571 lines
19 KiB
Plaintext
571 lines
19 KiB
Plaintext
[[query-dsl-function-score-query]]
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=== Function Score Query
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The `function_score` allows you to modify the score of documents that are
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retrieved by a query. This can be useful if, for example, a score
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function is computationally expensive and it is sufficient to compute
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the score on a filtered set of documents.
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To use `function_score`, the user has to define a query and one or
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more functions, that compute a new score for each document returned
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by the query.
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`function_score` can be used with only one function like this:
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[source,js]
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--------------------------------------------------
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GET /_search
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{
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"query": {
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"function_score": {
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"query": {},
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"boost": "5",
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"random_score": {}, <1>
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"boost_mode":"multiply"
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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<1> See <<score-functions>> for a list of supported functions.
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Furthermore, several functions can be combined. In this case one can
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optionally choose to apply the function only if a document matches a
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given filtering query
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[source,js]
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--------------------------------------------------
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GET /_search
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{
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"query": {
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"function_score": {
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"query": {},
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"boost": "5", <1>
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"functions": [
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{
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"filter": {},
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"random_score": {}, <2>
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"weight": 23
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},
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{
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"filter": {},
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"weight": 42
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}
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],
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"max_boost": 42,
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"score_mode": "max",
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"boost_mode": "multiply",
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"min_score" : 42
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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<1> Boost for the whole query.
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<2> See <<score-functions>> for a list of supported functions.
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NOTE: The scores produced by the filtering query of each function do not matter.
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If no filter is given with a function this is equivalent to specifying
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`"match_all": {}`
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First, each document is scored by the defined functions. The parameter
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`score_mode` specifies how the computed scores are combined:
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[horizontal]
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`multiply`:: scores are multiplied (default)
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`sum`:: scores are summed
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`avg`:: scores are averaged
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`first`:: the first function that has a matching filter
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is applied
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`max`:: maximum score is used
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`min`:: minimum score is used
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Because scores can be on different scales (for example, between 0 and 1 for decay functions but arbitrary for `field_value_factor`) and also
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because sometimes a different impact of functions on the score is desirable, the score of each function can be adjusted with a user defined
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`weight`. The `weight` can be defined per function in the `functions` array (example above) and is multiplied with the score computed by
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the respective function.
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If weight is given without any other function declaration, `weight` acts as a function that simply returns the `weight`.
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In case `score_mode` is set to `avg` the individual scores will be combined by a **weighted** average.
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For example, if two functions return score 1 and 2 and their respective weights are 3 and 4, then their scores will be combined as
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`(1*3+2*4)/(3+4)` and **not** `(1*3+2*4)/2`.
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The new score can be restricted to not exceed a certain limit by setting
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the `max_boost` parameter. The default for `max_boost` is FLT_MAX.
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The newly computed score is combined with the score of the
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query. The parameter `boost_mode` defines how:
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[horizontal]
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`multiply`:: query score and function score is multiplied (default)
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`replace`:: only function score is used, the query score is ignored
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`sum`:: query score and function score are added
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`avg`:: average
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`max`:: max of query score and function score
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`min`:: min of query score and function score
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By default, modifying the score does not change which documents match. To exclude
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documents that do not meet a certain score threshold the `min_score` parameter can be set to the desired score threshold.
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[[score-functions]]
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The `function_score` query provides several types of score functions.
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* <<function-script-score,`script_score`>>
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* <<function-weight,`weight`>>
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* <<function-random,`random_score`>>
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* <<function-field-value-factor,`field_value_factor`>>
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* <<function-decay,decay functions>>: `gauss`, `linear`, `exp`
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[[function-script-score]]
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==== Script score
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The `script_score` function allows you to wrap another query and customize
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the scoring of it optionally with a computation derived from other numeric
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field values in the doc using a script expression. Here is a
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simple sample:
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[source,js]
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--------------------------------------------------
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"script_score" : {
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"script" : {
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"lang": "painless",
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"inline": "_score * doc['my_numeric_field'].value"
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}
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}
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--------------------------------------------------
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On top of the different scripting field values and expression, the
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`_score` script parameter can be used to retrieve the score based on the
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wrapped query.
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Scripts are cached for faster execution. If the script has parameters
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that it needs to take into account, it is preferable to reuse the same
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script, and provide parameters to it:
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[source,js]
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--------------------------------------------------
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"script_score": {
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"script": {
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"lang": "painless",
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"params": {
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"param1": value1,
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"param2": value2
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},
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"inline": "_score * doc['my_numeric_field'].value / Math.pow(params.param1, params.param2)"
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}
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}
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--------------------------------------------------
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Note that unlike the `custom_score` query, the
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score of the query is multiplied with the result of the script scoring. If
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you wish to inhibit this, set `"boost_mode": "replace"`
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[[function-weight]]
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==== Weight
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The `weight` score allows you to multiply the score by the provided
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`weight`. This can sometimes be desired since boost value set on
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specific queries gets normalized, while for this score function it does
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not. The number value is of type float.
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[source,js]
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--------------------------------------------------
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"weight" : number
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--------------------------------------------------
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[[function-random]]
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==== Random
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The `random_score` generates scores using a hash of the `_uid` field,
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with a `seed` for variation. If `seed` is not specified, the current
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time is used.
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NOTE: Using this feature will load field data for `_uid`, which can
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be a memory intensive operation since the values are unique.
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[source,js]
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--------------------------------------------------
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"random_score": {
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"seed" : number
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}
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--------------------------------------------------
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[[function-field-value-factor]]
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==== Field Value factor
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The `field_value_factor` function allows you to use a field from a document to
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influence the score. It's similar to using the `script_score` function, however,
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it avoids the overhead of scripting. If used on a multi-valued field, only the
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first value of the field is used in calculations.
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As an example, imagine you have a document indexed with a numeric `popularity`
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field and wish to influence the score of a document with this field, an example
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doing so would look like:
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[source,js]
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--------------------------------------------------
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"field_value_factor": {
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"field": "popularity",
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"factor": 1.2,
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"modifier": "sqrt",
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"missing": 1
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}
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--------------------------------------------------
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Which will translate into the following formula for scoring:
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`sqrt(1.2 * doc['popularity'].value)`
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There are a number of options for the `field_value_factor` function:
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[horizontal]
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`field`::
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Field to be extracted from the document.
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`factor`::
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Optional factor to multiply the field value with, defaults to `1`.
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`modifier`::
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Modifier to apply to the field value, can be one of: `none`, `log`,
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`log1p`, `log2p`, `ln`, `ln1p`, `ln2p`, `square`, `sqrt`, or `reciprocal`.
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Defaults to `none`.
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[cols="<,<",options="header",]
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|=======================================================================
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| Modifier | Meaning
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| `none` | Do not apply any multiplier to the field value
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| `log` | Take the https://en.wikipedia.org/wiki/Logarithm[logarithm] of the field value
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| `log1p` | Add 1 to the field value and take the logarithm
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| `log2p` | Add 2 to the field value and take the logarithm
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| `ln` | Take the https://en.wikipedia.org/wiki/Natural_logarithm[natural logarithm] of the field value
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| `ln1p` | Add 1 to the field value and take the natural logarithm
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| `ln2p` | Add 2 to the field value and take the natural logarithm
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| `square` | Square the field value (multiply it by itself)
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| `sqrt` | Take the https://en.wikipedia.org/wiki/Square_root[square root] of the field value
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| `reciprocal` | https://en.wikipedia.org/wiki/Multiplicative_inverse[Reciprocate] the field value, same as `1/x` where `x` is the field's value
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|=======================================================================
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`missing`::
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Value used if the document doesn't have that field. The modifier
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and factor are still applied to it as though it were read from the document.
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Keep in mind that taking the log() of 0, or the square root of a negative number
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is an illegal operation, and an exception will be thrown. Be sure to limit the
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values of the field with a range filter to avoid this, or use `log1p` and
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`ln1p`.
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[[function-decay]]
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==== Decay functions
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Decay functions score a document with a function that decays depending
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on the distance of a numeric field value of the document from a user
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given origin. This is similar to a range query, but with smooth edges
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instead of boxes.
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To use distance scoring on a query that has numerical fields, the user
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has to define an `origin` and a `scale` for each field. The `origin`
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is needed to define the ``central point'' from which the distance
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is calculated, and the `scale` to define the rate of decay. The
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decay function is specified as
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[source,js]
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--------------------------------------------------
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"DECAY_FUNCTION": { <1>
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"FIELD_NAME": { <2>
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"origin": "11, 12",
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"scale": "2km",
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"offset": "0km",
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"decay": 0.33
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}
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}
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--------------------------------------------------
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<1> The `DECAY_FUNCTION` should be one of `linear`, `exp`, or `gauss`.
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<2> The specified field must be a numeric, date, or geo-point field.
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In the above example, the field is a <<geo-point,`geo_point`>> and origin can be provided in geo format. `scale` and `offset` must be given with a unit in this case. If your field is a date field, you can set `scale` and `offset` as days, weeks, and so on. Example:
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[source,js]
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--------------------------------------------------
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"gauss": {
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"date": {
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"origin": "2013-09-17", <1>
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"scale": "10d",
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"offset": "5d", <2>
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"decay" : 0.5 <2>
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}
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}
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--------------------------------------------------
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<1> The date format of the origin depends on the <<mapping-date-format,`format`>> defined in
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your mapping. If you do not define the origin, the current time is used.
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<2> The `offset` and `decay` parameters are optional.
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[horizontal]
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`origin`::
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The point of origin used for calculating distance. Must be given as a
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number for numeric field, date for date fields and geo point for geo fields.
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Required for geo and numeric field. For date fields the default is `now`. Date
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math (for example `now-1h`) is supported for origin.
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`scale`::
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Required for all types. Defines the distance from origin at which the computed
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score will equal `decay` parameter. For geo fields: Can be defined as number+unit (1km, 12m,...).
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Default unit is meters. For date fields: Can to be defined as a number+unit ("1h", "10d",...).
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Default unit is milliseconds. For numeric field: Any number.
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`offset`::
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If an `offset` is defined, the decay function will only compute the
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decay function for documents with a distance greater that the defined
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`offset`. The default is 0.
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`decay`::
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The `decay` parameter defines how documents are scored at the distance
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given at `scale`. If no `decay` is defined, documents at the distance
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`scale` will be scored 0.5.
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In the first example, your documents might represents hotels and contain a geo
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location field. You want to compute a decay function depending on how
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far the hotel is from a given location. You might not immediately see
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what scale to choose for the gauss function, but you can say something
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like: "At a distance of 2km from the desired location, the score should
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be reduced to one third."
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The parameter "scale" will then be adjusted automatically to assure that
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the score function computes a score of 0.33 for hotels that are 2km away
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from the desired location.
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In the second example, documents with a field value between 2013-09-12 and 2013-09-22 would get a weight of 1.0 and documents which are 15 days from that date a weight of 0.5.
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===== Supported decay functions
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The `DECAY_FUNCTION` determines the shape of the decay:
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`gauss`::
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+
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--
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Normal decay, computed as:
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image:images/Gaussian.png[]
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where image:images/sigma.png[] is computed to assure that the score takes the value `decay` at distance `scale` from `origin`+-`offset`
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image:images/sigma_calc.png[]
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See <<gauss-decay>> for graphs demonstrating the curve generated by the `gauss` function.
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--
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`exp`::
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+
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--
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Exponential decay, computed as:
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image:images/Exponential.png[]
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where again the parameter image:images/lambda.png[] is computed to assure that the score takes the value `decay` at distance `scale` from `origin`+-`offset`
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image:images/lambda_calc.png[]
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See <<exp-decay>> for graphs demonstrating the curve generated by the `exp` function.
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--
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`linear`::
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+
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--
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Linear decay, computed as:
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image:images/Linear.png[].
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where again the parameter `s` is computed to assure that the score takes the value `decay` at distance `scale` from `origin`+-`offset`
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image:images/s_calc.png[]
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In contrast to the normal and exponential decay, this function actually
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sets the score to 0 if the field value exceeds twice the user given
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scale value.
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--
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For single functions the three decay functions together with their parameters can be visualized like this (the field in this example called "age"):
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image:images/decay_2d.png[width=600]
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===== Multi-values fields
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If a field used for computing the decay contains multiple values, per default the value closest to the origin is chosen for determining the distance.
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This can be changed by setting `multi_value_mode`.
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[horizontal]
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`min`:: Distance is the minimum distance
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`max`:: Distance is the maximum distance
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`avg`:: Distance is the average distance
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`sum`:: Distance is the sum of all distances
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Example:
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[source,js]
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--------------------------------------------------
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"DECAY_FUNCTION": {
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"FIELD_NAME": {
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"origin": ...,
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"scale": ...
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},
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"multi_value_mode": "avg"
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}
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--------------------------------------------------
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==== Detailed example
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Suppose you are searching for a hotel in a certain town. Your budget is
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limited. Also, you would like the hotel to be close to the town center,
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so the farther the hotel is from the desired location the less likely
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you are to check in.
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You would like the query results that match your criterion (for
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example, "hotel, Nancy, non-smoker") to be scored with respect to
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distance to the town center and also the price.
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Intuitively, you would like to define the town center as the origin and
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maybe you are willing to walk 2km to the town center from the hotel. +
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In this case your *origin* for the location field is the town center
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and the *scale* is ~2km.
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If your budget is low, you would probably prefer something cheap above
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something expensive. For the price field, the *origin* would be 0 Euros
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and the *scale* depends on how much you are willing to pay, for example 20 Euros.
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In this example, the fields might be called "price" for the price of the
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hotel and "location" for the coordinates of this hotel.
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The function for `price` in this case would be
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[source,js]
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--------------------------------------------------
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"gauss": { <1>
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"price": {
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"origin": "0",
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"scale": "20"
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}
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}
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--------------------------------------------------
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<1> This decay function could also be `linear` or `exp`.
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and for `location`:
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[source,js]
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--------------------------------------------------
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"gauss": { <1>
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"location": {
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"origin": "11, 12",
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"scale": "2km"
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}
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}
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--------------------------------------------------
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<1> This decay function could also be `linear` or `exp`.
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Suppose you want to multiply these two functions on the original score,
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the request would look like this:
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[source,js]
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--------------------------------------------------
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GET /_search
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{
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"query": {
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"function_score": {
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"functions": [
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{
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"gauss": {
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"price": {
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"origin": "0",
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"scale": "20"
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}
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}
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},
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{
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"gauss": {
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"location": {
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"origin": "11, 12",
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"scale": "2km"
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}
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}
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}
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],
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"query": {
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"match": {
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"properties": "balcony"
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}
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},
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"score_mode": "multiply"
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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Next, we show how the computed score looks like for each of the three
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possible decay functions.
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[[gauss-decay]]
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===== Normal decay, keyword `gauss`
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When choosing `gauss` as the decay function in the above example, the
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contour and surface plot of the multiplier looks like this:
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image::https://f.cloud.github.com/assets/4320215/768157/cd0e18a6-e898-11e2-9b3c-f0145078bd6f.png[width="700px"]
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image::https://f.cloud.github.com/assets/4320215/768160/ec43c928-e898-11e2-8e0d-f3c4519dbd89.png[width="700px"]
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Suppose your original search results matches three hotels :
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* "Backback Nap"
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* "Drink n Drive"
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* "BnB Bellevue".
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"Drink n Drive" is pretty far from your defined location (nearly 2 km)
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and is not too cheap (about 13 Euros) so it gets a low factor a factor
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of 0.56. "BnB Bellevue" and "Backback Nap" are both pretty close to the
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defined location but "BnB Bellevue" is cheaper, so it gets a multiplier
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of 0.86 whereas "Backpack Nap" gets a value of 0.66.
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[[exp-decay]]
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===== Exponential decay, keyword `exp`
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When choosing `exp` as the decay function in the above example, the
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contour and surface plot of the multiplier looks like this:
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image::https://f.cloud.github.com/assets/4320215/768161/082975c0-e899-11e2-86f7-174c3a729d64.png[width="700px"]
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image::https://f.cloud.github.com/assets/4320215/768162/0b606884-e899-11e2-907b-aefc77eefef6.png[width="700px"]
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[[linear-decay]]
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===== Linear decay, keyword `linear`
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When choosing `linear` as the decay function in the above example, the
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contour and surface plot of the multiplier looks like this:
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image::https://f.cloud.github.com/assets/4320215/768164/1775b0ca-e899-11e2-9f4a-776b406305c6.png[width="700px"]
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image::https://f.cloud.github.com/assets/4320215/768165/19d8b1aa-e899-11e2-91bc-6b0553e8d722.png[width="700px"]
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==== Supported fields for decay functions
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Only numeric, date, and geo-point fields are supported.
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==== What if a field is missing?
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If the numeric field is missing in the document, the function will
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return 1.
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