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https://github.com/honeymoose/OpenSearch.git
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d684119618
Together with types removal, any mention of "fields with the same name in the same index" doesn't make sense anymore. (cherry picked from commit c5190106cbd4c007945156249cce462956933326)
130 lines
3.2 KiB
Plaintext
130 lines
3.2 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
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{
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"mappings": {
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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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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: 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
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{
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"mappings": {
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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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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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