Pattern analyzer
The pattern analyzer uses a regular expression to split the text into terms. The regular expression should match the token separators not the tokens themselves. The regular expression defaults to \W+ (or all non-word characters).
Beware of Pathological Regular Expressions
The pattern analyzer uses Java Regular Expressions.
A badly written regular expression could run very slowly or even throw a StackOverflowError and cause the node it is running on to exit suddenly.
Read more about pathological regular expressions and how to avoid them.
Example output
POST _analyze
{
"analyzer": "pattern",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
} The above sentence would produce the following terms:
[ the, 2, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]
Configuration
The pattern analyzer accepts the following parameters:
| | A Java regular expression, defaults to |
| | Java regular expression flags. Flags should be pipe-separated, eg |
| | Should terms be lowercased or not. Defaults to |
| | A pre-defined stop words list like |
| | The path to a file containing stop words. |
See the Stop Token Filter for more information about stop word configuration.
Example configuration
In this example, we configure the pattern analyzer to split email addresses on non-word characters or on underscores (\W|_), and to lower-case the result:
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_email_analyzer": {
"type": "pattern",
"pattern": "\\W|_",
"lowercase": true
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_email_analyzer",
"text": "John_Smith@foo-bar.com"
} | The backslashes in the pattern need to be escaped when specifying the pattern as a JSON string. |
The above example produces the following terms:
[ john, smith, foo, bar, com ]
CamelCase tokenizer
The following more complicated example splits CamelCase text into tokens:
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"camel": {
"type": "pattern",
"pattern": "([^\\p{L}\\d]+)|(?<=\\D)(?=\\d)|(?<=\\d)(?=\\D)|(?<=[\\p{L}&&[^\\p{Lu}]])(?=\\p{Lu})|(?<=\\p{Lu})(?=\\p{Lu}[\\p{L}&&[^\\p{Lu}]])"
}
}
}
}
}
GET my-index-000001/_analyze
{
"analyzer": "camel",
"text": "MooseX::FTPClass2_beta"
} The above example produces the following terms:
[ moose, x, ftp, class, 2, beta ]
The regex above is easier to understand as:
([^\p{L}\d]+) # swallow non letters and numbers,
| (?<=\D)(?=\d) # or non-number followed by number,
| (?<=\d)(?=\D) # or number followed by non-number,
| (?<=[ \p{L} && [^\p{Lu}]]) # or lower case
(?=\p{Lu}) # followed by upper case,
| (?<=\p{Lu}) # or upper case
(?=\p{Lu} # followed by upper case
[\p{L}&&[^\p{Lu}]] # then lower case
) Definition
The pattern analyzer consists of:
- Tokenizer
- Token Filters
-
- Lower Case Token Filter
- Stop Token Filter (disabled by default)
If you need to customize the pattern analyzer beyond the configuration parameters then you need to recreate it as a custom analyzer and modify it, usually by adding token filters. This would recreate the built-in pattern analyzer and you can use it as a starting point for further customization:
PUT /pattern_example
{
"settings": {
"analysis": {
"tokenizer": {
"split_on_non_word": {
"type": "pattern",
"pattern": "\\W+"
}
},
"analyzer": {
"rebuilt_pattern": {
"tokenizer": "split_on_non_word",
"filter": [
"lowercase"
]
}
}
}
}
} | The default pattern is | |
| You’d add other token filters after |
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https://www.elastic.co/guide/en/elasticsearch/reference/7.17/analysis-pattern-analyzer.html