363 lines
12 KiB
Plaintext
363 lines
12 KiB
Plaintext
[[query-dsl-script-score-query]]
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=== Script score query
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++++
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<titleabbrev>Script score</titleabbrev>
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++++
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Uses a <<modules-scripting,script>> to provide a custom score for returned
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documents.
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The `script_score` query is useful if, for example, a scoring function is expensive and you only need to calculate the score of a filtered set of documents.
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[[script-score-query-ex-request]]
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==== Example request
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The following `script_score` query assigns each returned document a score equal to the `likes` field value divided by `10`.
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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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"script_score" : {
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"query" : {
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"match": { "message": "elasticsearch" }
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},
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"script" : {
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"source" : "doc['likes'].value / 10 "
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}
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}
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}
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}
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--------------------------------------------------
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[[script-score-top-level-params]]
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==== Top-level parameters for `script_score`
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`query`::
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(Required, query object) Query used to return documents.
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`script`::
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+
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--
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(Required, <<modules-scripting-using,script object>>) Script used to compute the score of documents returned by the `query`.
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IMPORTANT: Final relevance scores from the `script_score` query cannot be
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negative. To support certain search optimizations, Lucene requires
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scores be positive or `0`.
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--
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`min_score`::
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(Optional, float) Documents with a <<relevance-scores,relevance score>> lower
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than this floating point number are excluded from the search results.
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[[script-score-query-notes]]
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==== Notes
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[[script-score-access-scores]]
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===== Use relevance scores in a script
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Within a script, you can
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{ref}/modules-scripting-fields.html#scripting-score[access]
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the `_score` variable which represents the current relevance score of a
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document.
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[[script-score-predefined-functions]]
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===== Predefined functions
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You can use any of the available {painless}/painless-contexts.html[painless
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functions] in your `script`. You can also use the following predefined functions
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to customize scoring:
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* <<script-score-saturation>>
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* <<script-score-sigmoid>>
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* <<random-score-function>>
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* <<decay-functions-numeric-fields>>
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* <<decay-functions-geo-fields>>
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* <<decay-functions-date-fields>>
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* <<script-score-functions-vector-fields>>
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We suggest using these predefined functions instead of writing your own.
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These functions take advantage of efficiencies from {es}' internal mechanisms.
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[[script-score-saturation]]
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====== Saturation
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`saturation(value,k) = value/(k + value)`
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "saturation(doc['likes'].value, 1)"
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}
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--------------------------------------------------
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// NOTCONSOLE
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[[script-score-sigmoid]]
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====== Sigmoid
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`sigmoid(value, k, a) = value^a/ (k^a + value^a)`
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "sigmoid(doc['likes'].value, 2, 1)"
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}
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--------------------------------------------------
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// NOTCONSOLE
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[[random-score-function]]
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====== Random score function
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`random_score` function generates scores that are uniformly distributed
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from 0 up to but not including 1.
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`randomScore` function has the following syntax:
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`randomScore(<seed>, <fieldName>)`.
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It has a required parameter - `seed` as an integer value,
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and an optional parameter - `fieldName` as a string value.
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "randomScore(100, '_seq_no')"
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}
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--------------------------------------------------
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// NOTCONSOLE
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If the `fieldName` parameter is omitted, the internal Lucene
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document ids will be used as a source of randomness. This is very efficient,
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but unfortunately not reproducible since documents might be renumbered
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by merges.
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "randomScore(100)"
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}
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--------------------------------------------------
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// NOTCONSOLE
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Note that documents that are within the same shard and have the
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same value for field will get the same score, so it is usually desirable
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to use a field that has unique values for all documents across a shard.
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A good default choice might be to use the `_seq_no`
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field, whose only drawback is that scores will change if the document is
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updated since update operations also update the value of the `_seq_no` field.
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[[decay-functions-numeric-fields]]
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====== Decay functions for numeric fields
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You can read more about decay functions
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{ref}/query-dsl-function-score-query.html#function-decay[here].
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* `double decayNumericLinear(double origin, double scale, double offset, double decay, double docValue)`
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* `double decayNumericExp(double origin, double scale, double offset, double decay, double docValue)`
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* `double decayNumericGauss(double origin, double scale, double offset, double decay, double docValue)`
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "decayNumericLinear(params.origin, params.scale, params.offset, params.decay, doc['dval'].value)",
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"params": { <1>
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"origin": 20,
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"scale": 10,
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"decay" : 0.5,
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"offset" : 0
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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<1> Using `params` allows to compile the script only once, even if params change.
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[[decay-functions-geo-fields]]
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====== Decay functions for geo fields
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* `double decayGeoLinear(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)`
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* `double decayGeoExp(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)`
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* `double decayGeoGauss(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)`
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "decayGeoExp(params.origin, params.scale, params.offset, params.decay, doc['location'].value)",
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"params": {
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"origin": "40, -70.12",
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"scale": "200km",
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"offset": "0km",
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"decay" : 0.2
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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[[decay-functions-date-fields]]
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====== Decay functions for date fields
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* `double decayDateLinear(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)`
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* `double decayDateExp(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)`
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* `double decayDateGauss(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)`
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "decayDateGauss(params.origin, params.scale, params.offset, params.decay, doc['date'].value)",
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"params": {
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"origin": "2008-01-01T01:00:00Z",
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"scale": "1h",
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"offset" : "0",
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"decay" : 0.5
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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NOTE: Decay functions on dates are limited to dates in the default format
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and default time zone. Also calculations with `now` are not supported.
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[[script-score-functions-vector-fields]]
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====== Functions for vector fields
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<<vector-functions, Functions for vector fields>> are accessible through
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`script_score` query.
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[[script-score-faster-alt]]
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===== Faster alternatives
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The `script_score` query calculates the score for
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every matching document, or hit. There are faster alternative query types that
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can efficiently skip non-competitive hits:
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* If you want to boost documents on some static fields, use the
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<<query-dsl-rank-feature-query, `rank_feature`>> query.
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* If you want to boost documents closer to a date or geographic point, use the
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<<query-dsl-distance-feature-query, `distance_feature`>> query.
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[[script-score-function-score-transition]]
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===== Transition from the function score query
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We are deprecating the <<query-dsl-function-score-query, `function_score`>>
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query. We recommend using the `script_score` query instead.
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You can implement the following functions from the `function_score` query using
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the `script_score` query:
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* <<script-score>>
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* <<weight>>
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* <<random-score>>
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* <<field-value-factor>>
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* <<decay-functions>>
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[[script-score]]
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====== `script_score`
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What you used in `script_score` of the Function Score query, you
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can copy into the Script Score query. No changes here.
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[[weight]]
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====== `weight`
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`weight` function can be implemented in the Script Score query through
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the following script:
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "params.weight * _score",
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"params": {
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"weight": 2
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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[[random-score]]
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====== `random_score`
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Use `randomScore` function
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as described in <<random-score-function, random score function>>.
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[[field-value-factor]]
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====== `field_value_factor`
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`field_value_factor` function can be easily implemented through script:
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "Math.log10(doc['field'].value * params.factor)",
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params" : {
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"factor" : 5
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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For checking if a document has a missing value, you can use
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`doc['field'].size() == 0`. For example, this script will use
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a value `1` if a document doesn't have a field `field`:
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[source,js]
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--------------------------------------------------
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"script" : {
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"source" : "Math.log10((doc['field'].size() == 0 ? 1 : doc['field'].value()) * params.factor)",
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params" : {
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"factor" : 5
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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This table lists how `field_value_factor` modifiers can be implemented
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through a script:
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[cols="<,<",options="header",]
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|=======================================================================
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| Modifier | Implementation in Script Score
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| `none` | -
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| `log` | `Math.log10(doc['f'].value)`
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| `log1p` | `Math.log10(doc['f'].value + 1)`
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| `log2p` | `Math.log10(doc['f'].value + 2)`
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| `ln` | `Math.log(doc['f'].value)`
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| `ln1p` | `Math.log(doc['f'].value + 1)`
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| `ln2p` | `Math.log(doc['f'].value + 2)`
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| `square` | `Math.pow(doc['f'].value, 2)`
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| `sqrt` | `Math.sqrt(doc['f'].value)`
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| `reciprocal` | `1.0 / doc['f'].value`
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|=======================================================================
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[[decay-functions]]
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====== `decay` functions
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The `script_score` query has equivalent <<decay-functions, decay functions>>
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that can be used in script.
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include::{es-repo-dir}/vectors/vector-functions.asciidoc[]
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[[score-explanation]]
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====== Explain request
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Using an <<search-explain, explain request>> provides an explanation of how the parts of a score were computed. The `script_score` query can add its own explanation by setting the `explanation` parameter:
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[source,console]
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--------------------------------------------------
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GET /twitter/_explain/0
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{
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"query" : {
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"script_score" : {
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"query" : {
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"match": { "message": "elasticsearch" }
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},
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"script" : {
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"source" : """
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long likes = doc['likes'].value;
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double normalizedLikes = likes / 10;
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if (explanation != null) {
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explanation.set('normalized likes = likes / 10 = ' + likes + ' / 10 = ' + normalizedLikes);
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}
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return normalizedLikes;
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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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// TEST[setup:twitter]
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Note that the `explanation` will be null when using in a normal `_search` request, so having a conditional guard is best practice.
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