We are working on updating this book for the latest version. Some content might be out of date.
The function_score query is the
ultimate tool for taking control of the scoring process.
It allows you to
apply a function to each document that matches the main query in order to
alter or completely replace the original query _score.
In fact, you can apply different functions to subsets of the main result set by using filters, which gives you the best of both worlds: efficient scoring with cacheable filters.
It supports several predefined functions out of the box:
-
weight -
Apply a simple boost to each document without the boost being
normalized: a
weightof2results in2 * _score. -
field_value_factor -
Use the value of a field in the document to alter the
_score, such as factoring in apopularitycount or number ofvotes. -
random_score - Use consistently random scoring to sort results differently for every user, while maintaining the same sort order for a single user.
-
Decay functions—
linear,exp,gauss -
Incorporate sliding-scale values like
publish_date,geo_location, orpriceinto the_scoreto prefer recently published documents, documents near a latitude/longitude (lat/lon) point, or documents near a specified price point. -
script_score - Use a custom script to take complete control of the scoring logic. If your needs extend beyond those of the functions in this list, write a custom script to implement the logic that you need.
Without the function_score query, we would not be able to combine the score
from a full-text query with a factor like recency. We would have to sort
either by _score or by date; the effect of one would obliterate the
effect of the other. This query allows you to blend the two together: to still
sort by full-text relevance, but giving extra weight to recently published
documents, or popular documents, or products that are near the user’s price
point. As you can imagine, a query that supports all of this can look fairly
complex. We’ll start with a simple use case and work our way up the
complexity ladder.