223 lines
4.9 KiB
Plaintext
223 lines
4.9 KiB
Plaintext
[[query-dsl-term-query]]
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=== Term query
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++++
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<titleabbrev>Term</titleabbrev>
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++++
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Returns documents that contain an *exact* term in a provided field.
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You can use the `term` query to find documents based on a precise value such as
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a price, a product ID, or a username.
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[WARNING]
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====
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Avoid using the `term` query for <<text, `text`>> fields.
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By default, {es} changes the values of `text` fields as part of <<analysis,
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analysis>>. This can make finding exact matches for `text` field values
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difficult.
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To search `text` field values, use the <<query-dsl-match-query,`match`>> query
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instead.
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====
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[[term-query-ex-request]]
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==== Example request
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[source,console]
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----
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GET /_search
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{
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"query": {
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"term": {
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"user.id": {
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"value": "kimchy",
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"boost": 1.0
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}
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}
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}
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}
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----
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[[term-top-level-params]]
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==== Top-level parameters for `term`
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`<field>`::
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(Required, object) Field you wish to search.
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[[term-field-params]]
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==== Parameters for `<field>`
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`value`::
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(Required, string) Term you wish to find in the provided `<field>`. To return a
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document, the term must exactly match the field value, including whitespace and
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capitalization.
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`boost`::
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(Optional, float) Floating point number used to decrease or increase the
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<<relevance-scores,relevance scores>> of a query. Defaults to `1.0`.
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+
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You can use the `boost` parameter to adjust relevance scores for searches
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containing two or more queries.
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+
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Boost values are relative to the default value of `1.0`. A boost value between
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`0` and `1.0` decreases the relevance score. A value greater than `1.0`
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increases the relevance score.
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`case_insensitive`::
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(Optional, Boolean) allows ASCII case insensitive matching of the
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value with the indexed field values when set to true. Default is false which means
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the case sensitivity of matching depends on the underlying field's mapping
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[[term-query-notes]]
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==== Notes
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[[avoid-term-query-text-fields]]
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===== Avoid using the `term` query for `text` fields
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By default, {es} changes the values of `text` fields during analysis. For
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example, the default <<analysis-standard-analyzer, standard analyzer>> changes
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`text` field values as follows:
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* Removes most punctuation
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* Divides the remaining content into individual words, called
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<<analysis-tokenizers, tokens>>
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* Lowercases the tokens
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To better search `text` fields, the `match` query also analyzes your provided
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search term before performing a search. This means the `match` query can search
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`text` fields for analyzed tokens rather than an exact term.
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The `term` query does *not* analyze the search term. The `term` query only
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searches for the *exact* term you provide. This means the `term` query may
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return poor or no results when searching `text` fields.
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To see the difference in search results, try the following example.
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. Create an index with a `text` field called `full_text`.
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+
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--
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[source,console]
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----
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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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"full_text": { "type": "text" }
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}
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}
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}
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----
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--
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. Index a document with a value of `Quick Brown Foxes!` in the `full_text`
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field.
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+
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--
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[source,console]
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----
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PUT my-index-000001/_doc/1
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{
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"full_text": "Quick Brown Foxes!"
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}
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----
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// TEST[continued]
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Because `full_text` is a `text` field, {es} changes `Quick Brown Foxes!` to
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`[quick, brown, fox]` during analysis.
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--
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. Use the `term` query to search for `Quick Brown Foxes!` in the `full_text`
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field. Include the `pretty` parameter so the response is more readable.
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+
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--
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[source,console]
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----
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GET my-index-000001/_search?pretty
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{
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"query": {
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"term": {
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"full_text": "Quick Brown Foxes!"
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}
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}
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}
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----
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// TEST[continued]
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Because the `full_text` field no longer contains the *exact* term `Quick Brown
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Foxes!`, the `term` query search returns no results.
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--
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. Use the `match` query to search for `Quick Brown Foxes!` in the `full_text`
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field.
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+
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--
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////
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[source,console]
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----
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POST my-index-000001/_refresh
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----
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// TEST[continued]
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////
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[source,console]
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----
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GET my-index-000001/_search?pretty
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{
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"query": {
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"match": {
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"full_text": "Quick Brown Foxes!"
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}
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}
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}
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----
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// TEST[continued]
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Unlike the `term` query, the `match` query analyzes your provided search term,
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`Quick Brown Foxes!`, before performing a search. The `match` query then returns
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any documents containing the `quick`, `brown`, or `fox` tokens in the
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`full_text` field.
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Here's the response for the `match` query search containing the indexed document
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in the results.
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[source,console-result]
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----
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{
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"took" : 1,
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"timed_out" : false,
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"_shards" : {
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"total" : 1,
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"successful" : 1,
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"skipped" : 0,
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"failed" : 0
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},
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"hits" : {
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"total" : {
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"value" : 1,
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"relation" : "eq"
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},
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"max_score" : 0.8630463,
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"hits" : [
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{
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"_index" : "my-index-000001",
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"_type" : "_doc",
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"_id" : "1",
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"_score" : 0.8630463,
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"_source" : {
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"full_text" : "Quick Brown Foxes!"
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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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// TESTRESPONSE[s/"took" : 1/"took" : $body.took/]
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--
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