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Aggregation Pipeline Stages

$lookup (aggregation)

On this page

  • Definition
  • Compatibility
  • Syntax
  • Considerations
  • Examples

Definition

$lookup

Changed in version 5.0.

Performs a left outer join to an unsharded collection in the same database to filter in documents from the "joined" collection for processing. 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.

Compatibility

You can use $lookup for deployments hosted in the following environments:

  • MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

  • MongoDB Enterprise: The subscription-based, self-managed version of MongoDB

  • MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB

Syntax

The $lookup stage has the following syntaxes:

Equality Match with a Single Join Condition

To perform an equality match between a field from the input documents with a field from the documents of the "joined" collection, the $lookup stage has this 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 these fields:

Field
Description

Specifies the collection in the same database to perform the join with. The from collection cannot be sharded. For details, see Sharded Collection Restrictions.

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.

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.

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.

The operation corresponds to this pseudo-SQL statement:

SELECT *, (
   SELECT ARRAY_AGG(*)
   FROM <collection to join>
   WHERE <foreignField> = <collection.localField>
) AS <output array field>
FROM collection;

Note

The SQL statements on this page are included for comparison to the MongoDB aggregation pipeline syntax. The SQL statements aren't runnable.

For MongoDB examples, see these pages:

Join Conditions and Subqueries on a Joined Collection

MongoDB supports:

  • Executing a pipeline on a joined collection.

  • Multiple join conditions.

  • Correlated and uncorrelated subqueries.

In MongoDB, a correlated subquery is a pipeline in a $lookup stage that references document fields from a joined collection. An uncorrelated subquery does not reference joined fields.

Note

Starting in MongoDB 5.0, for an uncorrelated subquery in a $lookup pipeline stage containing a $sample stage, the $sampleRate operator, or the $rand operator, the subquery is always run again if repeated. Previously, depending on the subquery output size, either the subquery output was cached or the subquery was run again.

MongoDB correlated subqueries are comparable to SQL correlated subqueries, where the inner query references outer query values. An SQL uncorrelated subquery does not reference outer query values.

MongoDB 5.0 also supports concise correlated subqueries.

To perform correlated and uncorrelated subqueries with two collections, and perform other join conditions besides a single equality match, use this $lookup syntax:

{
   $lookup:
      {
         from: <joined collection>,
         let: { <var_1>: <expression>, …, <var_n>: <expression> },
         pipeline: [ <pipeline to run on joined collection> ],
         as: <output array field>
      }
}

The $lookup stage accepts a document with these fields:

Field
Description

Specifies the collection in the same database to perform the join operation. The joined collection cannot be sharded (see Sharded Collection Restrictions).

Optional. Specifies variables to use in the pipeline stages. Use the variable expressions to access the fields from the joined collection's documents that are input to the pipeline.

To reference variables in pipeline stages, use the "$$<variable>" syntax.

The let variables can be accessed by the stages in the pipeline, including additional $lookup stages nested in the pipeline.

  • A $match stage requires the use of an $expr operator to access the variables. The $expr operator allows the use of aggregation expressions inside of the $match syntax.

    The $eq, $lt, $lte, $gt, and $gte comparison operators placed in an $expr operator can use an index on the from collection referenced in a $lookup stage. Limitations:

    • Indexes can only be used for comparisons between fields and constants, so the let operand must resolve to a constant.

      For example, a comparison between $a and a constant value can use an index, but a comparison between $a and $b cannot.

    • Indexes are not used for comparisons where the let operand resolves to an empty or missing value.

    • Multikey indexes are not used.

  • Other (non-$match) stages in the pipeline do not require an $expr operator to access the variables.

Specifies the pipeline to run on the joined collection. The pipeline determines the resulting documents from the joined collection. To return all documents, specify an empty pipeline [].

The pipeline cannot include the $out stage or the $merge stage.

The pipeline cannot directly access the joined document fields. Instead, define variables for the joined document fields using the let option and then reference the variables in the pipeline stages.

To reference variables in pipeline stages, use the "$$<variable>" syntax.

The let variables can be accessed by the stages in the pipeline, including additional $lookup stages nested in the pipeline.

  • A $match stage requires the use of an $expr operator to access the variables. The $expr operator allows the use of aggregation expressions inside of the $match syntax.

    The $eq, $lt, $lte, $gt, and $gte comparison operators placed in an $expr operator can use an index on the from collection referenced in a $lookup stage. Limitations:

    • Indexes can only be used for comparisons between fields and constants, so the let operand must resolve to a constant.

      For example, a comparison between $a and a constant value can use an index, but a comparison between $a and $b cannot.

    • Indexes are not used for comparisons where the let operand resolves to an empty or missing value.

    • Multikey indexes are not used.

  • Other (non-$match) stages in the pipeline do not require an $expr operator to access the variables.

Specifies the name of the new array field to add to the joined documents. The new array field contains the matching documents from the joined collection. If the specified name already exists in the joined document, the existing field is overwritten.

The operation corresponds to this pseudo-SQL statement:

SELECT *, <output array field>
FROM collection
WHERE <output array field> IN (
   SELECT <documents as determined from the pipeline>
   FROM <collection to join>
   WHERE <pipeline>
);

See the following examples:

Correlated Subqueries Using Concise Syntax

New in version 5.0.

Starting in MongoDB 5.0, you can use a concise syntax for a correlated subquery. Correlated subqueries reference document fields from a joined "foreign" collection and the "local" collection on which the aggregate() method was run.

The following new concise syntax removes the requirement for an equality match on the foreign and local fields inside of an $expr operator:

{
   $lookup:
      {
         from: <foreign collection>,
         localField: <field from local collection's documents>,
         foreignField: <field from foreign collection's documents>,
         let: { <var_1>: <expression>, …, <var_n>: <expression> },
         pipeline: [ <pipeline to run> ],
         as: <output array field>
      }
}

The $lookup accepts a document with these fields:

Field
Description

Specifies the foreign collection in the same database to join to the local collection. The foreign collection cannot be sharded (see Sharded Collection Restrictions).

Specifies the local documents' localField to perform an equality match with the foreign documents' foreignField.

If a local document does not contain a localField value, the $lookup uses a null value for the match.

Specifies the foreign documents' foreignField to perform an equality match with the local documents' localField.

If a foreign document does not contain a foreignField value, the $lookup uses a null value for the match.

Optional. Specifies the variables to use in the pipeline stages. Use the variable expressions to access the document fields that are input to the pipeline.

To reference variables in pipeline stages, use the "$$<variable>" syntax.

The let variables can be accessed by the stages in the pipeline, including additional $lookup stages nested in the pipeline.

  • A $match stage requires the use of an $expr operator to access the variables. The $expr operator allows the use of aggregation expressions inside of the $match syntax.

    The $eq, $lt, $lte, $gt, and $gte comparison operators placed in an $expr operator can use an index on the from collection referenced in a $lookup stage. Limitations:

    • Indexes can only be used for comparisons between fields and constants, so the let operand must resolve to a constant.

      For example, a comparison between $a and a constant value can use an index, but a comparison between $a and $b cannot.

    • Indexes are not used for comparisons where the let operand resolves to an empty or missing value.

    • Multikey indexes are not used.

  • Other (non-$match) stages in the pipeline do not require an $expr operator to access the variables.

Specifies the pipeline to run on the foreign collection. The pipeline returns documents from the foreign collection. To return all documents, specify an empty pipeline [].

The pipeline cannot include the $out or $merge stages.

The pipeline cannot directly access the document fields. Instead, define variables for the document fields using the let option and then reference the variables in the pipeline stages.

To reference variables in pipeline stages, use the "$$<variable>" syntax.

The let variables can be accessed by the stages in the pipeline, including additional $lookup stages nested in the pipeline.

  • A $match stage requires the use of an $expr operator to access the variables. The $expr operator allows the use of aggregation expressions inside of the $match syntax.

    The $eq, $lt, $lte, $gt, and $gte comparison operators placed in an $expr operator can use an index on the from collection referenced in a $lookup stage. Limitations:

    • Indexes can only be used for comparisons between fields and constants, so the let operand must resolve to a constant.

      For example, a comparison between $a and a constant value can use an index, but a comparison between $a and $b cannot.

    • Indexes are not used for comparisons where the let operand resolves to an empty or missing value.

    • Multikey indexes are not used.

  • Other (non-$match) stages in the pipeline do not require an $expr operator to access the variables.

Specifies the name of the new array field to add to the foreign documents. The new array field contains the matching documents from the foreign collection. If the specified name already exists in the foreign document, the existing field is overwritten.

The operation corresponds to this pseudo-SQL statement:

SELECT *, <output array field>
FROM localCollection
WHERE <output array field> IN (
   SELECT <documents as determined from the pipeline>
   FROM <foreignCollection>
   WHERE <foreignCollection.foreignField> = <localCollection.localField>
   AND <pipeline match condition>
);

See this example:

Considerations

Views and Collation

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

Restrictions

You cannot include the $out or the $merge stage in the $lookup stage. That is, when specifying a pipeline for the joined collection, you cannot include either stage in the pipeline field.

{
   $lookup:
   {
      from: <collection to join>,
      let: { <var_1>: <expression>, …, <var_n>: <expression> },
      pipeline: [ <pipeline to execute on the joined collection> ],  // Cannot include $out or $merge
      as: <output array field>
   }
}

Sharded Collection Restrictions

In the $lookup stage, the from collection cannot be sharded. However, the collection on which you run the aggregate() method can be sharded. Specifically, in this example:

db.collection.aggregate([
   { $lookup: { from: "fromCollection", ... } }
])

  • The collection can be sharded.

  • The fromCollection cannot be sharded.

To join a sharded collection with an unsharded collection, run the aggregation on the sharded collection and lookup the unsharded collection. For example:

db.shardedCollection.aggregate([
   { $lookup: { from: "unshardedCollection", ... } }
])

Alternatively, or to join multiple sharded collections, consider:

  • Modifying client applications to perform manual lookups instead of using the $lookup aggregation stage.

  • If possible, using an embedded data model that removes the need to join collections.

  • If possible, using an Atlas Data Lake $lookup pipeline stage to lookup a sharded collection.

Performance Considerations

$lookup performance depends on the type of operation performed. Refer to the following table for performance considerations for different $lookup operations.

$lookup Operation
Performance Considerations

  • $lookup operations that perform equality matches with a single join perform better when the foreign collection contains an index on the foreignField.

    IMPORTANT: If a supporting index on the foreignField does not exist, a $lookup operation that performs an equality match with a single join will likely have poor performance.

  • $lookup operations that contain uncorrelated subqueries perform better when the inner pipeline can reference an index of the foreign collection.

  • MongoDB only needs to run the $lookup subquery once before caching the query because there is no relationship between the source and foreign collections. The subquery is not based on any value in the source collection. This behavior improves performance for subsequent executions of the $lookup operation.

  • $lookup operations that contain correlated subqueries perform better when the following conditions apply:

    • The foreign collection contains an index on the foreignField.

    • The foreign collection contains an index that references the inner pipline.

  • If your pipeline passes a large number of documents to the $lookup query, the following strategies may improve performance:

    • Reduce the number of documents that MongoDB passes to the $lookup query. For example, set a stricter filter during the $match stage.

    • Run the inner pipeline of the $lookup subquery as a separate query and use $out to create a temporary collection. Then, run an equality match with a single join.

    • Reconsider the data's schema to ensure it is optimal for the use case.

For general performance strategies, see Indexing Strategies and Query Optimization.

Important

Excessive use of $lookup within a query may slow down performance. To avoid multiple $lookup stages, consider an embedded data model to optimize query performance.

Examples

Perform a Single Equality Join with $lookup

Create a collection orders with these documents:

db.orders.insertMany( [
   { "_id" : 1, "item" : "almonds", "price" : 12, "quantity" : 2 },
   { "_id" : 2, "item" : "pecans", "price" : 20, "quantity" : 1 },
   { "_id" : 3  }
] )

Create another collection inventory with these documents:

db.inventory.insertMany( [
   { "_id" : 1, "sku" : "almonds", "description": "product 1", "instock" : 120 },
   { "_id" : 2, "sku" : "bread", "description": "product 2", "instock" : 80 },
   { "_id" : 3, "sku" : "cashews", "description": "product 3", "instock" : 60 },
   { "_id" : 4, "sku" : "pecans", "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 these documents:

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

The operation corresponds to this pseudo-SQL statement:

SELECT *, inventory_docs
FROM orders
WHERE inventory_docs IN (
   SELECT *
   FROM inventory
   WHERE sku = orders.item
);

For more information, see Equality Match Performance Considerations.

Use $lookup with an Array

If the localField is an array, you can match the array elements against a scalar foreignField without an $unwind stage.

For example, create an example collection classes with these documents:

db.classes.insertMany( [
   { _id: 1, title: "Reading is ...", enrollmentlist: [ "giraffe2", "pandabear", "artie" ], days: ["M", "W", "F"] },
   { _id: 2, title: "But Writing ...", enrollmentlist: [ "giraffe1", "artie" ], days: ["T", "F"] }
] )

Create another collection members with these documents:

db.members.insertMany( [
   { _id: 1, name: "artie", joined: new Date("2016-05-01"), status: "A" },
   { _id: 2, name: "giraffe", joined: new Date("2017-05-01"), status: "D" },
   { _id: 3, name: "giraffe1", joined: new Date("2017-10-01"), status: "A" },
   { _id: 4, name: "panda", joined: new Date("2018-10-11"), status: "A" },
   { _id: 5, name: "pandabear", joined: new Date("2018-12-01"), status: "A" },
   { _id: 6, name: "giraffe2", joined: new Date("2018-12-01"), status: "D" }
] )

The following aggregation operation joins documents in the classes collection with the members collection, matching on the enrollmentlist field to the name field:

db.classes.aggregate( [
   {
      $lookup:
         {
            from: "members",
            localField: "enrollmentlist",
            foreignField: "name",
            as: "enrollee_info"
        }
   }
] )

The operation returns the following:

{
   "_id" : 1,
   "title" : "Reading is ...",
   "enrollmentlist" : [ "giraffe2", "pandabear", "artie" ],
   "days" : [ "M", "W", "F" ],
   "enrollee_info" : [
      { "_id" : 1, "name" : "artie", "joined" : ISODate("2016-05-01T00:00:00Z"), "status" : "A" },
      { "_id" : 5, "name" : "pandabear", "joined" : ISODate("2018-12-01T00:00:00Z"), "status" : "A" },
      { "_id" : 6, "name" : "giraffe2", "joined" : ISODate("2018-12-01T00:00:00Z"), "status" : "D" }
   ]
}
{
   "_id" : 2,
   "title" : "But Writing ...",
   "enrollmentlist" : [ "giraffe1", "artie" ],
   "days" : [ "T", "F" ],
   "enrollee_info" : [
      { "_id" : 1, "name" : "artie", "joined" : ISODate("2016-05-01T00:00:00Z"), "status" : "A" },
      { "_id" : 3, "name" : "giraffe1", "joined" : ISODate("2017-10-01T00:00:00Z"), "status" : "A" }
   ]
}

Use $lookup with $mergeObjects

The $mergeObjects operator combines multiple documents into a single document.

Create a collection orders with these documents:

db.orders.insertMany( [
   { "_id" : 1, "item" : "almonds", "price" : 12, "quantity" : 2 },
   { "_id" : 2, "item" : "pecans", "price" : 20, "quantity" : 1 }
] )

Create another collection items with these documents:

db.items.insertMany( [
  { "_id" : 1, "item" : "almonds", description: "almond clusters", "instock" : 120 },
  { "_id" : 2, "item" : "bread", description: "raisin and nut bread", "instock" : 80 },
  { "_id" : 3, "item" : "pecans", description: "candied pecans", "instock" : 60 }
] )

The following operation first uses the $lookup stage to join the two collections by the item fields and then uses $mergeObjects in the $replaceRoot to merge the joined documents from items and orders:

db.orders.aggregate( [
   {
      $lookup: {
         from: "items",
         localField: "item",    // field in the orders collection
         foreignField: "item",  // field in the items collection
         as: "fromItems"
      }
   },
   {
      $replaceRoot: { newRoot: { $mergeObjects: [ { $arrayElemAt: [ "$fromItems", 0 ] }, "$$ROOT" ] } }
   },
   { $project: { fromItems: 0 } }
] )

The operation returns these documents:

{
  _id: 1,
  item: 'almonds',
  description: 'almond clusters',
  instock: 120,
  price: 12,
  quantity: 2
},
{
  _id: 2,
  item: 'pecans',
  description: 'candied pecans',
  instock: 60,
  price: 20,
  quantity: 1
}

Perform Multiple Joins and a Correlated Subquery with $lookup

Pipelines can execute on a joined collection and include multiple join conditions.

A join condition can reference a field in the local collection on which the aggregate() method was run and reference a field in the joined collection. This allows a correlated subquery between the two collections.

MongoDB 5.0 supports concise correlated subqueries.

Create a collection orders with these documents:

db.orders.insertMany( [
  { "_id" : 1, "item" : "almonds", "price" : 12, "ordered" : 2 },
  { "_id" : 2, "item" : "pecans", "price" : 20, "ordered" : 1 },
  { "_id" : 3, "item" : "cookies", "price" : 10, "ordered" : 60 }
] )

Create another collection warehouses with these documents:

db.warehouses.insertMany( [
  { "_id" : 1, "stock_item" : "almonds", warehouse: "A", "instock" : 120 },
  { "_id" : 2, "stock_item" : "pecans", warehouse: "A", "instock" : 80 },
  { "_id" : 3, "stock_item" : "almonds", warehouse: "B", "instock" : 60 },
  { "_id" : 4, "stock_item" : "cookies", warehouse: "B", "instock" : 40 },
  { "_id" : 5, "stock_item" : "cookies", warehouse: "A", "instock" : 80 }
] )

The following example:

  • Uses a correlated subquery with a join on the orders.item and warehouse.stock_item fields.

  • Ensures the quantity of the item in stock can fulfill the ordered quantity.

db.orders.aggregate( [
   {
      $lookup:
         {
           from: "warehouses",
           let: { order_item: "$item", order_qty: "$ordered" },
           pipeline: [
              { $match:
                 { $expr:
                    { $and:
                       [
                         { $eq: [ "$stock_item",  "$$order_item" ] },
                         { $gte: [ "$instock", "$$order_qty" ] }
                       ]
                    }
                 }
              },
              { $project: { stock_item: 0, _id: 0 } }
           ],
           as: "stockdata"
         }
    }
] )

The operation returns these documents:

{
  _id: 1,
  item: 'almonds',
  price: 12,
  ordered: 2,
  stockdata: [
    { warehouse: 'A', instock: 120 },
    { warehouse: 'B', instock: 60 }
  ]
},
{
  _id: 2,
  item: 'pecans',
  price: 20,
  ordered: 1,
  stockdata: [ { warehouse: 'A', instock: 80 } ]
},
{
  _id: 3,
  item: 'cookies',
  price: 10,
  ordered: 60,
  stockdata: [ { warehouse: 'A', instock: 80 } ]
}

The operation corresponds to this pseudo-SQL statement:

SELECT *, stockdata
FROM orders
WHERE stockdata IN (
   SELECT warehouse, instock
   FROM warehouses
   WHERE stock_item = orders.item
   AND instock >= orders.ordered
);

The $eq, $lt, $lte, $gt, and $gte comparison operators placed in an $expr operator can use an index on the from collection referenced in a $lookup stage. Limitations:

  • Indexes can only be used for comparisons between fields and constants, so the let operand must resolve to a constant.

    For example, a comparison between $a and a constant value can use an index, but a comparison between $a and $b cannot.

  • Indexes are not used for comparisons where the let operand resolves to an empty or missing value.

  • Multikey indexes are not used.

For example, if the index { stock_item: 1, instock: 1 } exists on the warehouses collection:

  • The equality match on the warehouses.stock_item field uses the index.

  • The range part of the query on the warehouses.instock field also uses the indexed field in the compound index.

Tip

See also:

Perform an Uncorrelated Subquery with $lookup

An aggregation pipeline $lookup stage can execute a pipeline on the joined collection, which allows uncorrelated subqueries. An uncorrelated subquery does not reference the joined document fields.

Note

Starting in MongoDB 5.0, for an uncorrelated subquery in a $lookup pipeline stage containing a $sample stage, the $sampleRate operator, or the $rand operator, the subquery is always run again if repeated. Previously, depending on the subquery output size, either the subquery output was cached or the subquery was run again.

Create a collection absences with these documents:

db.absences.insertMany( [
   { "_id" : 1, "student" : "Ann Aardvark", sickdays: [ new Date ("2018-05-01"),new Date ("2018-08-23") ] },
   { "_id" : 2, "student" : "Zoe Zebra", sickdays: [ new Date ("2018-02-01"),new Date ("2018-05-23") ] },
] )

Create another collection holidays with these documents:

db.holidays.insertMany( [
   { "_id" : 1, year: 2018, name: "New Years", date: new Date("2018-01-01") },
   { "_id" : 2, year: 2018, name: "Pi Day", date: new Date("2018-03-14") },
   { "_id" : 3, year: 2018, name: "Ice Cream Day", date: new Date("2018-07-15") },
   { "_id" : 4, year: 2017, name: "New Years", date: new Date("2017-01-01") },
   { "_id" : 5, year: 2017, name: "Ice Cream Day", date: new Date("2017-07-16") }
] )

The following operation joins the absences collection with 2018 holiday information from the holidays collection:

db.absences.aggregate( [
   {
      $lookup:
         {
           from: "holidays",
           pipeline: [
              { $match: { year: 2018 } },
              { $project: { _id: 0, date: { name: "$name", date: "$date" } } },
              { $replaceRoot: { newRoot: "$date" } }
           ],
           as: "holidays"
         }
    }
] )

The operation returns the following:

{
  _id: 1,
  student: 'Ann Aardvark',
  sickdays: [
    ISODate("2018-05-01T00:00:00.000Z"),
    ISODate("2018-08-23T00:00:00.000Z")
  ],
  holidays: [
    { name: 'New Years', date: ISODate("2018-01-01T00:00:00.000Z") },
    { name: 'Pi Day', date: ISODate("2018-03-14T00:00:00.000Z") },
    { name: 'Ice Cream Day', date: ISODate("2018-07-15T00:00:00.000Z")
    }
  ]
},
{
  _id: 2,
  student: 'Zoe Zebra',
  sickdays: [
    ISODate("2018-02-01T00:00:00.000Z"),
    ISODate("2018-05-23T00:00:00.000Z")
  ],
  holidays: [
    { name: 'New Years', date: ISODate("2018-01-01T00:00:00.000Z") },
    { name: 'Pi Day', date: ISODate("2018-03-14T00:00:00.000Z") },
    { name: 'Ice Cream Day', date: ISODate("2018-07-15T00:00:00.000Z")
    }
  ]
}

The operation corresponds to this pseudo-SQL statement:

SELECT *, holidays
FROM absences
WHERE holidays IN (
   SELECT name, date
   FROM holidays
   WHERE year = 2018
);

For more information, see Uncorrelated Subquery Performance Considerations.

Perform a Concise Correlated Subquery with $lookup

New in version 5.0.

Starting in MongoDB 5.0, an aggregation pipeline $lookup stage supports a concise correlated subquery syntax that improves joins between collections. The new concise syntax removes the requirement for an equality match on the foreign and local fields inside of an $expr operator in a $match stage.

Create a collection restaurants:

db.restaurants.insertMany( [
   {
      _id: 1,
      name: "American Steak House",
      food: [ "filet", "sirloin" ],
      beverages: [ "beer", "wine" ]
   },
   {
      _id: 2,
      name: "Honest John Pizza",
      food: [ "cheese pizza", "pepperoni pizza" ],
      beverages: [ "soda" ]
   }
] )

Create another collection orders with food and optional drink orders:

db.orders.insertMany( [
   {
      _id: 1,
      item: "filet",
      restaurant_name: "American Steak House"
   },
   {
      _id: 2,
      item: "cheese pizza",
      restaurant_name: "Honest John Pizza",
      drink: "lemonade"
   },
   {
      _id: 3,
      item: "cheese pizza",
      restaurant_name: "Honest John Pizza",
      drink: "soda"
   }
] )

The following example:

  • Joins the orders and restaurants collections by matching the orders.restaurant_name localField with the restaurants.name foreignField. The match is performed before the pipeline is run.

  • Performs an $in array match between the orders.drink and restaurants.beverages fields that are accessed using $$orders_drink and $beverages respectively.

db.orders.aggregate( [
   {
      $lookup: {
         from: "restaurants",
         localField: "restaurant_name",
         foreignField: "name",
         let: { orders_drink: "$drink" },
         pipeline: [ {
            $match: {
               $expr: { $in: [ "$$orders_drink", "$beverages" ] }
            }
         } ],
         as: "matches"
      }
   }
] )

There is a match for the soda value in the orders.drink and restaurants.beverages fields. This output shows the matches array and contains all joined fields from the restaurants collection for the match:

{
   "_id" : 1, "item" : "filet",
   "restaurant_name" : "American Steak House",
   "matches" : [ ]
}
{
   "_id" : 2, "item" : "cheese pizza",
   "restaurant_name" : "Honest John Pizza",
   "drink" : "lemonade",
   "matches" : [ ]
}
{
   "_id" : 3, "item" : "cheese pizza",
   "restaurant_name" : "Honest John Pizza",
   "drink" : "soda",
   "matches" : [ {
      "_id" : 2, "name" : "Honest John Pizza",
      "food" : [ "cheese pizza", "pepperoni pizza" ],
      "beverages" : [ "soda" ]
   } ]
}

Before the introduction of concise correlated subqueries, you had to use an $eq equality match between the local field and the joined field in the $expr operator in the pipeline $lookup stage as shown in Perform Multiple Joins and a Correlated Subquery with $lookup.

This example uses the older verbose syntax from MongoDB versions before 5.0 and returns the same results as the previous concise example:

db.orders.aggregate( [
   {
      $lookup: {
         from: "restaurants",
         let: { orders_restaurant_name: "$restaurant_name",
                orders_drink: "$drink" },
         pipeline: [ {
            $match: {
               $expr: {
                  $and: [
                     { $eq: [ "$$orders_restaurant_name", "$name" ] },
                     { $in: [ "$$orders_drink", "$beverages" ] }
                  ]
               }
            }
         } ],
         as: "matches"
      }
   }
] )

The previous examples correspond to this pseudo-SQL statement:

SELECT *, matches
FROM orders
WHERE matches IN (
   SELECT *
   FROM restaurants
   WHERE restaurants.name = orders.restaurant_name
   AND restaurants.beverages = orders.drink
);

For more information, see Correlated Subquery Performance Considerations.

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