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Operators & Collectors

Operators & Collectors

`compound` Operator

Definition

compound: The compound operator combines two or more operators into a single query. Each element of a compound query is called a clause, and each clause consists of one or more sub-queries. MongoDB Search provides a sample compound query template with guidance.

Syntax

compound has the following syntax:

1 {
2   $search: {
3     "index": <index name>, // optional, defaults to "default"
4     "compound": {
5       <must | mustNot | should | filter>: [ { <clauses> } ],
6       "score": <options>
7     }
8   }
9 }

Each must, mustNot, should, and filter clause contains an array of subclauses. Use array syntax even if the array contains only one subclause. See the examples on this page.

Options

compound uses the following terms to construct a query:

must

Clauses that must match to for a document to be included in the results. The returned score is the sum of the scores of all the subqueries in the clause.

Maps to the AND boolean operator.

mustNot

Clauses that must not match for a document to be included in the results. mustNot clauses don't contribute to a returned document's score.

Maps to the AND NOT boolean operator.

should

Clauses that you prefer to match in documents that are included in the results. Documents that contain a match for a should clause have higher scores than documents that don't contain a should clause. The returned score is the sum of the scores of all the subqueries in the clause.

If you use more than one should clause, you can use the minimumShouldMatch option to specify a minimum number of should clauses that must match to include a document in the results. The value of the minimumShouldMatch must be less than or equal to the number of should clauses in the query. If omitted, the minimumShouldMatch option defaults to 0.

See an example.

Maps to the OR boolean operator.

If you use only the should clause inside a compound query, the compound operator treats the array of the should clause queries as a logical OR. MongoDB Search must find a match for at least one should criteria to return any results. When you specify multiple should clause criteria with the minimumShouldMatch option set to 0, MongoDB Search treats minimumShouldMatch as set to 1 and must match at least one criteria to return any results.

filter

Clauses that must all match for a document to be included in the results. filter clauses do not contribute to a returned document's score.

For example, you can replace the $match stage with the $search stage using the compound operator filter option. The following $match stage filters for documents with a given value for the field role:

$match: {
    "role": { "$in": [ "CLIENT", "PROFESSIONAL" ] }
}

You can use the compound operator filter option instead:

$search: {
  "compound": {
    "filter": [{
      "queryString": {
        "defaultPath": "role",
        "query": "CLIENT OR PROFESSIONAL"
      }
    }]
  }
}

See another filter example.

score

Modify the score of the entire compound clause. You can use score to boost, replace, or otherwise alter the score. If you don't specify score, the returned score is the sum of the scores of all the subqueries in the must and should clauses that generated a match. To learn more, see Scoring Behavior.

Usage

You can use any of the clauses with any top-level operator, such as autocomplete, text, or span, to specify query criteria.

To optimize performance, place non-scoring operators such as equals, range, and in in the filter clause to avoid unnecessary scoring operations. If you want to run multiple operations that must be evaluated as an OR condition, nest a should clause within the top-level filter.

Example

[
  {
    $search: {
      index: "default",
      compound: {
        filter: [
          {
            compound: {
              should: [
                {
                  equals: {
                    path: "accommodates",
                    value: 2
                  }
                },
                {
                  range: {
                    path: "pricePerNight",
                    lte: 200
                  }
                }
              ]
            }
          }
        ]
      }
    }
  }
]

Scoring Behavior

MongoDB Search scores documents in the result set by summing the score that the document received for each individual clause that generated a match on the document. Only must and should clauses participate in scoring. The result set is ordered by score, highest to lowest.

The following table shows the compound clauses that contribute and don't contribute to the score.

Clause	Contributes to Score
`filter`
`must`
`mustNot`
`should`

You can boost or replace the score of the entire compound query using the score option. For an example of replacing the entire compound score, see Compound Score Example below. You can use score to also boost or alter the score for each subquery in each clause. For some examples of altered scores in compound operator clauses, see Modify the Score.

Examples

You can try the following examples in the MongoDB Search Playground or your cluster.

Sample Collection

The examples on this page use a collection called fruit, which contains the following documents:

1 {
2   "_id" : 1,
3   "type" : "apple",
4   "description" : "Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp.",
5   "category" : "nonorganic",
6   "in_stock" : false
7 },
8 {
9   "_id" : 2,
10   "type" : "banana",
11   "description" : "Bananas are usually sold in bunches of five or six.",
12   "category" : "nonorganic",
13   "in_stock" : true
14 },
15 {
16   "_id" : 3,
17   "type" : "pear",
18   "description" : "Bosc and Bartlett are the most common varieties of pears.",
19   "category" : "organic",
20   "in_stock" : true
21 }

Sample Index

The fruit collection has a default index with dynamic mappings that automatically indexes all the fields in the collection and uses the default standard analyzer. The standard analyzer lower-cases all words and disregards common stop words ("the", "a", "and", etc).

Sample Queries

The following queries demonstrate the $search compound operator in MongoDB Search queries.

`must` and `mustNot` Example

The following example uses a combination of must and mustNot clauses to contruct a query. The must clause uses the text operator to search for the term varieties in the description field. For a document to match, it must fulfill the must clause. The mustNot clause performs a search operation for the term apples in the description field. For a document to match, it must not fulfill the mustNot clause.

1 db.fruit.aggregate([
2   {
3     "$search": {
4       "compound": {
5         "must": [{
6           "text": {
7             "query": "varieties",
8             "path": "description"
9           }
10         }],
11         "mustNot": [{
12           "text": {
13             "query": "apples",
14             "path": "description"
15           }
16         }]
17       }
18     }
19   }
20 ])

The above query returns the document with _id: 3 because its description field contains the word varieties and does not contain apples.

➤ Try this in the MongoDB Search Playground.

`must` and `should` Example

The following queries use must to specify search conditions that must be met and should to specify preference for documents that contain the word Fuji.

For this query, the $project pipeline stage excludes all document fields except _id and adds a score field, which displays the document's relevance score.

1 db.fruit.aggregate([
2   {
3     "$search": {
4       "compound": {
5         "must": [{
6           "text": {
7             "query": "varieties",
8             "path": "description"
9           }
10         }],
11         "should": [{
12           "text": {
13             "query": "Fuji",
14             "path": "description"
15           }
16         }]
17       }
18     }
19   },
20   {
21     "$project": {
22       "score": { "$meta": "searchScore" }
23     }
24   }
25 ])

{ "_id" : 1, "score" : 0.6425117254257202 }
{ "_id" : 3, "score" : 0.21649497747421265 }

The document with _id: 1 has a higher score because its description field contains the word Fuji, satisfying the should clause.

➤ Try this in the MongoDB Search Playground.

The following query also specifies a constant score of 3 for all the documents in the results. For this query, the $project pipeline stage excludes all document fields except _id and adds a score field.

1 db.fruit.aggregate([
2   {
3     "$search": {
4       "compound": {
5         "must": [{
6           "text": {
7             "query": "varieties",
8             "path": "description"
9           }
10         }],
11         "should": [{
12           "text": {
13             "query": "Fuji",
14             "path": "description"
15           }
16         }],
17         "score": { "constant": { "value": 3 } }
18       }
19     }
20   },
21   {
22     "$project": {
23       "score": { "$meta": "searchScore" }
24     }
25   }
26 ])

[ { _id: 1, score: 3 }, { _id: 3, score: 3 } ]

Both the documents receive the same score because the constant option in the query replaces the score of each document in the result with the number 3.

➤ Try this in the MongoDB Search Playground.

minimumShouldMatch Example

In a query with multiple should clauses, you can use the miniumumShouldMatch option to specify a minimum number of clauses which must match to return a result.

The following query has one must clause and two should clauses, with a minimumShouldMatch value of 1. A document must include the term varieties in the description field and must include either Fuji or Golden Delicious in the description field to be included in the result set.

1 db.fruit.aggregate([
2   {
3     $search: {
4       "compound": {
5         "must": [{
6           "text": {
7              "query": "varieties",
8              "path": "description"
9           }
10         }],
11         "should": [{
12           "text": {
13             "query": "Fuji",
14             "path": "description"
15           }
16         },
17         {
18           "text": {
19             "query": "Golden Delicious",
20             "path": "description"
21           }
22         }],
23         "minimumShouldMatch": 1
24       }
25     }
26   }
27 ])

1 {
2   "_id" : 1,
3   "type" : "apple",
4   "description" : "Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp.",
5   "category" : "nonorganic",
6   "in_stock" : false
7 }

The document with _id: 1 matches the must clause and the first of the two should clauses.

➤ Try this in the MongoDB Search Playground.

`filter` Examples

filter behaves the same as must, except that the filter clause is not considered in a returned document's score, and therefore does not affect the order of the returned documents.

The following query uses the following clauses:

must and filter to specify search conditions which must be met.
should to specify preference for documents containing the word banana. The should clause doesn't include the minimumShouldMatch option. When you omit minimumShouldMatch, it defaults to 0.

1 db.fruit.aggregate([
2   {
3     "$search": {
4       "compound": {
5         "must": [{
6           "text": {
7             "query": "varieties",
8             "path": "description"
9           }
10         }],
11         "should": [{
12           "text": {
13             "query": "banana",
14             "path": "description"
15           }
16         }],
17         "filter": [{
18           "text": {
19             "query": "granny",
20             "path": "description"
21           }
22         }]
23       }
24     }
25   }
26 ])

1 {
2   "_id" : 1,
3   "type" : "apple",
4   "description" : "Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp.",
5   "category" : "nonorganic",
6   "in_stock" : false
7 }

The returned document fulfills all the requirements for inclusion:

Both the must clause and the filter clause match.
The minimumShouldMatch value is not specified, so it defaults to 0. As a result, the should clause fails and still returns a document.

➤ Try this in the MongoDB Search Playground.

You can replace the $match with $in in your queries against data on your cluster with the filter clause. The following query demonstrates how to use filter in the $search stage to specify the search terms that must match. The query also uses should to specify preference for documents containing the term varieties. The query includes the $project pipeline stage to do the following:

Exclude all fields except _id and description.
Add a score field, which displays the document's relevance score.

1 db.fruit.aggregate([
2   {
3     "$search": {
4       "compound": {
5         "filter": [{
6           "text": {
7             "query": ["apples", "bananas"],
8             "path": "description"
9           }
10         }],
11         "should": [{
12           "text": {
13             "query": "varieties",
14             "path": "description"
15           }
16         }]
17       }
18     }
19   },
20   {
21     "$project": {
22       "description": 1,
23       "score": { "$meta": "searchScore" }
24     }
25   }
26 ])

1 [
2   {
3     _id: 1,
4     description: 'Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp. The most popular varieties are McIntosh, Gala, and Granny Smith.',
5     score: 0.36074575781822205
6   },
7   {
8     _id: 2,
9     description: 'Bananas are usually sold in bunches of five or six.',
10     score: 0
11   }
12 ]

The documents in the result fulfill all the requirements for inclusion:

Both documents contain the term apples or bananas that was specified in the filter clause of the query.
Document with _id: 1 scores higher than document with _id: 2 because it contains the term varieties specified in the should clause of the query.

➤ Try this in the MongoDB Search Playground.

Nested Example

The following example uses nested compound clauses to construct a query. For this example, the fruit collection has an index on the type, category, and in_stock fields, whose text fields use the default analyzer. The query requires documents to only satisfy one of the following should clauses:

Contain the word apple in the type field.
Contain the term organic in the category field and have the value true in the in_stock field.

1 db.fruit.aggregate([
2   {
3     $search: {
4       "compound": {
5         "should": [
6           {
7             "text": {
8               "query": "apple",
9               "path": "type"
10             }
11           },
12           {
13             "compound": {
14               "must": [
15                 {
16                   "text": {
17                     "query": "organic",
18                     "path": "category"
19                   }
20                 },
21                 {
22                   "equals": {
23                     "value": true,
24                     "path": "in_stock"
25                   }
26                 }
27               ]
28             }
29           }
30         ],
31         "minimumShouldMatch": 1
32       }
33     }
34   }
35 ])

1 {
2   "_id" : 3,
3   "type" : "pear",
4   "description" : "Bosc and Bartlett are the most common varieties of pears.",
5   "category" : "organic",
6   "in_stock" : true
7 }
8 {
9   "_id" : 1,
10   "type" : "apple",
11   "description" : "Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp.",
12   "category" : "nonorganic",
13   "in_stock" : false
14 }

The documents in the results fulfill all the requirements for inclusion:

The document with _id: 3 matches the must clause nested within the second should clause.
The document with _id: 1 matches the first should clause.

➤ Try this in the MongoDB Search Playground.

Metadata Examples

The following queries retrieve metadata to return the number of categories in the fruit collection using either the $searchMeta pipeline stage or the $$SEARCHMETA variable of the $search stage.

The following query returns only the metadata in the results.

db.fruit.aggregate([
  {
    "$searchMeta": {
      "facet": {
        "operator": {
          "compound": {
            "must": [{
              "text": {
                "query": "varieties",
                "path": "description"
              }
            }],
            "should": [{
              "text": {
                "query": "Fuji",
                "path": "description"
              }
            }]
          }
        },
        "facets": {
          "categoryFacet": {
            "type": "string",
            "path": "category"
          }
        }
      }
    }
  }
])

[
  {
    count: { lowerBound: Long('2') },
    facet: {
      categoryFacet: {
        buckets: [
          { _id: 'nonorganic', count: Long('1') },
          { _id: 'organic', count: Long('1') }
        ]
      }
    }
  }
]

The following query returns the metadata and search results.

db.fruit.aggregate([
  {
    "$search": {
      "facet": {
        "operator": {
          "compound": {
            "must": [{
              "text": {
                "query": "varieties",
                "path": "description"
              }
            }],
            "should": [{
              "text": {
                "query": "Fuji",
                "path": "description"
              }
            }]
          }
        },
        "facets": {
          "categoryFacet": {
            "type": "string",
            "path": "category"
          }
        }
      }
    }
  },
  { "$limit": 2 },
  {
    "$facet": {
      "docs": [
        { "$project":
          {
            "type": 1,
            "description": 1
          }
        }
      ],
      "meta": [
        {"$replaceWith": "$$SEARCH_META"},
        {"$limit": 1}
      ]
    }
  },
  {
    "$set": {
      "meta": {
        "$arrayElemAt": ["$meta", 0]
      }
    }
  }
])

[
  {
    docs: [
      {
        _id: 1,
        type: 'apple',
        description: 'Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp.'
      },
      {
        _id: 3,
        type: 'pear',
        description: 'Bosc and Bartlett are the most common varieties of pears.'
      }
    ],
    meta: {
      count: { lowerBound: Long('2') },
      facet: {
        categoryFacet: {
          buckets: [
            { _id: 'nonorganic', count: Long('1') },
            { _id: 'organic', count: Long('1') }
          ]
        }
      }
    }
  }
]

Back

autocomplete

embedded-document

1	{
2	$search: {
3	"index": <index name>, // optional, defaults to "default"
4	"compound": {
5	<must \| mustNot \| should \| filter>: [ { <clauses> } ],
6	"score": <options>
7	}
8	}
9	}

1	{
2	"_id" : 1,
3	"type" : "apple",
4	"description" : "Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp.",
5	"category" : "nonorganic",
6	"in_stock" : false
7	},
8	{
9	"_id" : 2,
10	"type" : "banana",
11	"description" : "Bananas are usually sold in bunches of five or six.",
12	"category" : "nonorganic",
13	"in_stock" : true
14	},
15	{
16	"_id" : 3,
17	"type" : "pear",
18	"description" : "Bosc and Bartlett are the most common varieties of pears.",
19	"category" : "organic",
20	"in_stock" : true
21	}

1	db.fruit.aggregate([
2	{
3	"$search": {
4	"compound": {
5	"must": [{
6	"text": {
7	"query": "varieties",
8	"path": "description"
9	}
10	}],
11	"mustNot": [{
12	"text": {
13	"query": "apples",
14	"path": "description"
15	}
16	}]
17	}
18	}
19	}
20	])

1	[
2	{
3	_id: 1,
4	description: 'Apples come in several varieties, including Fuji, Granny Smith, and Honeycrisp. The most popular varieties are McIntosh, Gala, and Granny Smith.',
5	score: 0.36074575781822205
6	},
7	{
8	_id: 2,
9	description: 'Bananas are usually sold in bunches of five or six.',
10	score: 0
11	}
12	]

Definition

Syntax

Options

Usage

Example

Scoring Behavior

Examples

Sample Collection

Sample Index

Sample Queries

must and mustNot Example

must and should Example

minimumShouldMatch Example

filter Examples

Nested Example

Metadata Examples

`must` and `mustNot` Example

`must` and `should` Example

`filter` Examples