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Tokenizers

A custom analyzer's tokenizer determines how MongoDB Search splits up text into discrete chunks for indexing. Tokenizers require a type field, and some take additional options as well.

Syntax

"tokenizer": {
  "type": "<tokenizer-type>",
  "<additional-option>": "<value>"
}

Tokenizer Types

MongoDB Search supports the following types of tokenizer:

The following sample index definitions and queries use the sample collection named minutes. To follow along with these examples, load the minutes collection on your cluster and navigate to the Create a Search Index page in the Atlas UI following the steps in the Create a MongoDB Search Index tutorial. Then, select the minutes collection as your data source, and follow the example procedure to create an index from the Atlas UI or using mongosh.

➤ Use the Select your language drop-down menu to set the language of the example on this page.

edgeGram

The edgeGram tokenizer tokenizes input from the left side, or "edge", of a text input into n-grams of given sizes. You can't use a custom analyzer with edgeGram tokenizer in the analyzer field for synonym or autocomplete field mapping definitions.

Attributes

It has the following attributes:

Note

The edgeGram tokenizer yields multiple output tokens per word and across words in input text, producing token graphs.

Because autocomplete field type mapping definitions and analyzers with synonym mappings only work when used with non-graph-producing tokenizers, you can't use a custom analyzer with edgeGram tokenizer in the analyzer field for autocomplete field type mapping definitions or analyzers with synonym mappings.

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `edgeGram`.
`minGram`	integer	yes	Number of characters to include in the shortest token created.
`maxGram`	integer	yes	Number of characters to include in the longest token created.

Example

The following index definition indexes the message field in the minutes collection using a custom analyzer named edgegramExample. It uses the edgeGram tokenizer to create tokens (searchable terms) between 2 and 7 characters long starting from the first character on the left side of words in the message field.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type edgegramExample in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select edgeGram from the dropdown and type the value for the following fields:

Field
Value
minGram
2
maxGram
7
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the message field.
Select message from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select edgegramExample from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Field	Value
minGram	`2`
maxGram	`7`

Replace the default index definition with the following:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "message": {
        "analyzer": "edgegramExample",
        "type": "string"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [],
      "name": "edgegramExample",
      "tokenFilters": [],
      "tokenizer": {
        "maxGram": 7,
        "minGram": 2,
        "type": "edgeGram"
      }
    }
  ]
}

1 db.minutes.createSearchIndex(
2   "default",
3   {
4     "mappings": {
5       "dynamic": true,
6       "fields": {
7         "message": {
8           "analyzer": "edgegramExample",
9           "type": "string"
10         }
11       }
12     },
13     "analyzers": [
14       {
15         "charFilters": [],
16         "name": "edgegramExample",
17         "tokenFilters": [],
18         "tokenizer": {
19           "maxGram": 7,
20           "minGram": 2,
21           "type": "edgeGram"
22         }
23       }
24     ]
25   }
26 )

The following query searches the message field in the minutes collection for text that begin with tr.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "text": {
      "query": "tr",
      "path": "message"
    }
  }
}

SCORE: 0.3150668740272522 _id:  "1"
 message: "try to siGn-In"
 page_updated_by: Object
   last_name: "AUERBACH"
   first_name: "Siân"
   email: "auerbach@example.com"
   phone: "(123)-456-7890"
 text: Object
   en_US: "<head> This page deals with department meetings.</head>"
   sv_FI: "Den här sidan behandlar avdelningsmöten"
   fr_CA: "Cette page traite des réunions de département"
SCORE: 0.3150668740272522 _id:  "3"
 message: "try to sign-in"
 page_updated_by: Object
   last_name: "LEWINSKY"
   first_name: "Brièle"
   email: "lewinsky@example.com"
   phone: "(123).456.9870"
 text: Object
   en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "tr",
6         "path": "message"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "message": 1
14     }
15   }
16 ])

{ _id: 1, message: 'try to siGn-In' },
{ _id: 3, message: 'try to sign-in' }

MongoDB Search returns documents with _id: 1 and _id: 3 in the results because MongoDB Search created a token with the value tr using the edgeGram tokenizer for the documents, which matches the search term. If you index the message field using the standard tokenizer, MongoDB Search would not return any results for the search term tr.

The following table shows the tokens that the edgeGram tokenizer and by comparison, the standard tokenizer, create for the documents in the results:

Tokenizer	Token Outputs
`standard`	`try`, `to`, `sign`, `in`
`edgeGram`	`tr`, `try`, `try{SPACE}`, `try t`, `try to`, `try to{SPACE}`

keyword

The keyword tokenizer tokenizes the entire input as a single token. MongoDB Search doesn't index string fields that exceed 32766 characters using the keyword tokenizer.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `keyword`.

Example

The following index definition indexes the message field in the minutes collection using a custom analyzer named keywordExample. It uses the keyword tokenizer to create a token (searchable terms) on the entire field as a single term.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type keywordExample in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select keyword from the dropdown.
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the message field.
Select message from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select keywordExample from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Replace the default index definition with the following:

1 {
2   "mappings": {
3     "dynamic": true,
4     "fields": {
5       "message": {
6         "analyzer": "keywordExample",
7         "type": "string"
8       }
9     }
10   },
11   "analyzers": [
12     {
13       "charFilters": [],
14       "name": "keywordExample",
15       "tokenFilters": [],
16       "tokenizer": {
17         "type": "keyword"
18       }
19     }
20   ]
21 }

1 db.minutes.createSearchIndex(
2   "default",
3   {
4     "mappings": {
5       "dynamic": true,
6       "fields": {
7         "message": {
8           "analyzer": "keywordExample",
9           "type": "string"
10         }
11       }
12     },
13     "analyzers": [
14       {
15         "charFilters": [],
16         "name": "keywordExample",
17         "tokenFilters": [],
18         "tokenizer": {
19           "type": "keyword"
20         }
21       }
22     ]
23   }
24 )

The following query searches the message field in the minutes collection for the phrase try to sign-in.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "text": {
      "query": "try to sign-in",
      "path": "message"
    }
  }
}

SCORE: 0.5472603440284729 _id:  "3"
 message: "try to sign-in"
 page_updated_by: Object
   last_name: "LEWINSKY"
   first_name: "Brièle"
   email: "lewinsky@example.com"
   phone: "(123).456.9870"
 text: Object
   en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "try to sign-in",
6         "path": "message"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "message": 1
14     }
15   }
16 ])

{ _id: 3, message: 'try to sign-in' }

MongoDB Search returns the document with _id: 3 in the results because MongoDB Search created a token with the value try to sign-in using the keyword tokenizer for the documents, which matches the search term. If you index the message field using the standard tokenizer, MongoDB Search returns documents with _id: 1, _id: 2 and _id: 3 for the search term try to sign-in because each document contains some of the tokens the standard tokenizer creates.

The following table shows the tokens that the keyword tokenizer and by comparison, the standard tokenizer, create for the document with _id: 3:

Tokenizer	Token Outputs
`standard`	`try`, `to`, `sign`, `in`
`keyword`	`try to sign-in`

nGram

The nGram tokenizer tokenizes into text chunks, or "n-grams", of given sizes. You can't use a custom analyzer with nGram tokenizer in the analyzer field for synonym or autocomplete field mapping definitions.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `nGram`.
`minGram`	integer	yes	Number of characters to include in the shortest token created.
`maxGram`	integer	yes	Number of characters to include in the longest token created.

Example

The following index definition indexes the title field in the minutes collection using a custom analyzer named ngramExample. It uses the nGram tokenizer to create tokens (searchable terms) between 4 and 6 characters long in the title field.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type ngramAnalyzer in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select nGram from the dropdown and type the value for the following fields:

Field
Value
minGram
4
maxGram
6
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the message field.
Select message from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select ngramAnalyzer from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Field	Value
minGram	`4`
maxGram	`6`

Replace the default index definition with the following:

1 {
2   "mappings": {
3     "dynamic": true,
4     "fields": {
5       "title": {
6         "analyzer": "ngramExample",
7         "type": "string"
8       }
9     }
10   },
11   "analyzers": [
12     {
13       "charFilters": [],
14       "name": "ngramExample",
15       "tokenFilters": [],
16       "tokenizer": {
17         "maxGram": 6,
18         "minGram": 4,
19         "type": "nGram"
20       }
21     }
22   ]
23 }

db.minutes.createSearchIndex("default", {
  "mappings": {
    "dynamic": true,
    "fields": {
      "title": {
        "analyzer": "ngramExample",
        "type": "string"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [],
      "name": "ngramExample",
      "tokenFilters": [],
      "tokenizer": {
        "maxGram": 6,
        "minGram": 4,
        "type": "nGram"
      }
    }
  ]
})

The following query searches the title field in the minutes collection for the term week.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "index": "default",
    "text": {
      "query": "week",
      "path": "title"
    }
  }
}

SCORE: 0.5895273089408875 _id:  "1"
   message: "try to siGn-In"
   page_updated_by: Object
     last_name: "AUERBACH"
     first_name: "Siân"
     email: "auerbach@example.com"
     phone: "(123)-456-7890"
   text: Object
     en_US: "<head> This page deals with department meetings.</head>"
     sv_FI: "Den här sidan behandlar avdelningsmöten"
     fr_CA: "Cette page traite des réunions de département"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "week",
6         "path": "title"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "title": 1
14     }
15   }
16 ])

{ _id: 1, title: "The team's weekly meeting" }

MongoDB Search returns the document with _id: 1 in the results because MongoDB Search created a token with the value week using the nGram tokenizer for the documents, which matches the search term. If you index the title field using the standard or edgeGram tokenizer, MongoDB Search would not return any results for the search term week.

The following table shows the tokens that the nGram tokenizer and by comparison, the standard and edgeGram tokenizer create for the document with _id: 1:

Tokenizer	Token Outputs
`standard`	`The`, `team's`, `weekly`, `meeting`
`edgeGram`	`The{SPACE}`, `The t`, `The te`
`nGram`	`The{SPACE}`, `The t`, `The te`, `he t`, ... , `week`, `weekl`, `weekly`, `eekl`, ..., `eetin`, `eeting`, `etin`, `eting`, `ting`

regexCaptureGroup

The regexCaptureGroup tokenizer matches a Java regular expression pattern to extract tokens.

Tip

To learn more about the Java regular expression syntax, see the Pattern class in the Java documentation.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `regexCaptureGroup`.
`pattern`	string	yes	Regular expression to match against.
`group`	integer	yes	Index of the character group within the matching expression to extract into tokens. Use `0` to extract all character groups.

Example

The following index definition indexes the page_updated_by.phone field in the minutes collection using a custom analyzer named phoneNumberExtractor. It uses the following:

mappings character filter to remove parenthesis around the first three digits and replace all spaces and periods with dashes
regexCaptureGroup tokenizer to create a single token from the first US-formatted phone number present in the text input

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type phoneNumberExtractor in the Analyzer Name field.
Expand Character Filters and click Add character filter.
Select mapping from the dropdown and click Add mapping.
Enter the following characters in the Original field, one at a time, and leave the corresponding Replacement field empty.

Original
Replacement
-
.
(
)
Click Add character filter.
Expand Tokenizer if it's collapsed.
Select regexCaptureGroup from the dropdown and type the value for the following fields:

Field
Value
pattern
^\\b\\d{3}[-]?\\d{3}[-]?\\d{4}\\b$
group
0
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the page_updated_by.phone field.
Select page_updated_by.phone from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select phoneNumberExtractor from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Original	Replacement
`-`
`.`
`(`
`)`

Field	Value
pattern	`^\\b\\d{3}[-]?\\d{3}[-]?\\d{4}\\b$`
group	`0`

Replace the default index definition with the following example:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "page_updated_by": {
        "fields": {
          "phone": {
            "analyzer": "phoneNumberExtractor",
            "type": "string"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [
        {
          "mappings": {
            " ": "-",
            "(": "",
            ")": "",
            ".": "-"
          },
          "type": "mapping"
        }
      ],
      "name": "phoneNumberExtractor",
      "tokenFilters": [],
      "tokenizer": {
        "group": 0,
        "pattern": "^\\b\\d{3}[-]?\\d{3}[-]?\\d{4}\\b$",
        "type": "regexCaptureGroup"
      }
    }
  ]
}

db.minutes.createSearchIndex("default", {
  "mappings": {
    "dynamic": true,
    "fields": {
      "page_updated_by": {
        "fields": {
          "phone": {
            "analyzer": "phoneNumberExtractor",
            "type": "string"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [
        {
          "mappings": {
            " ": "-",
            "(": "",
            ")": "",
            ".": "-"
          },
          "type": "mapping"
        }
      ],
      "name": "phoneNumberExtractor",
      "tokenFilters": [],
      "tokenizer": {
        "group": 0,
        "pattern": "^\\b\\d{3}[-]?\\d{3}[-]?\\d{4}\\b$",
        "type": "regexCaptureGroup"
        }
    }
  ]
})

The following query searches the page_updated_by.phone field in the minutes collection for the phone number 123-456-9870.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "index": "default",
    "text": {
      "query": "123-456-9870",
      "path": "page_updated_by.phone"
    }
  }
}

SCORE: 0.5472603440284729 _id:  "3"
 message: "try to sign-in"
 page_updated_by: Object
   last_name: "LEWINSKY"
   first_name: "Brièle"
   email: "lewinsky@example.com"
   phone: "(123).456.9870"
 text: Object
   en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "123-456-9870",
6         "path": "page_updated_by.phone"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "page_updated_by.phone": 1
14     }
15   }
16 ])

{ _id: 3, page_updated_by: { phone: '(123).456.9870' }

MongoDB Search returns the document with _id: 3 in the results because MongoDB Search created a token with the value 123-456-7890 using the regexCaptureGroup tokenizer for the documents, which matches the search term. If you index the page_updated_by.phone field using the standard tokenizer, MongoDB Search returns all of the documents for the search term 123-456-7890.

The following table shows the tokens that the regexCaptureGroup tokenizer and by comparison, the standard tokenizer, create for the document with _id: 3:

Tokenizer	Token Outputs
`standard`	`123`, `456.9870`
`regexCaptureGroup`	`123-456-9870`

regexSplit

The regexSplit tokenizer splits tokens with a Java regular-expression based delimiter.

Tip

To learn more about the Java regular expression syntax, see the Pattern class in the Java documentation.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `regexSplit`.
`pattern`	string	yes	Regular expression to match against.

Example

The following index definition indexes the page_updated_by.phone field in the minutes collection using a custom analyzer named dashDotSpaceSplitter. It uses the regexSplit tokenizer to create tokens (searchable terms) from one or more hyphens, periods and spaces on the page_updated_by.phone field.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type dashDotSpaceSplitter in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select regexSplit from the dropdown and type the value for the following field:

Field
Value
pattern
[-. ]+
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the page_updated_by.phone field.
Select page_updated_by.phone from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select dashDotSpaceSplitter from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Field	Value
pattern	`[-. ]+`

Replace the default index definition with the following:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "page_updated_by": {
        "fields": {
          "phone": {
            "analyzer": "dashDotSpaceSplitter",
            "type": "string"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [],
      "name": "dashDotSpaceSplitter",
      "tokenFilters": [],
      "tokenizer": {
        "pattern": "[-. ]+",
        "type": "regexSplit"
      }
    }
  ]
}

db.minutes.createSearchIndex("default", {
  "mappings": {
    "dynamic": true,
    "fields": {
      "page_updated_by": {
        "fields": {
          "phone": {
            "analyzer": "dashDotSpaceSplitter",
            "type": "string"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [],
      "name": "dashDotSpaceSplitter",
      "tokenFilters": [],
      "tokenizer": {
        "pattern": "[-. ]+",
        "type": "regexSplit"
      }
    }
  ]
})

The following query searches the page_updated_by.phone field in the minutes collection for the digits 9870.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "index": "default",
    "text": {
      "query": "9870",
      "path": "page_updated_by.phone"
    }
  }
}

SCORE: 0.5472603440284729 _id:  "3"
 message: "try to sign-in"
 page_updated_by: Object
   last_name: "LEWINSKY"
   first_name: "Brièle"
   email: "lewinsky@example.com"
   phone: "(123).456.9870"
 text: Object
   en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "9870",
6         "path": "page_updated_by.phone"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "page_updated_by.phone": 1
14     }
15   }
16 ])

{ _id: 3, page_updated_by: { phone: '(123).456.9870' }

MongoDB Search returns the document with _id: 3 in the results because MongoDB Search created a token with the value 9870 using the regexSplit tokenizer for the documents, which matches the search term. If you index the page_updated_by.phone field using the standard tokenizer, MongoDB Search would not return any results for the search term 9870.

The following table shows the tokens that the regexCaptureGroup tokenizer and by comparison, the standard tokenizer, create for the document with _id: 3:

Tokenizer	Token Outputs
`standard`	`123`, `456.9870`
`regexSplit`	`(123)`, `456`, `9870`

standard

The standard tokenizer tokenizes based on word break rules from the Unicode Text Segmentation algorithm.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `standard`.
`maxTokenLength`	integer	no	Maximum length for a single token. Tokens greater than this length are split at `maxTokenLength` into multiple tokens. Default: `255`

Example

The following index definition indexes the message field in the minutes collection using a custom analyzer named standardExample. It uses the standard tokenizer and the stopword token filter.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type standardExample in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select standard from the dropdown.
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the message field.
Select message from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select standardExample from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Replace the default index definition with the following:

{
 "mappings": {
   "dynamic": true,
   "fields": {
     "message": {
       "analyzer": "standardExample",
       "type": "string"
     }
   }
 },
 "analyzers": [
   {
     "charFilters": [],
     "name": "standardExample",
     "tokenFilters": [],
     "tokenizer": {
       "type": "standard"
     }
   }
 ]
}

1 db.minutes.createSearchIndex(
2   "default",
3   {
4     "mappings": {
5       "dynamic": true,
6       "fields": {
7         "message": {
8           "analyzer": "standardExample",
9           "type": "string"
10         }
11       }
12     },
13     "analyzers": [
14       {
15         "charFilters": [],
16         "name": "standardExample",
17         "tokenFilters": [],
18         "tokenizer": {
19           "type": "standard"
20         }
21       }
22     ]
23   }
24 )

The following query searches the message field in the minutes collection for the term signature.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "text": {
      "query": "signature",
      "path": "message"
    }
  }
}

SCORE: 0.5376965999603271 _id:  "4"
 message: "write down your signature or phone №"
 page_updated_by: Object
   last_name: "LEVINSKI"
   first_name: "François"
   email: "levinski@example.com"
   phone: "123-456-8907"
 text: Object
   en_US: "<body>This page has been updated with the items on the agenda.</body>"
   es_MX: "La página ha sido actualizada con los puntos de la agenda."
   pl_PL: "Strona została zaktualizowana o punkty porządku obrad."

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "signature",
6         "path": "message"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "message": 1
14     }
15   }
16 ])

{ _id: 4, message: 'write down your signature or phone №' }

MongoDB Search returns the document with _id: 4 because MongoDB Search created a token with the value signature using the standard tokenizer for the documents, which matches the search term. If you index the message field using the keyword tokenizer, MongoDB Search would not return any results for the search term signature.

The following table shows the tokens that the standard tokenizer and by comparison, the keyword analyzer, create for the document with _id: 4:

Tokenizer	Token Outputs
`standard`	`write`, `down`, `your`, `signature`, `or`, `phone`
`keyword`	`write down your signature or phone №`

uaxUrlEmail

The uaxUrlEmail tokenizer tokenizes URLs and email addresses. Although uaxUrlEmail tokenizer tokenizes based on word break rules from the Unicode Text Segmentation algorithm, we recommend using uaxUrlEmail tokenizer only when the indexed field value includes URLs and email addresses. For fields that don't include URLs or email addresses, use the standard tokenizer to create tokens based on word break rules.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `uaxUrlEmail`.
`maxTokenLength`	int	no	Maximum number of characters in one token. Default: `255`

Example

Basic Example

Create a simple index using the uaxUrlEmail tokenizer.

The following index definition indexes the page_updated_by.email field in the minutes collection using a custom analyzer named basicEmailAddressAnalyzer. It uses the uaxUrlEmail tokenizer to create tokens (searchable terms) from URLs and email addresses in the page_updated_by.email field.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type basicEmailAddressAnalyzer in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select uaxUrlEmail from the dropdown.
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the page_updated_by.email field.
Select page_updated_by.email from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select basicEmailAddressAnalyzer from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Replace the default index definition with the following:

{
  "mappings": {
    "fields": {
      "page_updated_by": {
       "fields": {
          "email": {
            "analyzer": "basicEmailAddressAnalyzer",
            "type": "string"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "name": "basicEmailAddressAnalyzer",
      "tokenizer": {
        "type": "uaxUrlEmail"
      }
    }
  ]
}

db.minutes.createSearchIndex("default", {
  "mappings": {
    "fields": {
      "page_updated_by": {
        "fields": {
          "email": {
            "analyzer": "basicEmailAddressAnalyzer",
            "type": "string"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "name": "basicEmailAddressAnalyzer",
      "tokenizer": {
        "type": "uaxUrlEmail"
      }
    }
  ]
})

The following query searches the page_updated_by.email field in the minutes collection for the email lewinsky@example.com.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "text": {
      "query": "lewinsky@example.com",
      "path": "page_updated_by.email"
    }
  }
}

SCORE: 0.5472603440284729   _id:  "3"
 message: "try to sign-in"
   page_updated_by: Object
   last_name: "LEWINSKY"
   first_name: "Brièle"
   email: "lewinsky@example.com"
   phone: "(123).456.9870"
 text: Object
   en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "autocomplete": {
5         "query": "lewinsky@example.com",
6         "path": "page_updated_by.email"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "page_updated_by.email": 1
14     }
15   }
16 ])

{ _id: 3, page_updated_by: { email: 'lewinsky@example.com' } }

MongoDB Search returns the document with _id: 3 in the results because MongoDB Search created a token with the value lewinsky@example.com using the uaxUrlEmail tokenizer for the documents, which matches the search term. If you index the page_updated_by.email field using the standard tokenizer, MongoDB Search returns all the documents for the search term lewinsky@example.com.

The following table shows the tokens that the uaxUrlEmail tokenizer and by comparison, the standard tokenizer, create for the document with _id: 3:

Tokenizer	Token Outputs
`standard`	`lewinsky`, `example.com`
`uaxUrlEmail`	`lewinsky@example.com`

Advanced Example

Create an index using the uaxUrlEmail tokenizer with autocomplete tokenization strategy.

The following index definition indexes the page_updated_by.email field in the minutes collection using a custom analyzer named emailAddressAnalyzer. It uses the following:

The autocomplete type with an edgeGram tokenization strategy
The uaxUrlEmail tokenizer to create tokens (searchable terms) from URLs and email addresses

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type emailAddressAnalyzer in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select uaxUrlEmail from the dropdown.
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the page_updated_by.email field.
Select page_updated_by.email from the Field Name dropdown and Autocomplete from the Data Type dropdown.
In the properties section for the data type, select emailAddressAnalyzer from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Replace the default index definition with the following:

{
  "mappings": {
    "fields": {
      "page_updated_by": {
        "fields": {
          "email": {
            "analyzer": "emailAddressAnalyzer",
            "tokenization": "edgeGram",
            "type": "autocomplete"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "name": "emailAddressAnalyzer",
      "tokenizer": {
        "type": "uaxUrlEmail"
      }
    }
  ]
}

db.minutes.createSearchIndex("default", {
  "mappings": {
    "fields": {
      "page_updated_by": {
        "fields": {
          "email": {
            "analyzer": "emailAddressAnalyzer",
            "tokenization": "edgeGram",
            "type": "autocomplete"
          }
        },
        "type": "document"
      }
    }
  },
  "analyzers": [
    {
      "name": "emailAddressAnalyzer",
      "tokenizer": {
        "type": "uaxUrlEmail"
      }
    }
  ]
})

The following query searches the page_updated_by.email field in the minutes collection for the term exam.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "autocomplete": {
      "query": "lewinsky@example.com",
      "path": "page_updated_by.email"
    }
  }
}

SCORE: 1.0203158855438232   _id:  "3"
 message: "try to sign-in"
   page_updated_by: Object
   last_name: "LEWINSKY"
   first_name: "Brièle"
   email: "lewinsky@example.com"
   phone: "(123).456.9870"
 text: Object
   en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "autocomplete": {
5         "query": "lewinsky@example.com",
6         "path": "page_updated_by.email"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "page_updated_by.email": 1
14     }
15   }
16 ])

{ _id: 3, page_updated_by: { email: 'lewinsky@example.com' } }

The following table shows the tokens that the uaxUrlEmail tokenizer and by comparison, the standard tokenizer, create for the document with _id: 3:

Tokenizer	MongoDB Search Field Type	Token Outputs
`standard`	`autocomplete` `edgeGram`	`le`, `lew`, `lewi`, `lewin`, `lewins`, `lewinsk`, `lewinsky`, `lewinsky@`, `lewinsky`, `ex`, `exa`, `exam`, `examp`, `exampl`, `example`, `example.`, `example.c`, `example.co`, `example.com`
`uaxUrlEmail`	`autocomplete` `edgeGram`	`le`, `lew`, `lewi`, `lewin`, `lewins`, `lewinsk`, `lewinsky`, `lewinsky@`, `lewinsky@e`, `lewinsky@ex`, `lewinsky@exa`, `lewinsky@exam`, `lewinsky@examp`, `lewinsky@exampl`

whitespace

The whitespace tokenizer tokenizes based on occurrences of whitespace between words.

Attributes

It has the following attributes:

Name	Type	Required?	Description
`type`	string	yes	Human-readable label that identifies this tokenizer type. Value must be `whitespace`.
`maxTokenLength`	integer	no	Maximum length for a single token. Tokens greater than this length are split at `maxTokenLength` into multiple tokens. Default: `255`

Example

The following index definition indexes the message field in the minutes collection using a custom analyzer named whitespaceExample. It uses the whitespace tokenizer to create tokens (searchable terms) from any whitespaces in the message field.

In the Custom Analyzers section, click Add Custom Analyzer.
Select the Create Your Own radio button and click Next.
Type whitespaceExample in the Analyzer Name field.
Expand Tokenizer if it's collapsed.
Select whitespace from the dropdown.
Click Add to add the custom analyzer to your index.
In the Field Mappings section, click Add Field Mapping to apply the custom analyzer on the message field.
Select message from the Field Name dropdown and String from the Data Type dropdown.
In the properties section for the data type, select whitespaceExample from the Index Analyzer and Search Analyzer dropdowns.
Click Add, then Save Changes.

Replace the default index definition with the following example:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "message": {
        "analyzer": "whitespaceExample",
        "type": "string"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [],
      "name": "whitespaceExample",
      "tokenFilters": [],
      "tokenizer": {
        "type": "whitespace"
      }
    }
  ]
}

db.minutes.createSearchIndex("default", {
  "mappings": {
    "dynamic": true,
    "fields": {
      "message": {
        "analyzer": "whitespaceExample",
        "type": "string"
      }
    }
  },
  "analyzers": [
    {
      "charFilters": [],
      "name": "whitespaceExample",
      "tokenFilters": [],
      "tokenizer": {
        "type": "whitespace"
      }
    }
  ]
})

The following query searches the message field in the minutes collection for the term SIGN-IN.

Click the Query button for your index.
Click Edit Query to edit the query.
Click on the query bar and select the database and collection.

Replace the default query with the following and click Find:

{
  "$search": {
    "text": {
      "query": "sign-in",
      "path": "message"
    }
  }
}

SCORE: 0.6722691059112549 _id:  "3"
   message: "try to sign-in"
   page_updated_by: Object
     last_name: "LEWINSKY"
     first_name: "Brièle"
     email: "lewinsky@example.com"
     phone: "(123).456.9870"
   text: Object
     en_US: "<body>We'll head out to the conference room by noon.</body>"

1 db.minutes.aggregate([
2   {
3     "$search": {
4       "text": {
5         "query": "sign-in",
6         "path": "message"
7       }
8     }
9   },
10   {
11     "$project": {
12       "_id": 1,
13       "message": 1
14     }
15   }
16 ])

[ { _id: 3, message: 'try to sign-in' } ]

MongoDB Search returns the document with _id: 3 in the results because MongoDB Search created a token with the value sign-in using the whitespace tokenizer for the documents, which matches the search term. If you index the message field using the standard tokenizer, MongoDB Search returns documents with _id: 1, _id: 2 and _id: 3 for the search term sign-in.

The following table shows the tokens that the whitespace tokenizer and by comparison, the standard tokenizer, create for the document with _id: 3:

Tokenizer	Token Outputs
`standard`	`try`, `to`, `sign`, `in`
`whitespace`	`try`, `to`, `sign-in`

Back

Character Filters

Token Filters

1	db.minutes.createSearchIndex(
2	"default",
3	{
4	"mappings": {
5	"dynamic": true,
6	"fields": {
7	"message": {
8	"analyzer": "edgegramExample",
9	"type": "string"
10	}
11	}
12	},
13	"analyzers": [
14	{
15	"charFilters": [],
16	"name": "edgegramExample",
17	"tokenFilters": [],
18	"tokenizer": {
19	"maxGram": 7,
20	"minGram": 2,
21	"type": "edgeGram"
22	}
23	}
24	]
25	}
26	)

1	db.minutes.aggregate([
2	{
3	"$search": {
4	"text": {
5	"query": "tr",
6	"path": "message"
7	}
8	}
9	},
10	{
11	"$project": {
12	"_id": 1,
13	"message": 1
14	}
15	}
16	])

1	db.minutes.aggregate([
2	{
3	"$search": {
4	"autocomplete": {
5	"query": "lewinsky@example.com",
6	"path": "page_updated_by.email"
7	}
8	}
9	},
10	{
11	"$project": {
12	"_id": 1,
13	"page_updated_by.email": 1
14	}
15	}
16	])