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36a3b84fc9
* Default include_type_name to false for get and put mappings. * Default include_type_name to false for get field mappings. * Add a constant for the default include_type_name value. * Default include_type_name to false for get and put index templates. * Default include_type_name to false for create index. * Update create index calls in REST documentation to use include_type_name=true. * Some minor clean-ups around the get index API. * In REST tests, use include_type_name=true by default for index creation. * Make sure to use 'expression == false'. * Clarify the different IndexTemplateMetaData toXContent methods. * Fix FullClusterRestartIT#testSnapshotRestore. * Fix the ml_anomalies_default_mappings test. * Fix GetFieldMappingsResponseTests and GetIndexTemplateResponseTests. We make sure to specify include_type_name=true during xContent parsing, so we continue to test the legacy typed responses. XContent generation for the typeless responses is currently only covered by REST tests, but we will be adding unit test coverage for these as we implement each typeless API in the Java HLRC. This commit also refactors GetMappingsResponse to follow the same appraoch as the other mappings-related responses, where we read include_type_name out of the xContent params, instead of creating a second toXContent method. This gives better consistency in the response parsing code. * Fix more REST tests. * Improve some wording in the create index documentation. * Add a note about types removal in the create index docs. * Fix SmokeTestMonitoringWithSecurityIT#testHTTPExporterWithSSL. * Make sure to mention include_type_name in the REST docs for affected APIs. * Make sure to use 'expression == false' in FullClusterRestartIT. * Mention include_type_name in the REST templates docs.
135 lines
3.4 KiB
Plaintext
135 lines
3.4 KiB
Plaintext
[[multi-fields]]
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=== `fields`
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It is often useful to index the same field in different ways for different
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purposes. This is the purpose of _multi-fields_. For instance, a `string`
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field could be mapped as a `text` field for full-text
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search, and as a `keyword` field for sorting or aggregations:
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[source,js]
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--------------------------------------------------
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PUT my_index?include_type_name=true
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{
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"mappings": {
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"_doc": {
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"properties": {
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"city": {
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"type": "text",
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"fields": {
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"raw": { <1>
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"type": "keyword"
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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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PUT my_index/_doc/1
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{
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"city": "New York"
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}
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PUT my_index/_doc/2
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{
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"city": "York"
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}
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GET my_index/_search
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{
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"query": {
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"match": {
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"city": "york" <2>
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}
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},
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"sort": {
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"city.raw": "asc" <3>
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},
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"aggs": {
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"Cities": {
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"terms": {
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"field": "city.raw" <3>
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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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<1> The `city.raw` field is a `keyword` version of the `city` field.
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<2> The `city` field can be used for full text search.
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<3> The `city.raw` field can be used for sorting and aggregations
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NOTE: Multi-fields do not change the original `_source` field.
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TIP: The `fields` setting is allowed to have different settings for fields of
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the same name in the same index. New multi-fields can be added to existing
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fields using the <<indices-put-mapping,PUT mapping API>>.
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==== Multi-fields with multiple analyzers
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Another use case of multi-fields is to analyze the same field in different
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ways for better relevance. For instance we could index a field with the
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<<analysis-standard-analyzer,`standard` analyzer>> which breaks text up into
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words, and again with the <<english-analyzer,`english` analyzer>>
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which stems words into their root form:
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[source,js]
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--------------------------------------------------
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PUT my_index?include_type_name=true
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{
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"mappings": {
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"_doc": {
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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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PUT my_index/_doc/1
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{ "text": "quick brown fox" } <3>
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PUT my_index/_doc/2
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{ "text": "quick brown foxes" } <3>
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GET my_index/_search
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{
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"query": {
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"multi_match": {
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"query": "quick brown foxes",
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"fields": [ <4>
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"text",
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"text.english"
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],
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"type": "most_fields" <4>
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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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<1> The `text` field uses the `standard` analyzer.
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<2> The `text.english` field uses the `english` analyzer.
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<3> Index two documents, one with `fox` and the other with `foxes`.
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<4> Query both the `text` and `text.english` fields and combine the scores.
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The `text` field contains the term `fox` in the first document and `foxes` in
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the second document. The `text.english` field contains `fox` for both
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documents, because `foxes` is stemmed to `fox`.
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The query string is also analyzed by the `standard` analyzer for the `text`
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field, and by the `english` analyzer for the `text.english` field. The
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stemmed field allows a query for `foxes` to also match the document containing
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just `fox`. This allows us to match as many documents as possible. By also
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querying the unstemmed `text` field, we improve the relevance score of the
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document which matches `foxes` exactly.
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