133 lines
3.2 KiB
Plaintext
133 lines
3.2 KiB
Plaintext
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[[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 <<mapping-index,indexed>> as an `analyzed` field for full-text
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search, and as a `not_analyzed` 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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"my_type": {
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"properties": {
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"city": {
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"type": "string",
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"fields": {
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"raw": { <1>
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"type": "string",
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"index": "not_analyzed"
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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/my_type/1
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{
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"city": "New York"
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}
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PUT /my_index/my_type/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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// AUTOSENSE
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<1> The `city.raw` field is a `not_analyzed` version of the `city` field.
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<2> The analyzed `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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==== 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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"my_type": {
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"properties": {
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"text": { <1>
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"type": "string"
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},
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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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PUT my_index/my_type/1
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{ "text": "quick brown fox" } <3>
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PUT my_index/my_type/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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// AUTOSENSE
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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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