OpenSearch/docs/reference/mapping/params/analyzer.asciidoc

[[analyzer]]
=== `analyzer`

The values of <<mapping-index,`analyzed`>> string fields are passed through an
<<analysis,analyzer>> to convert the string into a stream of _tokens_ or
_terms_.  For instance, the string `"The quick Brown Foxes."` may, depending
on which analyzer is used,  be analyzed to the tokens: `quick`, `brown`,
`fox`.  These are the actual terms that are indexed for the field, which makes
it possible to search efficiently for individual words _within_  big blobs of
text.

This analysis process needs to happen not just at index time, but also at
query time: the query string needs to be passed through the same (or a
similar) analyzer so that the terms that it tries to find are in the same
format as those that exist in the index.

Elasticsearch ships with a number of <<analysis-analyzers,pre-defined analyzers>>,
which can be used without further configuration.  It also ships with many
<<analysis-charfilters,character filters>>, <<analysis-tokenizers,tokenizers>>,
and <<analysis-tokenfilters>> which can be combined to configure
custom analyzers per index.

Analyzers can be specified per-query, per-field or per-index. At index time,
Elasticsearch will look for an analyzer in this order:

* The `analyzer` defined in the field mapping.
* An analyzer named `default` in the index settings.
* The <<analysis-standard-analyzer,`standard`>> analyzer.

At query time, there are a few more layers:

* The `analyzer` defined in a <<full-text-queries,full-text query>>.
* The `search_analyzer` defined in the field mapping.
* The `analyzer` defined in the field mapping.
* An analyzer named `default_search` in the index settings.
* An analyzer named `default` in the index settings.
* The <<analysis-standard-analyzer,`standard`>> analyzer.

The easiest way to specify an analyzer for a particular field is to define it
in the field mapping, as follows:

[source,js]
--------------------------------------------------
PUT /my_index?include_type_name=true
{
  "mappings": {
    "_doc": {
      "properties": {
        "text": { <1>
          "type": "text",
          "fields": {
            "english": { <2>
              "type":     "text",
              "analyzer": "english"
            }
          }
        }
      }
    }
  }
}

GET my_index/_analyze <3>
{
  "field": "text",
  "text": "The quick Brown Foxes."
}

GET my_index/_analyze <4>
{
  "field": "text.english",
  "text": "The quick Brown Foxes."
}
--------------------------------------------------
// CONSOLE
<1> The `text` field uses the default `standard` analyzer`.
<2> The `text.english` <<multi-fields,multi-field>> uses the `english` analyzer, which removes stop words and applies stemming.
<3> This returns the tokens: [ `the`, `quick`, `brown`, `foxes` ].
<4> This returns the tokens: [ `quick`, `brown`, `fox` ].


[[search-quote-analyzer]]
==== `search_quote_analyzer`

The `search_quote_analyzer` setting allows you to specify an analyzer for phrases, this is particularly useful when dealing with disabling
stop words for phrase queries.

To disable stop words for phrases a field utilising three analyzer settings will be required:

1. An `analyzer` setting for indexing all terms including stop words
2. A `search_analyzer` setting for non-phrase queries that will remove stop words
3. A `search_quote_analyzer` setting for phrase queries that will not remove stop words

[source,js]
--------------------------------------------------
PUT my_index?include_type_name=true
{
   "settings":{
      "analysis":{
         "analyzer":{
            "my_analyzer":{ <1>
               "type":"custom",
               "tokenizer":"standard",
               "filter":[
                  "lowercase"
               ]
            },
            "my_stop_analyzer":{ <2>
               "type":"custom",
               "tokenizer":"standard",
               "filter":[
                  "lowercase",
                  "english_stop"
               ]
            }
         },
         "filter":{
            "english_stop":{
               "type":"stop",
               "stopwords":"_english_"
            }
         }
      }
   },
   "mappings":{
      "_doc":{
         "properties":{
            "title": {
               "type":"text",
               "analyzer":"my_analyzer", <3>
               "search_analyzer":"my_stop_analyzer", <4>
               "search_quote_analyzer":"my_analyzer" <5>
            }
         }
      }
   }
}

PUT my_index/_doc/1
{
   "title":"The Quick Brown Fox"
}

PUT my_index/_doc/2
{
   "title":"A Quick Brown Fox"
}

GET my_index/_search
{
   "query":{
      "query_string":{
         "query":"\"the quick brown fox\"" <6>
      }
   }
}
--------------------------------------------------
// CONSOLE
<1> `my_analyzer` analyzer which tokens all terms including stop words
<2> `my_stop_analyzer` analyzer which removes stop words
<3> `analyzer` setting that points to the `my_analyzer` analyzer which will be used at index time
<4> `search_analyzer` setting that points to the `my_stop_analyzer` and removes stop words for non-phrase queries
<5> `search_quote_analyzer` setting that points to the `my_analyzer` analyzer and ensures that stop words are not removed from phrase queries
<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
are not removed from the query. The `my_analyzer` analyzer will then return the following tokens [`the`, `quick`, `brown`, `fox`] which will match one
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
`The quick brown fox` or `A quick brown fox` will return both documents since both documents contain the following tokens [`quick`, `brown`, `fox`].
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
removed resulting in both documents matching.