173 lines
5.9 KiB
Plaintext
173 lines
5.9 KiB
Plaintext
[[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": "text",
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"fields": {
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"english": { <2>
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"type": "text",
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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 <3>
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{
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"field": "text",
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"text": "The quick Brown Foxes."
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}
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GET my_index/_analyze <4>
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{
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"field": "text.english",
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"text": "The quick Brown Foxes."
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}
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--------------------------------------------------
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// CONSOLE
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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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[[search-quote-analyzer]]
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==== `search_quote_analyzer`
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The `search_quote_analyzer` setting allows you to specify an analyzer for phrases, this is particularly useful when dealing with disabling
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stop words for phrase queries.
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To disable stop words for phrases a field utilising three analyzer settings will be required:
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1. An `analyzer` setting for indexing all terms including stop words
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2. A `search_analyzer` setting for non-phrase queries that will remove stop words
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3. A `search_quote_analyzer` setting for phrase queries that will not remove stop words
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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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"settings":{
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"analysis":{
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"analyzer":{
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"my_analyzer":{ <1>
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"type":"custom",
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"tokenizer":"standard",
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"filter":[
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"lowercase"
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]
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},
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"my_stop_analyzer":{ <2>
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"type":"custom",
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"tokenizer":"standard",
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"filter":[
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"lowercase",
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"english_stop"
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]
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}
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},
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"filter":{
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"english_stop":{
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"type":"stop",
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"stopwords":"_english_"
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}
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}
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}
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},
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"mappings":{
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"my_type":{
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"properties":{
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"title": {
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"type":"text",
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"analyzer":"my_analyzer", <3>
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"search_analyzer":"my_stop_analyzer", <4>
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"search_quote_analyzer":"my_analyzer" <5>
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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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// CONSOLE
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[source,js]
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--------------------------------------------------
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PUT my_index/my_type/1
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{
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"title":"The Quick Brown Fox"
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}
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PUT my_index/my_type/2
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{
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"title":"A Quick Brown Fox"
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}
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GET my_index/my_type/_search
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{
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"query":{
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"query_string":{
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"query":"\"the quick brown fox\"" <6>
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}
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}
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}
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--------------------------------------------------
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<1> `my_analyzer` analyzer which tokens all terms including stop words
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<2> `my_stop_analyzer` analyzer which removes stop words
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<3> `analyzer` setting that points to the `my_analyzer` analyzer which will be used at index time
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<4> `search_analyzer` setting that points to the `my_stop_analyzer` and removes stop words for non-phrase queries
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<5> `search_quote_analyzer` setting that points to the `my_analyzer` analyzer and ensures that stop words are not removed from phrase queries
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<6> Since the query is wrapped in quotes it is detected as a phrase query therefore the `search_quote_analyzer` kicks in and ensures the stop words
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are not removed from the query. The `my_analyzer` analyzer will then return the following tokens [`the`, `quick`, `brown`, `fox`] which will match one
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of the documents. Meanwhile term queries will be analyzed with the `my_stop_analyzer` analyzer which will filter out stop words. So a search for either
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`The quick brown fox` or `A quick brown fox` will return both documents since both documents contain the following tokens [`quick`, `brown`, `fox`].
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Without the `search_quote_analyzer` it would not be possible to do exact matches for phrase queries as the stop words from phrase queries would be
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removed resulting in both documents matching.
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