81 lines
2.7 KiB
Plaintext
81 lines
2.7 KiB
Plaintext
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[[analyzer]]
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=== `analyzer`
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The values of <<mapping-index,`analyzed`>> string fields are passed through an
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<<analysis,analyzer>> to convert the string into a stream of _tokens_ or
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_terms_. For instance, the string `"The quick Brown Foxes."` may, depending
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on which analyzer is used, be analyzed to the tokens: `quick`, `brown`,
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`fox`. These are the actual terms that are indexed for the field, which makes
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it possible to search efficiently for individual words _within_ big blobs of
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text.
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This analysis process needs to happen not just at index time, but also at
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query time: the query string needs to be passed through the same (or a
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similar) analyzer so that the terms that it tries to find are in the same
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format as those that exist in the index.
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Elasticsearch ships with a number of <<analysis-analyzers,pre-defined analyzers>>,
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which can be used without further configuration. It also ships with many
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<<analysis-charfilters,character filters>>, <<analysis-tokenizers,tokenizers>>,
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and <<analysis-tokenfilters>> which can be combined to configure
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custom analyzers per index.
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Analyzers can be specified per-query, per-field or per-index. At index time,
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Elasticsearch will look for an analyzer in this order:
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* The `analyzer` defined in the field mapping.
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* An analyzer named `default` in the index settings.
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* The <<analysis-standard-analyzer,`standard`>> analyzer.
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At query time, there are a few more layers:
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* The `analyzer` defined in a <<full-text-queries,full-text query>>.
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* The `search_analyzer` defined in the field mapping.
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* The `analyzer` defined in the field mapping.
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* An analyzer named `default_search` in the index settings.
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* An analyzer named `default` in the index settings.
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* The <<analysis-standard-analyzer,`standard`>> analyzer.
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The easiest way to specify an analyzer for a particular field is to define it
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in the field mapping, as follows:
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[source,js]
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--------------------------------------------------
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PUT my_index
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{
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"mappings": {
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"my_type": {
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"properties": {
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"text": { <1>
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"type": "string",
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"fields": {
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"english": { <2>
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"type": "string",
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"analyzer": "english"
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}
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}
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}
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}
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}
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}
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}
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GET my_index/_analyze?field=text <3>
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{
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"text": "The quick Brown Foxes."
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}
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GET my_index/_analyze?field=text.english <4>
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{
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"text": "The quick Brown Foxes."
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}
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--------------------------------------------------
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// AUTOSENSE
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<1> The `text` field uses the default `standard` analyzer`.
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<2> The `text.english` <<multi-fields,multi-field>> uses the `english` analyzer, which removes stop words and applies stemming.
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<3> This returns the tokens: [ `the`, `quick`, `brown`, `foxes` ].
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<4> This returns the tokens: [ `quick`, `brown`, `fox` ].
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