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Custom Analyzers

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  • Overview
  • Syntax
  • Attributes
  • Usage
  • Example Collection

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.

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. See Usage for more information.
no
tokenizer
object
Tokenizer to use to create tokens. See Usage for more information.
yes
tokenFilters
list of objects
Array containing zero or more token filters. See Usage for more information.
no

To use a custom analyzer when indexing a collection, include the following in your index definition analyzers field:

  1. Optional. Specify one or more character filters. Character filters examine text one character at a time and perform filtering operations.

  2. Required. Specify the 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.

  3. Optional. Specify one or more 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.

Note

The text passes through character filters first, then a tokenizer, and then the token filters.

The Character Filters, Tokenizers, and Token Filters pages contain sample index definitions and query examples for character filters, tokenizers, and token filters. These examples use a sample minutes collection with the following documents:

{
"_id": 1,
"page_updated_by": {
"last_name": "AUERBACH",
"first_name": "Siân",
"email": "auerbach@example.com",
"phone": "123-456-7890"
},
"title": "The weekly team meeting",
"text" : "<head> This page deals with department meetings. </head>"
}
{
"_id": 2,
"page_updated_by": {
"last_name": "OHRBACH",
"first_name": "Noël",
"email": "ohrbach@example.com",
"phone": "123-456-0987"
},
"title": "The check-in with sales team",
"text" : "The head of the sales department spoke first."
}
{
"_id": 3,
"page_updated_by": {
"last_name": "LEWINSKY",
"first_name": "Brièle",
"email": "lewinsky@example.com",
"phone": "123-456-9870"
},
"title": "The regular board meeting",
"text" : "<body>We'll head out to the conference room by noon.</body>"
}
{
"_id": 4,
"page_updated_by": {
"last_name": "LEVINSKI",
"first_name": "François",
"email": "levinski@example.com",
"phone": "123-456-8907"
},
"title": "The daily huddle",
"text" : "<body>The page has been updated with the items on the agenda.</body>"
}
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