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Aggregation Pipeline Stages

$documents (expression operator)

Definition

$documents

New in version 6.0.

Returns literal documents from input values.

Syntax

The $documents stage has the following form:

{ $documents: <expression> }

Limitations

  • You can only use $documents in a database-level aggregation pipeline.

  • You must use $documents as the first stage of an aggregation pipeline.

See below for usage examples.

Behavior

$documents accepts any valid expression that resolves to an array of objects. This includes:

  • system variables, such as $$NOW or $$SEARCH_META

  • $let expressions

  • variables in scope from $lookup expressions

Expressions that do not resolve to a current document, like $myField or $$ROOT, will result in an error.

Examples

Test a Pipeline Stage

Create testing and debugging data for a pipeline stage without creating test collections.

db.aggregate(
   [
      { $documents: [ { x: 10 }, { x: 2 }, { x: 5 } ] },
      { $bucketAuto: { groupBy: "$x", buckets: 4 } }
   ]
)

The aggregation expression does not specify a collection. It uses the input data in the highlighted $documents stage as input to the $bucketAuto stage.

[
  { _id: { min: 2, max: 5 }, count: 1 },
  { _id: { min: 5, max: 10 }, count: 1 },
  { _id: { min: 10, max: 10 }, count: 1 }
]

Use a $documents Stage in a $lookup Stage

Correlate documents in a collection with other data using $documents to modify $lookup output.

Create the locations collection.

db.locations.insertMany(
   [
      { zip: 94301, name: "Palo Alto" },
      { zip: 10019, name: "New York" }
   ]
 )

Use $documents as a data source to transform the documents.

db.locations.aggregate(
   [
      { $match: {} },
      { $lookup:
         {
            localField: "zip",
            foreignField: "zip_id",
            as: "city_state",
            pipeline:
              [
                 { $documents:
                    [
                       { zip_id: 94301, name: "Palo Alto, CA" },
                       { zip_id: 10019, name: "New York, NY" }
                    ]
                 }
              ]
         }
      }
   ]
 )

The output correlates the data in the locations collection with the values in the $documents pipeline stage.

[
   {
      _id: ObjectId("618949d60f7bfd5f5689490d"),
      zip: 94301,
      name: 'Palo Alto',
      city_state: [ { zip_id: 94301, name: 'Palo Alto, CA' } ]
   },
   {
      _id: ObjectId("618949d60f7bfd5f5689490e"),
      zip: 10019,
      name: 'New York',
      city_state: [ { zip_id: 10019, name: 'New York, NY' } ]
   }
]

  • The zip field corresponds to the zip_id field

  • The as parameter creates a new output field

To use the MongoDB .NET/C# driver to add a $documents stage to an aggregation pipeline, call the Documents() method on a PipelineDefinition object.

The following example creates a pipeline stage that creates three documents:

var documentArray = new[]
{
    new BsonDocument {{ "title", "The Shawshank Redemption" }},
    new BsonDocument {{ "title", "Back to the Future" }},
    new BsonDocument {{ "title", "Jurassic Park" }},
};
var pipeline = new EmptyPipelineDefinition<NoPipelineInput>()
    .Documents(documentArray);

To use the MongoDB Node.js driver to add a $documents stage to an aggregation pipeline, use the $documents operator in a pipeline object.

The following example creates a pipeline stage that creates three documents. The example then runs the aggregation pipeline:

const pipeline = [
  {
    $documents: [
      { "title": "The Shawshank Redemption" },
      { "title": "Back to the Future" },
      { "title": "Jurassic Park" }
    ]
  }
];
const cursor = collection.aggregate(pipeline);
return cursor;

Learn More

For details on subqueries using the $lookup syntax, see Correlated Subqueries Using Concise Syntax.

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