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63 lines
2.2 KiB
Markdown
63 lines
2.2 KiB
Markdown
---
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navigation_title: "Simple pattern split"
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mapped_pages:
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- https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-simplepatternsplit-tokenizer.html
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---
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# Simple pattern split tokenizer [analysis-simplepatternsplit-tokenizer]
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The `simple_pattern_split` tokenizer uses a regular expression to split the input into terms at pattern matches. The set of regular expression features it supports is more limited than the [`pattern`](/reference/text-analysis/analysis-pattern-tokenizer.md) tokenizer, but the tokenization is generally faster.
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This tokenizer does not produce terms from the matches themselves. To produce terms from matches using patterns in the same restricted regular expression subset, see the [`simple_pattern`](/reference/text-analysis/analysis-simplepattern-tokenizer.md) tokenizer.
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This tokenizer uses [Lucene regular expressions](https://lucene.apache.org/core/10_0_0/core/org/apache/lucene/util/automaton/RegExp.html). For an explanation of the supported features and syntax, see [Regular Expression Syntax](/reference/query-languages/query-dsl/regexp-syntax.md).
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The default pattern is the empty string, which produces one term containing the full input. This tokenizer should always be configured with a non-default pattern.
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## Configuration [_configuration_18]
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The `simple_pattern_split` tokenizer accepts the following parameters:
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`pattern`
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: A [Lucene regular expression](https://lucene.apache.org/core/10_0_0/core/org/apache/lucene/util/automaton/RegExp.html), defaults to the empty string.
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## Example configuration [_example_configuration_12]
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This example configures the `simple_pattern_split` tokenizer to split the input text on underscores.
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```console
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PUT my-index-000001
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"my_analyzer": {
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"tokenizer": "my_tokenizer"
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}
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},
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"tokenizer": {
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"my_tokenizer": {
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"type": "simple_pattern_split",
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"pattern": "_"
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}
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}
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}
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}
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}
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POST my-index-000001/_analyze
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{
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"analyzer": "my_analyzer",
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"text": "an_underscored_phrase"
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}
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```
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The above example produces these terms:
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```text
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[ an, underscored, phrase ]
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```
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