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embeddedDocument

On this page

  • Definition
  • Syntax
  • Options
  • Behavior
  • Scoring Behavior
  • Sorting Behavior
  • Highlighting
  • Limitations
  • Examples
  • Index Definition
  • Basic Query
  • Facet Query
  • Query and Sort
  • Query for Matching Embedded Documents Only

Note

The Atlas Search embeddedDocuments type, embeddedDocument operator, and embedded scoring option are in preview. When an Atlas Search index on a replica set or single MongoDB shard reaches 2,100,000,000 index objects, Atlas Search transitions the index to a failed, non-queryable state. A solution to accommodate this limitation will be in place when this feature is generally available. To troubleshoot any issues related to using this feature, contact Support. To request support for more than 2,100,000,000 index objects, upvote this request in the MongoDB Feedback Engine.

Definition

embeddedDocument

The embeddedDocument operator is similar to $elemMatch operator. It constrains multiple query predicates to be satisfied from a single element of an array of embedded documents. embeddedDocument can be used only for queries over fields of the embeddedDocuments type.

Syntax

embeddedDocument has the following syntax:

{
  "embeddedDocument": {
    "path": "<path-to-field>",
    "operator": { <operator-specification> },
    "score": { <score-options> }
  }
}

Options

embeddedDocument uses the following options to construct a query:

Field
Type
Description
Necessity
operator
object
Operator to use to query each document in the array of documents that you specify in the path. The moreLikeThis operator is not supported.
Required
path
string
Indexed embeddedDocuments type field to search. The specified field must be a parent for all operators and fields specified using the operator option. See Path Construction for more information.
Required
score
object
Score to assign to matching search results. You can use the embedded scoring option to configure scoring options. To learn more, see Scoring Behavior.
Optional

Behavior

When you query embedded documents in arrays using the embeddedDocument operator, Atlas Search evaluates and scores the operator query predicates at different stages of query execution. Atlas Search:

  1. Evaluates each embedded document in the array independently.

  2. Combines the scores of matching results as configured using the embedded option, or scores by summing the scores of matching results if you don't specify an embedded score option.

  3. Joins the matching results with the parent document if other query predicates are specified through the compound operator.

    Note

    For string faceting, Atlas Search counts string facets once for each document in the result set. For an example of this behavior, see Examples.

Scoring Behavior

By default, embeddedDocument operator uses the default aggregation strategy, sum, for combining scores of embedded document matches. The embeddedDocument operator score option allows you to override the default and configure the score of matching results using the embedded option.

Tip

See also:

Score the Documents in the Results

Sorting Behavior

To sort the parent documents by an embedded document field, you must do the following:

  • Index the parents of the embedded document child field as the document type.

  • Index the child field with string values within the embedded document as the token type. For child fields with number and date values, enable dynamic mapping to index those fields automatically.

Atlas Search sorts on parent documents only. It doesn't sort the child fields within an array of documents. For an example, see Sort Example.

Highlighting

You can highlight on fields if the fields are indexed under a parent field of document type for query predicates specified inside the embeddedDocument operator. For an example, see tutorial.

Limitations

You can't highlight on queries inside the embeddedDocument operator.

Examples

The following examples use the sample_supplies.sales collection in the sample dataset.

Index Definition

These sample queries use the following index definition on the collection:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "items": [
        {
          "dynamic": true,
          "type": "embeddedDocuments"
        },
        {
          "dynamic": true,
          "fields": {
            "tags": {
              "type": "token"
            }
          },
          "type": "document"
        }
      ],
      "purchaseMethod": {
        "type": "stringFacet"
      }
    }
  }
}

Basic Query

The following query searches the collection for items tagged school with a preference for items named backpack. Atlas Search scores the results in descending order based on the average (arithmetic mean) score of all matching embedded documents. The query includes a $limit stage to limit the output to 5 documents and a $project stage to:

  • Exclude all fields except items.name and items.tags fields

  • Add a field named score

1db.sales.aggregate({
2  "$search": {
3    "embeddedDocument": {
4      "path": "items",
5      "operator": {
6        "compound": {
7          "must": [{
8            "text": {
9              "path": "items.tags",
10              "query": "school"
11            }
12          }],
13          "should": [{
14            "text": {
15              "path": "items.name",
16              "query": "backpack"
17            }
18          }]
19        }
20      },
21      "score": {
22        "embedded": {
23          "aggregate": "mean"
24        }
25      }
26    }
27  }
28},
29{
30  $limit: 5
31},
32{
33  $project: {
34    "_id": 0,
35    "items.name": 1,
36    "items.tags": 1,
37    "score": { $meta: "searchScore" }
38  }
39})
[
  {
    items: [ {
      name: 'backpack',
      tags: [ 'school', 'travel', 'kids' ]
    } ],
    score: 1.2907354831695557
  },
  {
    items: [ {
      name: 'envelopes',
      tags: [ 'stationary', 'office', 'general' ]
    },
    {
      name: 'printer paper',
      tags: [ 'office', 'stationary' ]
    },
    {
      name: 'backpack',
      tags: [ 'school', 'travel', 'kids' ]
    } ],
    score: 1.2907354831695557
  },
  {
    items: [ {
      name: 'backpack',
      tags: [ 'school', 'travel', 'kids' ]
    } ],
    score: 1.2907354831695557
  },
  {
    items: [ {
      name: 'backpack',
      tags: [ 'school', 'travel', 'kids' ]
    } ],
    score: 1.2907354831695557
  },
  {
    items: [ {
      name: 'backpack',
      tags: [ 'school', 'travel', 'kids' ]
    } ],
    score: 1.2907354831695557
  }
]

Facet Query

The following query searches for items tagged school with a preference for items named backpack. It requests facet information on the purchaseMethod field.

1db.sales.aggregate({
2  "$searchMeta": {
3    "facet": {
4      "operator": {
5        "embeddedDocument": {
6          "path": "items",
7          "operator": {
8            "compound": {
9              "must": [
10                {
11                  "text": {
12                    "path": "items.tags",
13                    "query": "school"
14                  }
15                }
16              ],
17              "should": [
18                {
19                  "text": {
20                    "path": "items.name",
21                    "query": "backpack"
22                  }
23                }
24              ]
25            }
26          }
27        }
28      },
29      "facets": {
30        "purchaseMethodFacet": {
31          "type": "string",
32          "path": "purchaseMethod"
33        }
34      }
35    }
36  }
37})
[
  {
    count: { lowerBound: Long("2309") },
    facet: {
      purchaseMethodFacet: {
        buckets: [
          { _id: 'In store', count: Long("2751") },
          { _id: 'Online', count: Long("1535") },
          { _id: 'Phone', count: Long("578") }
        ]
      }
    }
  }
]

Query and Sort

The following query searches for items named laptop and it sorts the results by the items.tags field. The query includes a $limit stage to limit the output to 5 documents and a $project stage to:

  • Exclude all fields except items.name and items.tags

  • Add a field named score

1db.sales.aggregate({
2  "$search": {
3    "embeddedDocument": {
4      "path": "items",
5      "operator": {
6        "text": {
7          "path": "items.name",
8          "query": "laptop"
9        }
10      }
11    },
12    "sort": {
13      "items.tags": 1
14    }
15  }
16},
17{
18  "$limit": 5
19},
20{
21  "$project": {
22    "_id": 0,
23    "items.name": 1,
24    "items.tags": 1,
25    "score": { "$meta": "searchScore" }
26  }
27})
1[
2  {
3    items: [
4      { name: 'envelopes',  tags: [ 'stationary', 'office', 'general' ] },
5      { name: 'binder', tags: [ 'school', 'general', 'organization' ] },
6      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
7      { name: 'laptop', tags: [ 'electronics', 'school', 'office' ] },
8      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
9      { name: 'printer paper', tags: [ 'office', 'stationary' ] },
10      { name: 'backpack', tags: [ 'school', 'travel', 'kids' ] },
11      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
12      { name: 'envelopes', tags: [ 'stationary', 'office', 'general' ] }
13    ],
14    score: 1.168686032295227
15  },
16  {
17    items: [
18      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
19      { name: 'binder', tags: [ 'school', 'general', 'organization' ] },
20      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
21      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
22      { name: 'printer paper', tags: [ 'office', 'stationary' ] },
23      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
24      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
25      { name: 'backpack', tags: [ 'school', 'travel', 'kids' ] },
26      { name: 'laptop', tags: [ 'electronics', 'school', 'office' ] }
27    ],
28    score: 1.168686032295227
29  },
30  {
31    items: [
32      { name: 'backpack', tags: [ 'school', 'travel', 'kids' ] },
33      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
34      { name: 'binder', tags: [ 'school', 'general', 'organization' ] },
35      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
36      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
37      { name: 'envelopes', tags: [ 'stationary', 'office', 'general' ] },
38      { name: 'laptop', tags: [ 'electronics', 'school', 'office' ] }
39    ],
40    score: 1.168686032295227
41  },
42  {
43    items: [
44      { name: 'laptop', tags: [ 'electronics', 'school', 'office' ] },
45      { name: 'binder', tags: [ 'school', 'general', 'organization' ] },
46      { name: 'binder', tags: [ 'school', 'general', 'organization' ] },
47      { name: 'backpack', tags: [ 'school', 'travel', 'kids' ] },
48      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
49      { name: 'printer paper', tags: [ 'office', 'stationary' ] },
50      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
51      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
52      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
53      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] }
54    ],
55    score: 1.168686032295227
56  },
57  {
58    items: [
59      { name: 'envelopes', tags: [ 'stationary', 'office', 'general' ] },
60      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
61      { name: 'notepad', tags: [ 'office', 'writing', 'school' ] },
62      { name: 'backpack', tags: [ 'school', 'travel', 'kids' ] },
63      { name: 'envelopes', tags: [ 'stationary', 'office', 'general' ] },
64      { name: 'pens', tags: [ 'writing', 'office', 'school', 'stationary' ] },
65      { name: 'binder', tags: [ 'school', 'general', 'organization' ] },
66      { name: 'laptop', tags: [ 'electronics', 'school', 'office' ] },
67      { name: 'printer paper', tags: [ 'office', 'stationary' ] },
68      { name: 'binder', tags: [ 'school', 'general', 'organization' ] }
69    ],
70    score: 1.168686032295227
71  }
72]

Query for Matching Embedded Documents Only

The following query returns only the nested documents that match the query. The query uses Atlas Search compound operator clauses in the $search stage to find matching documents and then the aggregation operators in the $project stage to return only matching embedded documents. Specifically, the query specifies the following pipeline stages:

Specifies the following criteria in the compound operator must clause:

  • Check if items.price field exists in the collection.

  • Search for the items tagged as office in the items.tags field.

  • Match only if value is more than 2 for the items.quantity field.

Limits the output to 5 documents.

Perform the following actions:

  • Exclude the _id field and only include the items and storeLocation fields.

  • Use $filter to return only elements of the items input array that match the condition specified using the $and operator. The and operator uses the following operators:

    • $ifNull to determine if items.price contains null values and replace null values, if present, with the replacement expression false.

    • $gt to check if quantity is greater than 2.

    • $in to check if office exists in the tags array.

1db.sales.aggregate(
2  {
3    "$search": {
4      "embeddedDocument": {
5        "path": "items",
6        "operator": {
7          "compound": {
8            "must": [
9              {
10                "range": {
11                  "path": "items.quantity",
12                  "gt": 2
13                }
14              },
15              {
16                "exists": {
17                  "path": "items.price"
18                }
19              },
20              {
21                "text": {
22                  "path": "items.tags",
23                  "query": "school"
24                }
25              }
26            ]
27          }
28        }
29      }
30    }
31  },
32  {
33    "$limit": 2
34  },
35  {
36    "$project": {
37      "_id": 0,
38      "storeLocation": 1,
39      "items": {
40        "$filter": {
41          "input": "$items",
42          "cond": {
43            "$and": [
44              {
45                "$ifNull": [
46                  "$$this.price", "false"
47                ]
48              },
49              {
50                "$gt": [
51                  "$$this.quantity", 2
52                ]
53              },
54              {
55                "$in": [
56                  "office", "$$this.tags"
57                ]
58              }
59            ]
60          }
61        }
62      }
63    }
64  }
65)
1[
2  {
3    storeLocation: 'Austin',
4    items: [
5      {
6        name: 'laptop',
7        tags: [ 'electronics', 'school', 'office' ],
8        price: Decimal128('753.04'),
9        quantity: 3
10      },
11      {
12        name: 'pens',
13        tags: [ 'writing', 'office', 'school', 'stationary' ],
14        price: Decimal128('19.09'),
15        quantity: 4
16      },
17      {
18        name: 'notepad',
19        tags: [ 'office', 'writing', 'school' ],
20        price: Decimal128('30.23'),
21        quantity: 5
22      },
23      {
24        name: 'pens',
25        tags: [ 'writing', 'office', 'school', 'stationary' ],
26        price: Decimal128('20.05'),
27        quantity: 4
28      },
29      {
30        name: 'notepad',
31        tags: [ 'office', 'writing', 'school' ],
32        price: Decimal128('22.08'),
33        quantity: 3
34      },
35      {
36        name: 'notepad',
37        tags: [ 'office', 'writing', 'school' ],
38        price: Decimal128('21.67'),
39        quantity: 4
40      }
41    ]
42  },
43  {
44    storeLocation: 'Austin',
45    items: [
46      {
47        name: 'notepad',
48        tags: [ 'office', 'writing', 'school' ],
49        price: Decimal128('24.16'),
50        quantity: 5
51      },
52      {
53        name: 'notepad',
54        tags: [ 'office', 'writing', 'school' ],
55        price: Decimal128('28.04'),
56        quantity: 5
57      },
58      {
59        name: 'notepad',
60        tags: [ 'office', 'writing', 'school' ],
61        price: Decimal128('21.42'),
62        quantity: 5
63      },
64      {
65        name: 'laptop',
66        tags: [ 'electronics', 'school', 'office' ],
67        price: Decimal128('1540.63'),
68        quantity: 3
69      },
70      {
71        name: 'pens',
72        tags: [ 'writing', 'office', 'school', 'stationary' ],
73        price: Decimal128('29.43'),
74        quantity: 5
75      },
76      {
77        name: 'pens',
78        tags: [ 'writing', 'office', 'school', 'stationary' ],
79        price: Decimal128('28.48'),
80        quantity: 5
81      }
82    ]
83  }
84]

Tip

To return only matching embedded documents in the results, include an equivalent $filter to match the $search criteria on the embedded document field. To return only matching embedded documents in the $search stage results, upvote this request in the MongoDB Feedback Engine.

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