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Elastic Docs ›Elasticsearch Guide [master] ›Text analysis ›Built-in analyzer reference

Pattern analyzer

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Pattern analyzer

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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

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resp = client.indices.analyze(
    analyzer="pattern",
    text="The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
)
print(resp)
response = client.indices.analyze(
  body: {
    analyzer: 'pattern',
    text: "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
  }
)
puts response
const response = await client.indices.analyze({
  analyzer: "pattern",
  text: "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
});
console.log(response);
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

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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 configuration

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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:

resp = client.indices.create(
    index="my-index-000001",
    settings={
        "analysis": {
            "analyzer": {
                "my_email_analyzer": {
                    "type": "pattern",
                    "pattern": "\\W|_",
                    "lowercase": True
                }
            }
        }
    },
)
print(resp)

resp1 = client.indices.analyze(
    index="my-index-000001",
    analyzer="my_email_analyzer",
    text="John_Smith@foo-bar.com",
)
print(resp1)
response = client.indices.create(
  index: 'my-index-000001',
  body: {
    settings: {
      analysis: {
        analyzer: {
          my_email_analyzer: {
            type: 'pattern',
            pattern: '\\W|_',
            lowercase: true
          }
        }
      }
    }
  }
)
puts response

response = client.indices.analyze(
  index: 'my-index-000001',
  body: {
    analyzer: 'my_email_analyzer',
    text: 'John_Smith@foo-bar.com'
  }
)
puts response
const response = await client.indices.create({
  index: "my-index-000001",
  settings: {
    analysis: {
      analyzer: {
        my_email_analyzer: {
          type: "pattern",
          pattern: "\\W|_",
          lowercase: true,
        },
      },
    },
  },
});
console.log(response);

const response1 = await client.indices.analyze({
  index: "my-index-000001",
  analyzer: "my_email_analyzer",
  text: "John_Smith@foo-bar.com",
});
console.log(response1);
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

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The following more complicated example splits CamelCase text into tokens:

resp = client.indices.create(
    index="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}]])"
                }
            }
        }
    },
)
print(resp)

resp1 = client.indices.analyze(
    index="my-index-000001",
    analyzer="camel",
    text="MooseX::FTPClass2_beta",
)
print(resp1)
response = client.indices.create(
  index: 'my-index-000001',
  body: {
    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}]])'
          }
        }
      }
    }
  }
)
puts response

response = client.indices.analyze(
  index: 'my-index-000001',
  body: {
    analyzer: 'camel',
    text: 'MooseX::FTPClass2_beta'
  }
)
puts response
const response = await client.indices.create({
  index: "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}]])",
        },
      },
    },
  },
});
console.log(response);

const response1 = await client.indices.analyze({
  index: "my-index-000001",
  analyzer: "camel",
  text: "MooseX::FTPClass2_beta",
});
console.log(response1);
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

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The pattern analyzer 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:

resp = client.indices.create(
    index="pattern_example",
    settings={
        "analysis": {
            "tokenizer": {
                "split_on_non_word": {
                    "type": "pattern",
                    "pattern": "\\W+"
                }
            },
            "analyzer": {
                "rebuilt_pattern": {
                    "tokenizer": "split_on_non_word",
                    "filter": [
                        "lowercase"
                    ]
                }
            }
        }
    },
)
print(resp)
response = client.indices.create(
  index: 'pattern_example',
  body: {
    settings: {
      analysis: {
        tokenizer: {
          split_on_non_word: {
            type: 'pattern',
            pattern: '\\W+'
          }
        },
        analyzer: {
          rebuilt_pattern: {
            tokenizer: 'split_on_non_word',
            filter: [
              'lowercase'
            ]
          }
        }
      }
    }
  }
)
puts response
const response = await client.indices.create({
  index: "pattern_example",
  settings: {
    analysis: {
      tokenizer: {
        split_on_non_word: {
          type: "pattern",
          pattern: "\\W+",
        },
      },
      analyzer: {
        rebuilt_pattern: {
          tokenizer: "split_on_non_word",
          filter: ["lowercase"],
        },
      },
    },
  },
});
console.log(response);
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.
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