IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

› › ›

Script score query

edit

Script score query

edit

This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

The script_score allows you to modify the score of documents that are retrieved by a query. This can be useful if, for example, a score function is computationally expensive and it is sufficient to compute the score on a filtered set of documents.

To use script_score, you have to define a query and a script - a function to be used to compute a new score for each document returned by the query. For more information on scripting see scripting documentation.

Here is an example of using script_score to assign each matched document a score equal to the number of likes divided by 10:

GET /_search
{
    "query" : {
        "script_score" : {
            "query" : {
                "match": { "message": "elasticsearch" }
            },
            "script" : {
                "source" : "doc['likes'].value / 10 "
            }
        }
     }
}

Accessing the score of a document within a script

edit

Within a script, you can access the _score variable which represents the current relevance score of a document.

Predefined functions within a Painless script

edit

You can use any of the available painless functions in the painless script. Besides these functions, there are a number of predefined functions that can help you with scoring. We suggest you to use them instead of rewriting equivalent functions of your own, as these functions try to be the most efficient by using the internal mechanisms.

saturation

edit

saturation(value,k) = value/(k + value)

"script" : {
    "source" : "saturation(doc['likes'].value, 1)"
}

sigmoid

edit

sigmoid(value, k, a) = value^a/ (k^a + value^a)

"script" : {
    "source" : "sigmoid(doc['likes'].value, 2, 1)"
}

Random functions

edit

There are two predefined ways to produce random values: randomNotReproducible and randomReproducible.

randomNotReproducible() uses java.util.Random class to generate a random value of the type long. The generated values are not reproducible between requests' invocations.

"script" : {
    "source" : "randomNotReproducible()"
}

randomReproducible(String seedValue, int seed) produces reproducible random values of type long. This function requires more computational time and memory than the non-reproducible version.

A good candidate for the seedValue is document field values that are unique across documents and already pre-calculated and preloaded in the memory. For example, values of the document’s _seq_no field is a good candidate, as documents on the same shard have unique values for the _seq_no field.

"script" : {
    "source" : "randomReproducible(Long.toString(doc['_seq_no'].value), 100)"
}

A drawback of using _seq_no is that generated values change if documents are updated. Another drawback is not absolute uniqueness, as documents from different shards with the same sequence numbers generate the same random values.

If you need random values to be distinct across different shards, you can use a field with unique values across shards, such as _id, but watch out for the memory usage as all these unique values need to be loaded into memory.

"script" : {
    "source" : "randomReproducible(doc['_id'].value, 100)"
}

Decay functions for numeric fields

edit

You can read more about decay functions here.

double decayNumericLinear(double origin, double scale, double offset, double decay, double docValue)
double decayNumericExp(double origin, double scale, double offset, double decay, double docValue)
double decayNumericGauss(double origin, double scale, double offset, double decay, double docValue)

"script" : {
    "source" : "decayNumericLinear(params.origin, params.scale, params.offset, params.decay, doc['dval'].value)",
    "params": { 
        "origin": 20,
        "scale": 10,
        "decay" : 0.5,
        "offset" : 0
    }
}

Using params allows to compile the script only once, even if params change.

Decay functions for geo fields

edit

double decayGeoLinear(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)
double decayGeoExp(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)
double decayGeoGauss(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)

"script" : {
    "source" : "decayGeoExp(params.origin, params.scale, params.offset, params.decay, doc['location'].value)",
    "params": {
        "origin": "40, -70.12",
        "scale": "200km",
        "offset": "0km",
        "decay" : 0.2
    }
}

Decay functions for date fields

edit

double decayDateLinear(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)
double decayDateExp(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)
double decayDateGauss(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)

"script" : {
    "source" : "decayDateGauss(params.origin, params.scale, params.offset, params.decay, doc['date'].value)",
    "params": {
        "origin": "2008-01-01T01:00:00Z",
        "scale": "1h",
        "offset" : "0",
        "decay" : 0.5
    }
}

Decay functions on dates are limited to dates in the default format and default time zone. Also calculations with now are not supported.

Faster alternatives

edit

Script Score Query calculates the score for every hit (matching document). There are faster alternative query types that can efficiently skip non-competitive hits:

If you want to boost documents on some static fields, use Rank Feature Query.

Transition from Function Score Query

edit

We are deprecating Function Score, and Script Score Query will be a substitute for it.

Here we describe how Function Score Query’s functions can be equivalently implemented in Script Score Query:

`script_score`

edit

What you used in script_score of the Function Score query, you can copy into the Script Score query. No changes here.

`weight`

edit

weight function can be implemented in the Script Score query through the following script:

"script" : {
    "source" : "params.weight * _score",
    "params": {
        "weight": 2
    }
}

`random_score`

edit

Use randomReproducible and randomNotReproducible functions as described in random functions.

`field_value_factor`

edit

field_value_factor function can be easily implemented through script:

"script" : {
    "source" : "Math.log10(doc['field'].value * params.factor)",
    params" : {
        "factor" : 5
    }
}

For checking if a document has a missing value, you can use doc['field'].size() == 0. For example, this script will use a value 1 if a document doesn’t have a field field:

"script" : {
    "source" : "Math.log10((doc['field'].size() == 0 ? 1 : doc['field'].value()) * params.factor)",
    params" : {
        "factor" : 5
    }
}

This table lists how field_value_factor modifiers can be implemented through a script:

Modifier	Implementation in Script Score
`none`	-
`log`	`Math.log10(doc['f'].value)`
`log1p`	`Math.log10(doc['f'].value + 1)`
`log2p`	`Math.log10(doc['f'].value + 2)`
`ln`	`Math.log(doc['f'].value)`
`ln1p`	`Math.log(doc['f'].value + 1)`
`ln2p`	`Math.log(doc['f'].value + 2)`
`square`	`Math.pow(doc['f'].value, 2)`
`sqrt`	`Math.sqrt(doc['f'].value)`
`reciprocal`	`1.0 / doc['f'].value`

`decay functions`

edit

Script Score query has equivalent decay functions that can be used in script.

« Script query Wrapper query »