Custom Analyzers¶

Overview¶

An Atlas Search analyzer prepares a set of documents to be indexed by performing a series of operations to transform, filter, and group sequences of characters. You can define a custom analyzer to suit your specific indexing needs.

Text analysis is a three-step process:

Character Filters
You can specify one or more character filters to use in your custom analyzer. Character filters examine text one character at a time and perform filtering operations.
Tokenizer
An analyzer uses a tokenizer to split chunks of text into groups, or tokens, for indexing purposes. For example, the whitespace tokenizer splits text fields into individual words based on where whitespace occurs.
Token Filters
After the tokenization step, the resulting tokens can pass through one or more token filters. A token filter performs operations such as:
- Stemming, which reduces related words, such as "talking", "talked", and "talks" to their root word "talk".
- Redaction, the removal of sensitive information from public documents.

Usage¶

A custom analyzer has the following syntax:

"analyzers": [
  {
    "name": "<name>",
    "charFilters": [ <list-of-character-filters> ],
    "tokenizer": {
      "type": "<tokenizer-type"
    },
    "tokenFilters": [ <list-of-token-filters> ]
  }
]

A custom analyzer has the following attributes:

Attribute	Type	Description	Required?
`name`	string	Name of the custom analyzer. Names must be unique within an index, and may not start with any of the following strings: `lucene.` `builtin.` `mongodb.`	yes
`charFilters`	list of objects	Array containing zero or more character filters.	no
`tokenizer`	object	Tokenizer to use.	yes
`tokenFilters`	list of objects	Array containing zero or more token filters.	no

To use a custom analyzer when indexing a collection, include it in the index definition. In the following example, a custom analyzer named htmlStrippingAnalyzer uses a character filter to remove all HTML tags except a from the text.

{
  "analyzer": "htmlStrippingAnalyzer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "htmlStrippingAnalyzer",
      "charFilters": [
        {
          "type": "htmlStrip",
          "ignoredTags": ["a"]
        }
      ],
      "tokenizer": {
        "type": "standard"
      },
      "tokenFilters": []
    }
  ]
}

Character Filters¶

Character filters always require a type field, and some take additional options as well.

"charFilters": [
  {
    "type": "<filter-type>",
    "<additional-option>": <value>
  }
]

Atlas Search supports four types of character filters:

Type	Description
htmlStrip	Strips out HTML constructs.
icuNormalize	Normalizes text with the ICU Normalizer. Based on Lucene's ICUNormalizer2CharFilter.
mapping	Applies user-specified normalization mappings to characters. Based on Lucene's MappingCharFilter.
persian	Replaces instances of zero-width non-joiner with ordinary space. Based on Lucene's PersianCharFilter.

htmlStrip¶

The htmlStrip character filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this character filter. Must be `htmlStrip`.	yes
`ignoredTags`	array of strings	A list of HTML tags to exclude from filtering.	no

Example

The following example index definition uses a custom analyzer named htmlStrippingAnalyzer. It uses the htmlStrip character filter to remove all HTML tags from the text except the a tag. It uses the standard tokenizer and no token filters.

{
  "analyzer": "htmlStrippingAnalyzer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [{
    "name": "htmlStrippingAnalyzer",
    "charFilters": [{
      "type": "htmlStrip",
      "ignoredTags": ["a"]
    }],
    "tokenizer": {
      "type": "standard"
    },
    "tokenFilters": []
  }]
}

icuNormalize¶

The icuNormalize character filter has the following attribute:

Name	Type	Description	Required?	Default
`type`	string	The type of this character filter. Must be `icuNormalize`.	yes

Example

The following example index definition uses a custom analyzer named normalizingAnalyzer. It uses the icuNormalize character filter, the whitespace tokenizer and no token filters.

{
  "analyzer": "normalizingAnalyzer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "normalizingAnalyzer",
      "charFilters": [
        {
          "type": "icuNormalize"
        }
      ],
      "tokenizer": {
        "type": "whitespace"
      },
      "tokenFilters": []
    }
  ]
}

mapping¶

The mapping character filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this character filter. Must be `mapping`.	yes
`mappings`	object	An object containing a comma-separated list of mappings. A mapping indicates that one character or group of characters should be substituted for another, in the format `<original> : <replacement>`.	yes

Example

The following example index definition uses a custom analyzer named mappingAnalyzer. It uses the mapping character filter to replace instances of \\ with /. It uses the keyword tokenizer and no token filters.

{
  "analyzer": "mappingAnalyzer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "mappingAnalyzer",
      "charFilters": [
        {
          "type": "mapping",
          "mappings": {
            "\\": "/"
          }
        }
      ],
      "tokenizer": {
        "type": "keyword"
      },
      "tokenFilters": []
    }
  ]
}

persian¶

The persian character filter has the following attribute:

Name	Type	Description	Required?	Default
`type`	string	The type of this character filter. Must be `persian`.	yes

Example

The following example index definition uses a custom analyzer named persianCharacterIndex. It uses the persian character filter, the whitespace tokenizer and no token filters.

{
  "analyzer": "persianCharacterIndex",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "persianCharacterIndex",
      "charFilters": [
        {
          "type": "persian"
        }
      ],
      "tokenizer": {
        "type": "whitespace"
      },
      "tokenFilters": []
    }
  ]
}

Tokenizers¶

A custom analyzer's tokenizer determines how Atlas Search splits up text into discrete chunks for indexing.

Tokenizers always require a type field, and some take additional options as well.

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

Atlas Search supports the following tokenizer options:

Name	Description
standard	Tokenize based on word break rules from the Unicode Text Segmentation algorithm.
keyword	Tokenize the entire input as a single token.
whitespace	Tokenize based on occurrences of whitespace between words.
nGram	Tokenize into text chunks, or "n-grams", of given sizes.
edgeGram	Tokenize input from the beginning, or "edge", of a text input into n-grams of given sizes.
regexCaptureGroup	Match a regular expression pattern to extract tokens.
regexSplit	Split tokens with a regular-expression based delimiter.

standard¶

The standard tokenizer has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this tokenizer. Must be `standard`.	yes
`maxTokenLength`	integer	Maximum length for a single token. Tokens greater than this length are split at `maxTokenLength` into multiple tokens.	no	255

Example

The following example index definition uses a custom analyzer named standardShingler. It uses the standard tokenizer and the shingle token filter.

{
  "analyzer": "standardShingler",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "standardShingler",
      "charFilters": [],
      "tokenizer": {
        "type": "standard",
        "maxTokenLength": 10,
      },
      "tokenFilters": [
        {
          "type": "shingle",
          "minSingleSize": 2,
          "maxShingleSize": 3
        }
      ]
    }
  ]
}

keyword¶

The keyword tokenizer has the following attribute:

Name	Type	Description	Required?	Default
`type`	string	The type of this tokenizer. Must be `keyword`.	yes

Example

The following example index definition uses a custom analyzer named keywordTokenizingIndex. It uses the keyword tokenizer and a regular expression token filter that redacts email addresses.

{
  "analyzer": "keywordTokenizingIndex",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "keywordTokenizingIndex",
      "charFilters": [],
      "tokenizer": {
        "type": "keyword"
      },
      "tokenFilters": [
        {
          "type": "regex",
          "pattern": "^([a-z0-9_\\.-]+)@([\\da-z\\.-]+)\\.([a-z\\.]{2,5})$",
          "replacement": "redacted",
          "matches": "all"
        }
      ]
    }
  ]
}

whitespace¶

The whitespace tokenizer has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this tokenizer. Must be `whitespace`.	yes
`maxTokenLength`	integer	Maximum length for a single token. Tokens greater than this length are split at `maxTokenLength` into multiple tokens.	no	255

Example

The following example index definition uses a custom analyzer named whitespaceLowerer. It uses the whitespace tokenizer and a token filter that lowercases all tokens.

{
  "analyzer": "whitespaceLowerer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "whitespaceLowerer",
      "charFilters": [],
      "tokenizer": {
        "type": "whitespace"
      },
      "tokenFilters": [
        {
          "type": "lowercase"
        }
      ]
    }
  ]
}

nGram¶

The nGram tokenizer has the following attributes:

Name	Type	Description	Required?
`type`	string	The type of this tokenizer. Must be `nGram`.	yes
`minGram`	integer	Number of characters to include in the shortest token created.	yes
`maxGram`	integer	Number of characters to include in the longest token created.	yes

Example

The following example index definition uses a custom analyzer named ngramShingler. It uses the nGram tokenizer to create tokens between 2 and 5 characters long and the shingle token filter.

{
  "analyzer": "ngramShingler",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "ngramShingler",
      "charFilters": [],
      "tokenizer": {
        "type": "nGram",
        "minGram": 2,
        "maxGram": 5
      },
      "tokenFilters": [
        {
          "type": "shingle",
          "minSingleSize": 2,
          "maxShingleSize": 3
        }
      ]
    }
  ]
}

edgeGram¶

The edgeGram tokenizer has the following attributes:

Name	Type	Description	Required?
`type`	string	The type of this tokenizer. Must be `edgeGram`.	yes
`minGram`	integer	Number of characters to include in the shortest token created.	yes
`maxGram`	integer	Number of characters to include in the longest token created.	yes

Example

The following example index definition uses a custom analyzer named edgegramShingler. It uses the edgeGram tokenizer to create tokens between 2 and 5 characters long starting from the first character of text input and the shingle token filter.

{
  "analyzer": "edegramShingler",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "edgegramShingler",
      "charFilters": [],
      "tokenizer": {
        "type": "edgeGram",
        "minGram": 2,
        "maxGram": 5
      },
      "tokenFilters": [
        {
          "type": "shingle",
          "minSingleSize": 2,
          "maxShingleSize": 3
        }
      ]
    }
  ]
}

regexCaptureGroup¶

The regexCaptureGroup tokenizer has the following attributes:

Name	Type	Description	Required?
`type`	string	The type of this tokenizer. Must be `regexCaptureGroup`.	yes
`pattern`	string	A regular expression to match against.	yes
`group`	integer	Index of the character group within the matching expression to extract into tokens. Use `0` to extract all character groups.	yes

Example

The following example index definition uses a custom analyzer named phoneNumberExtractor. It uses the regexCaptureGroup tokenizer to creates a single token from the first US-formatted phone number present in the text input.

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

regexSplit¶

The regexSplit tokenizer has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this tokenizer. Must be `regexSplit`.	yes
`pattern`	string	A regular expression to match against.	yes

Example

The following example index definition uses a custom analyzer named dashSplitter. It uses the regexSplit tokenizer to create tokens from hyphen-delimited input text.

{
  "analyzer": "dashSplitter",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "dashSplitter",
      "charFilters": [],
      "tokenizer": {
        "type": "regexSplit",
        "pattern": "[-]+",
      },
      "tokenFilters": []
    }
  ]
}

Token Filters¶

Token Filters always require a type field, and some take additional options as well.

"tokenFilters": [
  {
    "type": "<token-filter-type>",
    "<additional-option>": <value>
  }
]

Atlas Search supports the following token filters:

Name	Description
lowercase	Normalizes token text to lowercase.
length	Removes tokens that are too short or too long.
icuFolding	Applies character folding from Unicode Technical Report #30.
icuNormalizer	Normalizes tokens using a standard Unicode Normalization Mode.
nGram	Tokenizes input into n-grams of configured sizes.
edgeGram	Tokenizes input into edge n-grams of configured sizes.
shingle	Constructs shingles (token n-grams) from a series of tokens.
regex	Applies a regular expression to each token, replacing matches with a specified string.
snowballStemming	Stems tokens using a Snowball-generated stemmer.

lowercase¶

The lowercase token filter has the following attribute:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `lowercase`.	yes

Example

The following example index definition uses a custom analyzer named lowercaser. It uses the standard tokenizer with the lowercase token filter to lowercase all tokens.

{
  "analyzer": "lowercaser",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "lowercaser",
      "charFilters": [],
      "tokenizer": {
        "type": "standard"
      },
      "tokenFilters": [
        {
          "type": "lowercase"
        }
      ]
    }
  ]
}

length¶

The length token filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `length`.	yes
`min`	integer	The minimum length of a token. Must be less than or equal to `max`.	no	0
`max`	integer	The maximum length of a token. Must be greater than or equal to `min`.	no	255

Example

The following example index definition uses a custom analyzer named longOnly. It uses the length token filter to index only tokens that are at least 20 UTF-16 code units long after tokenizing with the standard tokenizer.

{
  "analyzer": "longOnly",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "longOnly",
      "charFilters": [],
      "tokenizer": {
        "type": "standard"
      },
      "tokenFilters": [
        {
          "type": "length",
          "min": 20
        }
      ]
    }
  ]
}

icuFolding¶

The icuFolding token filter has the following attribute:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `icuFolding`.	yes

Example

The following example index definition uses a custom analyzer named diacriticFolder. It uses the keyword tokenizer with the icuFolding token filter to apply foldings from UTR#30 Character Foldings. Foldings include accent removal, case folding, canonical duplicates folding, and many others detailed in the report.

{
  "analyzer": "diacriticFolder",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "diacriticFolder",
      "charFilters": [],
      "tokenizer": {
        "type": "keyword"
      },
      "tokenFilters": [
        {
          "type": "icuFolding",
        }
      ]
    }
  ]
}

icuNormalizer¶

The icuNormalizer token filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `icuNormalizer`.	yes
`normalizationForm`	string	Normalization form to apply. Accepted values are: `nfd` (Canonical Decomposition) `nfc` (Canonical Decomposition, followed by Canonical Composition) `nfkd` (Compatibility Decomposition) `nfkc` (Compatibility Decomposition, followed by Canonical Composition) For more information about the supported normalization forms, see Section 1.2: Normalization Forms, UTR#15.	no	`nfc`

Example

The following example index definition uses a custom analyzer named normalizer. It uses the whitespace tokenizer, then normalizes tokens by Canonical Decomposition, followed by Canonical Composition.

{
  "analyzer": "normalizer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "normalizer",
      "charFilters": [],
      "tokenizer": {
        "type": "whitespace"
      },
      "tokenFilters": [
        {
          "type": "icuNormalizer",
          "normalizationForm": "nfc"
        }
      ]
    }
  ]
}

nGram¶

The nGram token filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `nGram`.	yes
`minGram`	integer	The minimum length of generated n-grams. Must be less than or equal to `maxGram`.	yes
`maxGram`	integer	The maximum length of generated n-grams. Must be greater than or equal to `minGram`.	yes
`termNotInBounds`	string	Accepted values are: `include` `omit` If `include` is specified, tokens shorter than `minGram` or longer than `maxGram` are indexed as-is. If `omit` is specified, those tokens are not indexed.	no	`omit`

Example

The following example index definition uses a custom analyzer named persianAutocomplete. It functions as an autocomplete analyzer for Persian and other languages that use the zero-width non-joiner character. It performs the following operations:

Normalizes zero-width non-joiner characters with the persian character filter.
Tokenizes by whitespace with the whitespace tokenizer.
Applies a series of token filters:
- icuNormalizer
- shingle
- nGram

{
  "analyzer": "persianAutocomplete",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "persianAutocomplete",
      "charFilters": [
        {
          "type": "persian"
        }
      ],
      "tokenizer": {
        "type": "whitespace"
      },
      "tokenFilters": [
        {
          "type": "icuNormalizer",
          "normalizationForm": "nfc"
        },
        {
          "type": "shingle",
          "minShingleSize": 2,
          "maxShingleSize": 3
        },
        {
          "type": "nGram",
          "minGram": 1,
          "maxGram": 10
        }
      ]
    }
  ]
}

edgeGram¶

The edgeGram token filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `edgeGram`.	yes
`minGram`	integer	The minimum length of generated n-grams. Must be less than or equal to `maxGram`.	yes
`maxGram`	integer	The maximum length of generated n-grams. Must be greater than or equal to `minGram`.	yes
`termNotInBounds`	string	Accepted values are: `include` `omit` If `include` is specified, tokens shorter than `minGram` or longer than `maxGram` are indexed as-is. If `omit` is specified, those tokens are not indexed.	no	`omit`

Example

The following example index definition uses a custom analyzer named englishAutocomplete. It performs the following operations:

Tokenizes with the standard tokenizer.
Token filtering with the following filters:
- icuFolding
- shingle
- edgeGram

{
  "analyzer": "englishAutocomplete",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "englishAutocomplete",
      "charFilters": [],
      "tokenizer": {
        "type": "standard"
      },
      "tokenFilters": [
        {
          "type": "icuFolding"
        },
        {
          "type": "shingle",
          "minShingleSize": 2,
          "maxShingleSize": 3
        },
        {
          type: "edgeGram",
          "minGram": 1,
          "maxGram": 10
        }
      ]
    }
  ]
}

shingle¶

The shingle token filter has the following attributes:

Name	Type	Description	Required?
`type`	string	The type of this token filter. Must be `shingle`.	yes
`minShingleSize`	integer	Minimum number of tokens per shingle. Must be less than or equal to `maxShingleSize`.	yes
`maxShingleSize`	integer	Maximum number of tokens per shingle. Must be greater than or equal to `minShingleSize`.	yes

Example

The following example index definition uses a custom analyzer named shingler. It creates shingles of two and three token combinations after tokenizing with the standard tokenizer. minShingleSize is set to 2, so it does not index input when only one token is created from the tokenizer.

{
  "analyzer": "shingler",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "shingler",
      "charFilters": [],
      "tokenizer": {
        "type": "standard"
      },
      "tokenFilters": [
        {
          "type": "shingle",
          "minShingleSize": 2,
          "maxShingleSize": 3
        }
      ]
    }
  ]
}

regex¶

The regex token filter has the following attributes:

Name	Type	Description	Required?
`type`	string	The type of this token filter. Must be `regex`.	yes
`pattern`	string	Regular expression pattern to apply to each token.	yes
`replacement`	string	Replacement string to substitute wherever a matching pattern occurs.	yes
`matches`	string	Acceptable values are: `all` `first` If `matches` is set to `all`, replace all matching patterns. Otherwise, replace only the first matching pattern.	yes

Example

The following example index definition uses a custom analyzer named emailRedact. It uses the keyword tokenizer. It finds strings that look like email addresses and replaces them with the word redacted.

{
  "analyzer": "emailRedact",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "emailRedact",
      "charFilters": [],
      "tokenizer": {
        "type": "keyword"
      },
      "tokenFilters": [
        {
          "type": "regex",
          "pattern": "^([a-z0-9_\\.-]+)@([\\da-z\\.-]+)\\.([a-z\\.]{2,5})$",
          "replacement": "redacted",
          "matches": "all"
        }
      ]
    }
  ]
}

snowballStemming¶

The snowballStemming token filter has the following attributes:

Name	Type	Description	Required?	Default
`type`	string	The type of this token filter. Must be `snowballStemming`.	yes
`stemmerName`	string	The following values are valid: `arabic` `armenian` `basque` `catalan` `danish` `dutch` `english` `finnish` `french` `german` `german2` (Alternative German language stemmer. Handles the umlaut by expanding ü to ue in most contexts.) `hungarian` `irish` `italian` `kp` (Kraaij-Pohlmann stemmer, an alternative stemmer for Dutch.) `lithuanian` `lovins` (The first-ever published "Lovins JB" stemming algorithm.) `norwegian` `porter` (The original Porter English stemming algorithm.) `portuguese` `romanian` `russian` `spanish` `swedish` `turkish`	yes

Example

The following example index definition uses a custom analyzer named frenchStemmer. It uses the lowercase token filter and the standard tokenizer, followed by the french variant of the snowballStemming token filter.

{
  "analyzer": "frenchStemmer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [
    {
      "name": "frenchStemmer",
      "charFilters": [],
      "tokenizer": {
        "type": "standard"
      },
      "tokenFilters": [
        {
          "type": "lowercase"
        },
        {
          "type": "snowballStemming",
          "stemmerName": "french"
        }
      ]
    }
  ]
}

Query Example¶

A collection named minutes contains the following documents:

{ "_id" : 1, "text" : "<head> This page deals with department meetings. </head>" }
{ "_id" : 2, "text" : "The head of the sales department spoke first." }
{ "_id" : 3, "text" : "<body>We'll head out to the conference room by noon.</body>" }

The index definition for the minutes collection uses a custom analyzer with the htmlStrip character filter to strip out HTML tags when searching for text specified in the query field of a search.

{
  "analyzer": "htmlStrippingAnalyzer",
  "mappings": {
    "dynamic": true
  },
  "analyzers": [{
    "name": "htmlStrippingAnalyzer",
    "charFilters": [{
      "type": "htmlStrip",
      "ignoredTags": ["a"]
    }],
    "tokenizer": {
      "type": "standard"
    },
    "tokenFilters": []
  }]
}

The following search operation looks for occurrences of the string head in the text field of the minutes collection.

db.minutes.aggregate([
  {
    $search: {
       text: {
         query: "head",
         path: "text"
       }
    }
  }
])

The query returns the following results:

{ "_id" : 2, "text" : "The head of the sales department spoke first." }
{ "_id" : 3, "text" : "<body>We'll head out to the conference room by noon.</body>" }

The document with _id: 1 is not returned, because the string head is part of the HTML tag <head>. The document with _id: 3 contains HTML tags, but the string head is elsewhere so the document is a match.

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