$lookup (aggregation)¶

On this page

Definition¶

$lookup¶

New in version 3.2.

Performs a left outer join to an unsharded collection in the same database to filter in documents from the “joined” collection for processing. The $lookup stage does an equality match between a field from the input documents with a field from the documents of the “joined” collection.

To each input document, the $lookup stage adds a new array field whose elements are the matching documents from the “joined” collection. The $lookup stage passes these reshaped documents to the next stage.

The $lookup stage has the following syntax:

{
   $lookup:
     {
       from: <collection to join>,
       localField: <field from the input documents>,
       foreignField: <field from the documents of the "from" collection>,
       as: <output array field>
     }
}

The $lookup takes a document with the following fields:

Field	Description
`from`	Specifies the collection in the same database to perform the join with. The `from` collection cannot be sharded.
`localField`	Specifies the field from the documents input to the `$lookup` stage. `$lookup` performs an equality match on the `localField` to the `foreignField` from the documents of the `from` collection. If an input document does not contain the `localField`, the `$lookup` treats the field as having a value of `null` for matching purposes.
`foreignField`	Specifies the field from the documents in the `from` collection. `$lookup` performs an equality match on the `foreignField` to the `localField` from the input documents. If a document in the `from` collection does not contain the `foreignField`, the `$lookup` treats the value as `null` for matching purposes.
`as`	Specifies the name of the new array field to add to the input documents. The new array field contains the matching documents from the `from` collection. If the specified name already exists in the input document, the existing field is overwritten.

Note

If your localField is an array, you’ll need to add an $unwind stage to your pipeline. See the example on this page.

Consideration¶

Views and Collation¶

If performing an aggregation that involves multiple views, such as with $lookup or $graphLookup, the views must have the same collation.

Examples¶

Perform a Join with `$lookup`¶

A collection orders contains the following documents:

{ "_id" : 1, "item" : "abc", "price" : 12, "quantity" : 2 }
{ "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1 }
{ "_id" : 3  }

Another collection inventory contains the following documents:

{ "_id" : 1, "sku" : "abc", description: "product 1", "instock" : 120 }
{ "_id" : 2, "sku" : "def", description: "product 2", "instock" : 80 }
{ "_id" : 3, "sku" : "ijk", description: "product 3", "instock" : 60 }
{ "_id" : 4, "sku" : "jkl", description: "product 4", "instock" : 70 }
{ "_id" : 5, "sku": null, description: "Incomplete" }
{ "_id" : 6 }

The following aggregation operation on the orders collection joins the documents from orders with the documents from the inventory collection using the fields item from the orders collection and the sku field from the inventory collection:

db.orders.aggregate([
    {
      $lookup:
        {
          from: "inventory",
          localField: "item",
          foreignField: "sku",
          as: "inventory_docs"
        }
   }
])

The operation returns the following documents:

{
  "_id" : 1,
   "item" : "abc",
  "price" : 12,
  "quantity" : 2,
  "inventory_docs" : [
    { "_id" : 1, "sku" : "abc", description: "product 1", "instock" : 120 }
  ]
}
{
  "_id" : 2,
  "item" : "jkl",
  "price" : 20,
  "quantity" : 1,
  "inventory_docs" : [
    { "_id" : 4, "sku" : "jkl", "description" : "product 4", "instock" : 70 }
  ]
}
{
  "_id" : 3,
  "inventory_docs" : [
    { "_id" : 5, "sku" : null, "description" : "Incomplete" },
    { "_id" : 6 }
  ]
}

Use `$lookup` with an Array¶

If your localField is an array and you’d like to match the elements inside it against a foreignField which is a single element, you’ll need to $unwind the array as one stage of the aggregation pipeline.

Consider a collection orders with the following document:

{ "_id" : 1, "item" : "MON1003", "price" : 350, "quantity" : 2, "specs" :
[ "27 inch", "Retina display", "1920x1080" ], "type" : "Monitor" }

Another collection inventory contains the following documents:

{ "_id" : 1, "sku" : "MON1003", "type" : "Monitor", "instock" : 120,
"size" : "27 inch", "resolution" : "1920x1080" }
{ "_id" : 2, "sku" : "MON1012", "type" : "Monitor", "instock" : 85,
"size" : "23 inch", "resolution" : "1280x800" }
{ "_id" : 3, "sku" : "MON1031", "type" : "Monitor", "instock" : 60,
"size" : "23 inch", "display_type" : "LED" }

The following aggregation operation performs a join on documents in the orders collection which match a particular element of the specs array to the size field in the inventory collection.

db.orders.aggregate([
   {
      $unwind: "$specs"
   },
   {
      $lookup:
         {
            from: "inventory",
            localField: "specs",
            foreignField: "size",
            as: "inventory_docs"
        }
   },
   {
      $match: { "inventory_docs": { $ne: [] } }
   }
])

The operation returns the following document:

{
   "_id" : 1,
   "item" : "MON1003",
   "price" : 350,
   "quantity" : 2,
   "specs" : "27 inch",
   "type" : "Monitor",
   "inventory_docs" : [
      {
         "_id" : 1,
         "sku" : "MON1003",
         "type" : "Monitor",
         "instock" : 120,
         "size" : "27 inch",
         "resolution" : "1920x1080"
      }
   ]
}

← $geoNear (aggregation) $out (aggregation) →