Pattern Analyzeredit

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).

Warning

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 outputedit

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 ]

Configurationedit

The pattern analyzer accepts the following parameters:

pattern

A Java regular expression, defaults to \W+.

flags

Java regular expression flags. Flags should be pipe-separated, eg "CASE_INSENSITIVE|COMMENTS".

lowercase

Should terms be lowercased or not. Defaults to true.

stopwords

A pre-defined stop words list like _english_ or an array containing a list of stop words. Defaults to \_none_.

stopwords_path

The path to a file containing stop words.

See the Stop Token Filter for more information about stop word configuration.

Example configurationedit

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
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_email_analyzer": {
          "type":      "pattern",
          "pattern":   "\\W|_", 
          "lowercase": true
        }
      }
    }
  }
}

POST my_index/_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 tokenizeredit

The following more complicated example splits CamelCase text into tokens:

PUT my_index
{
  "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/_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
  )

Definitionedit

The pattern anlayzer consists of:

Tokenizer
Token Filters

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 \W+ which splits on non-word characters and this is where you’d change it.

You’d add other token filters after lowercase.