You are viewing an offline version of MongoDB documentation. Some page features might be unavailable. To view the latest version of the page or use interactive features, visit the live page.

Database Manual

Aggregation Operations

Reference

Stages

$unionWith (aggregation)

Definition

$unionWith

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 Atlas Search `$search` stage as the first stage inside the pipeline. To learn more, see Atlas 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, that collation is used for the operation, ignoring any other collations.

If the db.collection.aggregate() does not include a collation, 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.

Atlas Search Support

Starting in MongoDB 6.0, you can specify the Atlas 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 Atlas Search tutorial Run an Atlas Search $search Query Using $unionWith.

Warning

Queries using $unionWith with $search in the sub-pipeline might fail during an upgrade from a replica set to a sharded cluster running MongoDB v7.0 if the collection is not spread across multiple shards. To mitigate this, deploy more than one shard on your cluster and shard the collection such that multiple chunks are spread across multiple shards.

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 }
]

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 string 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; }
}

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);

Back

$sortByCount

$unset