The {ref}query-dsl-function-score-query.html[function_score query] is the ultimate tool for taking control of the scoring process. It allows you to apply a function to each document which 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 using filters, which gives you the best of both worlds: efficient scoring with cacheable filters.

It supports a number of predefined functions out of the box:

weight

Apply a simple boost to each document without the boost being normalized: a weight of 2 results in 2 * _score.

field_value_factor

Use the value of a field in the document to alter the _score, such as factoring in a popularity count or number of votes.

random_score

Apply consistently random sorting to your results to ensure that documents would otherwise have the same score are randomly shown to different users, while maintaining the same sort order for each user.

Decay functions — linear, exp, gauss

Incorporate sliding-scale values like publish_date, geo_location, or price into the _score to prefer recently published document, documents near a 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 listed above, 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 which are near the user’s price point.

As you can imagine, a query which supports all of the above can look fairly complex. We’ll start with a simple use case and work our way up the complexity ladder.