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124 lines
3.7 KiB
Markdown
124 lines
3.7 KiB
Markdown
---
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mapped_pages:
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- https://www.elastic.co/guide/en/elasticsearch/reference/current/multi-fields.html
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---
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# fields [multi-fields]
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It is often useful to index the same field in different ways for different purposes. This is the purpose of *multi-fields*. For instance, a `string` field could be mapped as a `text` field for full-text search, and as a `keyword` field for sorting or aggregations:
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```console
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PUT my-index-000001
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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-000001/_doc/1
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{
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"city": "New York"
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}
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PUT my-index-000001/_doc/2
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{
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"city": "York"
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}
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GET my-index-000001/_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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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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You can add multi-fields to an existing field using the [update mapping API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-put-mapping).
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::::{warning}
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If an index (or data stream) contains documents when you add a multi-field, those documents will not have values for the new multi-field. You can populate the new multi-field with the [update by query API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-update-by-query).
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::::
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A multi-field mapping is completely separate from the parent field’s mapping. A multi-field doesn’t inherit any mapping options from its parent field. Multi-fields don’t change the original `_source` field.
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## Multi-fields with multiple analyzers [_multi_fields_with_multiple_analyzers]
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Another use case of multi-fields is to analyze the same field in different ways for better relevance. For instance we could index a field with the [`standard` analyzer](/reference/text-analysis/analysis-standard-analyzer.md) which breaks text up into words, and again with the [`english` analyzer](/reference/text-analysis/analysis-lang-analyzer.md#english-analyzer) which stems words into their root form:
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```console
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PUT my-index-000001
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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-000001/_doc/1
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{ "text": "quick brown fox" } <3>
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PUT my-index-000001/_doc/2
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{ "text": "quick brown foxes" } <3>
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GET my-index-000001/_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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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 the second document. The `text.english` field contains `fox` for both documents, because `foxes` is stemmed to `fox`.
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The query string is also analyzed by the `standard` analyzer for the `text` field, and by the `english` analyzer for the `text.english` field. The stemmed field allows a query for `foxes` to also match the document containing just `fox`. This allows us to match as many documents as possible. By also querying the unstemmed `text` field, we improve the relevance score of the document which matches `foxes` exactly.
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