0 } },
   { $unionWith: { coll: "warehouses", pipeline: [ { $project: { state: 1, _id: 0 } } ]} }
])

The result set contains duplicates:

{ "state" : "Texas" }
{ "state" : "Colorado" }
{ "state" : "Rhode Island" }
{ "state" : "California" }
{ "state" : "Colorado" }
{ "state" : "Florida" }

To remove the duplicates, you can include a $group stage to group by the state field:

db.suppliers.aggregate([
   { $project: { state: 1, _id: 0 } },
   { $unionWith: { coll: "warehouses", pipeline: [ { $project: { state: 1, _id: 0 } } ]} },
   { $group: { _id: "$state" } }
])

The result set no longer contains duplicates:

 { "_id" : "California" }
 { "_id" : "Texas" }
 { "_id" : "Florida" }
 { "_id" : "Colorado" }
 { "_id" : "Rhode Island" }

`$unionWith` a Sharded Collection

If the $unionWith stage is part of the pipeline, the $unionWith coll cannot be sharded. For example, in the following aggregation operation, the inventory_q1 collection cannot be sharded:

db.suppliers.aggregate([
   {
      $lookup: {
         from: "warehouses",
         let: { order_item: "$item", order_qty: "$ordered" },
         pipeline: [
            ...
            { $unionWith: { coll: "inventory_q1", pipeline: [ ... ] } },
            ...
         ],
         as: "stockdata"
      }
   }
])

Collation

If the db.collection.aggregate() includes a collation document, that collation is used for the operation, ignoring any other collations.

If the db.collection.aggregate() does not include a collation document, the db.collection.aggregate() method uses the collation for the top-level collection/view on which the db.collection.aggregate() is run:

If the $unionWith coll is a collection, its collation is ignored.
If the $unionWith coll is a view, then its collation must match that of the top-level collection/view. Otherwise, the operation errors.

MongoDB Search Support

Starting in MongoDB 6.0, you can specify the MongoDB Search $search or $searchMeta stage in the $unionWith pipeline to search collections on the Atlas cluster. The $search or the $searchMeta stage must be the first stage inside the $unionWith pipeline.

[{
  "$unionWith": {
    "coll": <collection-name>,
    "pipeline": [{
      "$search": {
        "<operator>": {
          <operator-specification>
        }
      },
      ...
    }]
  }
}]

[{
  "$unionWith": {
    "coll": <collection-name>,
    "pipeline": [{
      "$searchMeta": {
        "<collector>": {
          <collector-specification>
        }
      },
      ...
    }]
  }
}]

To see an example of $unionWith with $search, see the MongoDB Search tutorial Run a MongoDB Search $search Query Using $unionWith.

Restrictions

Restrictions	Description
transactions	An aggregation pipeline cannot use `$unionWith` inside transactions.
Sharded Collection	If the `$unionWith` stage is part of the pipeline, the $unionWith coll cannot be sharded.
`$out`	The $unionWith pipeline cannot include the `$out` stage.
`$merge`	The $unionWith pipeline cannot include the `$merge` stage.

Examples

Create Sales Reports from the Union of Yearly Data Collections

The following examples use the $unionWith stage to combine data and return results from multiple collections. In these examples, each collection contains a year of sales data.

Populate Sample Data

Create a sales_2017 collection with the following documents:

db.sales_2017.insertMany( [
  { store: "General Store", item: "Chocolates", quantity: 150 },
  { store: "ShopMart", item: "Chocolates", quantity: 50 },
  { store: "General Store", item: "Cookies", quantity: 100 },
  { store: "ShopMart", item: "Cookies", quantity: 120 },
  { store: "General Store", item: "Pie", quantity: 10 },
  { store: "ShopMart", item: "Pie", quantity: 5 }
] )

Create a sales_2018 collection with the following documents:

db.sales_2018.insertMany( [
  { store: "General Store", item: "Cheese", quantity: 30 },
  { store: "ShopMart", item: "Cheese", quantity: 50 },
  { store: "General Store", item: "Chocolates", quantity: 125 },
  { store: "ShopMart", item: "Chocolates", quantity: 150 },
  { store: "General Store", item: "Cookies", quantity: 200 },
  { store: "ShopMart", item: "Cookies", quantity: 100 },
  { store: "ShopMart", item: "Nuts", quantity: 100 },
  { store: "General Store", item: "Pie", quantity: 30 },
  { store: "ShopMart", item: "Pie", quantity: 25 }
] )

Create a sales_2019 collection with the following documents:

db.sales_2019.insertMany( [
  { store: "General Store", item: "Cheese", quantity: 50 },
  { store: "ShopMart", item: "Cheese", quantity: 20 },
  { store: "General Store", item: "Chocolates", quantity: 125 },
  { store: "ShopMart", item: "Chocolates", quantity: 150 },
  { store: "General Store", item: "Cookies", quantity: 200 },
  { store: "ShopMart", item: "Cookies", quantity: 100 },
  { store: "General Store", item: "Nuts", quantity: 80 },
  { store: "ShopMart", item: "Nuts", quantity: 30 },
  { store: "General Store", item: "Pie", quantity: 50 },
  { store: "ShopMart", item: "Pie", quantity: 75 }
] )

Create a sales_2020 collection with the following documents:

db.sales_2020.insertMany( [
  { store: "General Store", item: "Cheese", quantity: 100, },
  { store: "ShopMart", item: "Cheese", quantity: 100},
  { store: "General Store", item: "Chocolates", quantity: 200 },
  { store: "ShopMart", item: "Chocolates", quantity: 300 },
  { store: "General Store", item: "Cookies", quantity: 500 },
  { store: "ShopMart", item: "Cookies", quantity: 400 },
  { store: "General Store", item: "Nuts", quantity: 100 },
  { store: "ShopMart", item: "Nuts", quantity: 200 },
  { store: "General Store", item: "Pie", quantity: 100 },
  { store: "ShopMart", item: "Pie", quantity: 100 }
] )

Report 1: All Sales by Year and Stores and Items

The following aggregation creates a yearly sales report that lists all sales by quarter and stores. The pipeline uses $unionWith to combine documents from all four collections:

db.sales_2017.aggregate( [
   { $set: { _id: "2017" } },
   { $unionWith: { coll: "sales_2018", pipeline: [ { $set: { _id: "2018" } } ] } },
   { $unionWith: { coll: "sales_2019", pipeline: [ { $set: { _id: "2019" } } ] } },
   { $unionWith: { coll: "sales_2020", pipeline: [ { $set: { _id: "2020" } } ] } },
   { $sort: { _id: 1, store: 1, item: 1 } }
] )

Specifically, the aggregation pipeline uses:

A $set stage to update the _id field to contain the year.
A sequence of $unionWith stages to combine all documents from the four collections, each also using the $set stage on its documents.
A $sort stage to sort by the _id (the year), the store, and item.

Pipeline output:

{ "_id" : "2017", "store" : "General Store", "item" : "Chocolates", "quantity" : 150 }
{ "_id" : "2017", "store" : "General Store", "item" : "Cookies", "quantity" : 100 }
{ "_id" : "2017", "store" : "General Store", "item" : "Pie", "quantity" : 10 }
{ "_id" : "2017", "store" : "ShopMart", "item" : "Chocolates", "quantity" : 50 }
{ "_id" : "2017", "store" : "ShopMart", "item" : "Cookies", "quantity" : 120 }
{ "_id" : "2017", "store" : "ShopMart", "item" : "Pie", "quantity" : 5 }
{ "_id" : "2018", "store" : "General Store", "item" : "Cheese", "quantity" : 30 }
{ "_id" : "2018", "store" : "General Store", "item" : "Chocolates", "quantity" : 125 }
{ "_id" : "2018", "store" : "General Store", "item" : "Cookies", "quantity" : 200 }
{ "_id" : "2018", "store" : "General Store", "item" : "Pie", "quantity" : 30 }
{ "_id" : "2018", "store" : "ShopMart", "item" : "Cheese", "quantity" : 50 }
{ "_id" : "2018", "store" : "ShopMart", "item" : "Chocolates", "quantity" : 150 }
{ "_id" : "2018", "store" : "ShopMart", "item" : "Cookies", "quantity" : 100 }
{ "_id" : "2018", "store" : "ShopMart", "item" : "Nuts", "quantity" : 100 }
{ "_id" : "2018", "store" : "ShopMart", "item" : "Pie", "quantity" : 25 }
{ "_id" : "2019", "store" : "General Store", "item" : "Cheese", "quantity" : 50 }
{ "_id" : "2019", "store" : "General Store", "item" : "Chocolates", "quantity" : 125 }
{ "_id" : "2019", "store" : "General Store", "item" : "Cookies", "quantity" : 200 }
{ "_id" : "2019", "store" : "General Store", "item" : "Nuts", "quantity" : 80 }
{ "_id" : "2019", "store" : "General Store", "item" : "Pie", "quantity" : 50 }
{ "_id" : "2019", "store" : "ShopMart", "item" : "Cheese", "quantity" : 20 }
{ "_id" : "2019", "store" : "ShopMart", "item" : "Chocolates", "quantity" : 150 }
{ "_id" : "2019", "store" : "ShopMart", "item" : "Cookies", "quantity" : 100 }
{ "_id" : "2019", "store" : "ShopMart", "item" : "Nuts", "quantity" : 30 }
{ "_id" : "2019", "store" : "ShopMart", "item" : "Pie", "quantity" : 75 }
{ "_id" : "2020", "store" : "General Store", "item" : "Cheese", "quantity" : 100 }
{ "_id" : "2020", "store" : "General Store", "item" : "Chocolates", "quantity" : 200 }
{ "_id" : "2020", "store" : "General Store", "item" : "Cookies", "quantity" : 500 }
{ "_id" : "2020", "store" : "General Store", "item" : "Nuts", "quantity" : 100 }
{ "_id" : "2020", "store" : "General Store", "item" : "Pie", "quantity" : 100 }
{ "_id" : "2020", "store" : "ShopMart", "item" : "Cheese", "quantity" : 100 }
{ "_id" : "2020", "store" : "ShopMart", "item" : "Chocolates", "quantity" : 300 }
{ "_id" : "2020", "store" : "ShopMart", "item" : "Cookies", "quantity" : 400 }
{ "_id" : "2020", "store" : "ShopMart", "item" : "Nuts", "quantity" : 200 }
{ "_id" : "2020", "store" : "ShopMart", "item" : "Pie", "quantity" : 100 }

Report 2: Aggregated Sales by Items

The following aggregation creates a sales report that lists the sales quantity per item. The pipeline uses $unionWith to combine documents from all four years:

db.sales_2017.aggregate( [
   { $unionWith: "sales_2018" },
   { $unionWith: "sales_2019" },
   { $unionWith: "sales_2020" },
   { $group: { _id: "$item", total: { $sum: "$quantity" } } },
   { $sort: { total: -1 } }
] )

The sequence of $unionWith stages retrieve documents from the specified collections into the pipeline:
The $group stage groups by the item field and uses $sum to calculate the total sales quantity per item.
The $sort stage orders the documents by descending total.

Pipeline output:

{ "_id" : "Cookies", "total" : 1720 }
{ "_id" : "Chocolates", "total" : 1250 }
{ "_id" : "Nuts", "total" : 510 }
{ "_id" : "Pie", "total" : 395 }
{ "_id" : "Cheese", "total" : 350 }

Create a Union with Specified Documents

You can use $unionWith to perform a union with documents that you specify in the pipeline field. When you specify a $documents stage in the pipeline field, you perform a union with documents that aren't stored in a separate collection.

Create a collection cakeFlavors:

db.cakeFlavors.insertMany( [
   { _id: 1, flavor: "chocolate" },
   { _id: 2, flavor: "strawberry" },
   { _id: 3, flavor: "cherry" }
] )

The following $unionWith operation performs a union with documents specified in the pipeline $documents field:

db.cakeFlavors.aggregate( [
   {
      $unionWith: {
         pipeline: [
            {
               $documents: [
                  { _id: 4, flavor: "orange" },
                  { _id: 5, flavor: "vanilla", price: 20 }
               ]
            }
         ]
      }
   }
] )

Output:

[
   { _id: 1, flavor: 'chocolate' },
   { _id: 2, flavor: 'strawberry' },
   { _id: 3, flavor: 'cherry' },
   { _id: 4, flavor: 'orange' },
   { _id: 5, flavor: 'vanilla', price: 20 }
]

Namespaces in Subpipelines

Starting in MongoDB 8.0, namespaces in subpipelines within $lookup and $unionWith are validated to ensure the correct use of from and coll fields:

For $lookup, omit the from field if you use a subpipeline with a stage which doesn't require a specified collection. For example, a $documents stage.
Similarly, for $unionWith, omit the coll field.

Unchanged behavior:

For a $lookup that starts with a stage for a collection, for example a $match or $collStats subpipeline, you must include the from field and specify the collection.
Similarly, for $unionWith, include the coll field and specify the collection.

The following scenario shows an example.

Create a collection cakeFlavors:

db.cakeFlavors.insertMany( [
   { _id: 1, flavor: "chocolate" },
   { _id: 2, flavor: "strawberry" },
   { _id: 3, flavor: "cherry" }
] )

Starting in MongoDB 8.0, the following example returns an error because it contains an invalid coll field:

db.cakeFlavors.aggregate( [ {
   $unionWith: {
      coll: "cakeFlavors",
      pipeline: [ { $documents: [] } ] }
} ] )

In MongoDB versions before 8.0, the previous example runs.

For an example with a valid coll field, see Duplicate Results.

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 $unionWith stage to an aggregation pipeline, call the UnionWith() method on a PipelineDefinition object.

The following example creates a pipeline stage that combines the incoming documents from the sample_mflix.movies collection with the Movie documents in the sample_mflix.Movies collection:

var firstMovieCollection = client.GetDatabase("sample_mflix").GetCollection<Movie>("movies");
var secondMovieCollection = client.GetDatabase("sample_mflix").GetCollection<Movie>("Movies");
var pipeline = new EmptyPipelineDefinition<Movie>()
    .UnionWith(
        withCollection: secondMovieCollection,
        withPipeline: new EmptyPipelineDefinition<Movie>());
var allMovieDocuments = firstMovieCollection.Aggregate(pipeline);

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 $unionWith stage to an aggregation pipeline, use the $unionWith operator in a pipeline object.

The following example creates a pipeline stage that combines the incoming documents from the sample_mflix.movies collection with the movie documents in the sample_mflix.Movies collection. The example then runs the aggregation pipeline:

const db = client.db("sample_mflix");
const collection = db.collection("movies");
const pipeline = [{ $unionWith: { coll: "Movies" } }];
const cursor = collection.aggregate(pipeline);
return cursor;

Back

$sortByCount

$unset

Definition

Syntax

Considerations

Duplicate Results

$unionWith a Sharded Collection

Collation

MongoDB Search Support

Restrictions

Examples

Create Sales Reports from the Union of Yearly Data Collections

Populate Sample Data

Report 1: All Sales by Year and Stores and Items

Report 2: Aggregated Sales by Items

Create a Union with Specified Documents

Namespaces in Subpipelines

Note

ConventionPack for Pascal Case

`$unionWith` a Sharded Collection