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[DOCS] Rewrite
term query docs for new format (elastic#41498)
* [DOCS] Restructure `term` query docs.
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| Original file line number | Diff line number | Diff line change |
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| [[query-dsl-term-query]] | ||
| === Term Query | ||
|
|
||
| The `term` query finds documents that contain the *exact* term specified | ||
| in the inverted index. For instance: | ||
| Returns documents that contain an *exact* term in a provided field. | ||
|
|
||
| [source,js] | ||
| -------------------------------------------------- | ||
| POST _search | ||
| { | ||
| "query": { | ||
| "term" : { "user" : "Kimchy" } <1> | ||
| } | ||
| } | ||
| -------------------------------------------------- | ||
| // CONSOLE | ||
| <1> Finds documents which contain the exact term `Kimchy` in the inverted index | ||
| of the `user` field. | ||
| You can use the `term` query to find documents based on a precise value such as | ||
| a price, a product ID, or a username. | ||
|
|
||
| [WARNING] | ||
| ==== | ||
| Avoid using the `term` query for <<text, `text`>> fields. | ||
| By default, {es} changes the values of `text` fields as part of <<analysis, | ||
| analysis>>. This can make finding exact matches for `text` field values | ||
| difficult. | ||
| A `boost` parameter can be specified to give this `term` query a higher | ||
| relevance score than another query, for instance: | ||
| To search `text` field values, use the <<query-dsl-match-query,`match`>> query | ||
| instead. | ||
| ==== | ||
|
|
||
| [[term-query-ex-request]] | ||
| ==== Example request | ||
|
|
||
| [source,js] | ||
| -------------------------------------------------- | ||
| GET _search | ||
| ---- | ||
| GET /_search | ||
| { | ||
| "query": { | ||
| "bool": { | ||
| "should": [ | ||
| { | ||
| "term": { | ||
| "status": { | ||
| "value": "urgent", | ||
| "boost": 2.0 <1> | ||
| "query": { | ||
| "term": { | ||
| "user": { | ||
| "value": "Kimchy", | ||
| "boost": 1.0 | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "term": { | ||
| "status": "normal" <2> | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| } | ||
| } | ||
| -------------------------------------------------- | ||
| ---- | ||
| // CONSOLE | ||
|
|
||
| <1> The `urgent` query clause has a boost of `2.0`, meaning it is twice as important | ||
| as the query clause for `normal`. | ||
| <2> The `normal` clause has the default neutral boost of `1.0`. | ||
|
|
||
| A `term` query can also match against <<range, range data types>>. | ||
|
|
||
| .Why doesn't the `term` query match my document? | ||
| ************************************************** | ||
| String fields can be of type `text` (treated as full text, like the body of an | ||
| email), or `keyword` (treated as exact values, like an email address or a | ||
| zip code). Exact values (like numbers, dates, and keywords) have | ||
| the exact value specified in the field added to the inverted index in order | ||
| to make them searchable. | ||
| However, `text` fields are `analyzed`. This means that their | ||
| values are first passed through an <<analysis,analyzer>> to produce a list of | ||
| terms, which are then added to the inverted index. | ||
| There are many ways to analyze text: the default | ||
| <<analysis-standard-analyzer,`standard` analyzer>> drops most punctuation, | ||
| breaks up text into individual words, and lower cases them. For instance, | ||
| the `standard` analyzer would turn the string ``Quick Brown Fox!'' into the | ||
| terms [`quick`, `brown`, `fox`]. | ||
| This analysis process makes it possible to search for individual words | ||
| within a big block of full text. | ||
| The `term` query looks for the *exact* term in the field's inverted index -- | ||
| it doesn't know anything about the field's analyzer. This makes it useful for | ||
| looking up values in keyword fields, or in numeric or date | ||
| fields. When querying full text fields, use the | ||
| <<query-dsl-match-query,`match` query>> instead, which understands how the field | ||
| has been analyzed. | ||
| To demonstrate, try out the example below. First, create an index, specifying the field mappings, and index a document: | ||
| [[term-top-level-params]] | ||
| ==== Top-level parameters for `term` | ||
| `<field>`:: | ||
| Field you wish to search. | ||
|
|
||
| [[term-field-params]] | ||
| ==== Parameters for `<field>` | ||
| `value`:: | ||
| Term you wish to find in the provided `<field>`. To return a document, the term | ||
| must exactly match the field value, including whitespace and capitalization. | ||
|
|
||
| `boost`:: | ||
| Floating point number used to decrease or increase the | ||
| <<query-filter-context, relevance scores>> of a query. Default is `1.0`. | ||
| Optional. | ||
| + | ||
| You can use the `boost` parameter to adjust relevance scores for searches | ||
| containing two or more queries. | ||
| + | ||
| Boost values are relative to the default value of `1.0`. A boost value between | ||
| `0` and `1.0` decreases the relevance score. A value greater than `1.0` | ||
| increases the relevance score. | ||
|
|
||
| [[term-query-notes]] | ||
| ==== Notes | ||
|
|
||
| [[avoid-term-query-text-fields]] | ||
| ===== Avoid using the `term` query for `text` fields | ||
| By default, {es} changes the values of `text` fields during analysis. For | ||
| example, the default <<analysis-standard-analyzer, standard analyzer>> changes | ||
| `text` field values as follows: | ||
|
|
||
| * Removes most punctuation | ||
| * Divides the remaining content into individual words, called | ||
| <<analysis-tokenizers, tokens>> | ||
| * Lowercases the tokens | ||
|
|
||
| To better search `text` fields, the `match` query also analyzes your provided | ||
| search term before performing a search. This means the `match` query can search | ||
| `text` fields for analyzed tokens rather than an exact term. | ||
|
|
||
| The `term` query does *not* analyze the search term. The `term` query only | ||
| searches for the *exact* term you provide. This means the `term` query may | ||
| return poor or no results when searching `text` fields. | ||
|
|
||
| To see the difference in search results, try the following example. | ||
|
|
||
| . Create an index with a `text` field called `full_text`. | ||
| + | ||
| -- | ||
|
|
||
| [source,js] | ||
| -------------------------------------------------- | ||
| ---- | ||
| PUT my_index | ||
| { | ||
| "mappings": { | ||
| "properties": { | ||
| "full_text": { | ||
| "type": "text" <1> | ||
| }, | ||
| "exact_value": { | ||
| "type": "keyword" <2> | ||
| } | ||
| "mappings" : { | ||
| "properties" : { | ||
| "full_text" : { "type" : "text" } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| ---- | ||
| // CONSOLE | ||
|
|
||
| -- | ||
|
|
||
| . Index a document with a value of `Quick Brown Foxes!` in the `full_text` | ||
| field. | ||
| + | ||
| -- | ||
|
|
||
| [source,js] | ||
| ---- | ||
| PUT my_index/_doc/1 | ||
| { | ||
| "full_text": "Quick Foxes!", <3> | ||
| "exact_value": "Quick Foxes!" <4> | ||
| "full_text": "Quick Brown Foxes!" | ||
| } | ||
| -------------------------------------------------- | ||
| ---- | ||
| // CONSOLE | ||
| // TEST[continued] | ||
|
|
||
| Because `full_text` is a `text` field, {es} changes `Quick Brown Foxes!` to | ||
| `[quick, brown, fox]` during analysis. | ||
|
|
||
| <1> The `full_text` field is of type `text` and will be analyzed. | ||
| <2> The `exact_value` field is of type `keyword` and will NOT be analyzed. | ||
| <3> The `full_text` inverted index will contain the terms: [`quick`, `foxes`]. | ||
| <4> The `exact_value` inverted index will contain the exact term: [`Quick Foxes!`]. | ||
| -- | ||
|
|
||
| Now, compare the results for the `term` query and the `match` query: | ||
| . Use the `term` query to search for `Quick Brown Foxes!` in the `full_text` | ||
| field. Include the `pretty` parameter so the response is more readable. | ||
| + | ||
| -- | ||
|
|
||
| [source,js] | ||
| -------------------------------------------------- | ||
| GET my_index/_search | ||
| ---- | ||
| GET my_index/_search?pretty | ||
| { | ||
| "query": { | ||
| "term": { | ||
| "exact_value": "Quick Foxes!" <1> | ||
| "full_text": "Quick Brown Foxes!" | ||
| } | ||
| } | ||
| } | ||
| ---- | ||
| // CONSOLE | ||
| // TEST[continued] | ||
|
|
||
| GET my_index/_search | ||
| { | ||
| "query": { | ||
| "term": { | ||
| "full_text": "Quick Foxes!" <2> | ||
| } | ||
| } | ||
| } | ||
| Because the `full_text` field no longer contains the *exact* term `Quick Brown | ||
| Foxes!`, the `term` query search returns no results. | ||
|
|
||
| GET my_index/_search | ||
| { | ||
| "query": { | ||
| "term": { | ||
| "full_text": "foxes" <3> | ||
| } | ||
| } | ||
| } | ||
| -- | ||
|
|
||
| . Use the `match` query to search for `Quick Brown Foxes!` in the `full_text` | ||
| field. | ||
| + | ||
| -- | ||
|
|
||
| //// | ||
| GET my_index/_search | ||
| [source,js] | ||
| ---- | ||
| POST my_index/_refresh | ||
| ---- | ||
| // CONSOLE | ||
| // TEST[continued] | ||
| //// | ||
|
|
||
| [source,js] | ||
| ---- | ||
| GET my_index/_search?pretty | ||
| { | ||
| "query": { | ||
| "match": { | ||
| "full_text": "Quick Foxes!" <4> | ||
| "full_text": "Quick Brown Foxes!" | ||
| } | ||
| } | ||
| } | ||
| -------------------------------------------------- | ||
| ---- | ||
| // CONSOLE | ||
| // TEST[continued] | ||
|
|
||
| <1> This query matches because the `exact_value` field contains the exact | ||
| term `Quick Foxes!`. | ||
| <2> This query does not match, because the `full_text` field only contains | ||
| the terms `quick` and `foxes`. It does not contain the exact term | ||
| `Quick Foxes!`. | ||
| <3> A `term` query for the term `foxes` matches the `full_text` field. | ||
| <4> This `match` query on the `full_text` field first analyzes the query string, | ||
| then looks for documents containing `quick` or `foxes` or both. | ||
| ************************************************** | ||
| Unlike the `term` query, the `match` query analyzes your provided search term, | ||
| `Quick Brown Foxes!`, before performing a search. The `match` query then returns | ||
| any documents containing the `quick`, `brown`, or `fox` tokens in the | ||
| `full_text` field. | ||
|
|
||
| Here's the response for the `match` query search containing the indexed document | ||
| in the results. | ||
|
|
||
| [source,js] | ||
| ---- | ||
| { | ||
| "took" : 1, | ||
| "timed_out" : false, | ||
| "_shards" : { | ||
| "total" : 1, | ||
| "successful" : 1, | ||
| "skipped" : 0, | ||
| "failed" : 0 | ||
| }, | ||
| "hits" : { | ||
| "total" : { | ||
| "value" : 1, | ||
| "relation" : "eq" | ||
| }, | ||
| "max_score" : 0.8630463, | ||
| "hits" : [ | ||
| { | ||
| "_index" : "my_index", | ||
| "_type" : "_doc", | ||
| "_id" : "1", | ||
| "_score" : 0.8630463, | ||
| "_source" : { | ||
| "full_text" : "Quick Brown Foxes!" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| } | ||
| ---- | ||
| // TESTRESPONSE[s/"took" : 1/"took" : $body.took/] | ||
| -- |