[[function-score-query]] === function_score Query
The http://bit.ly/1sCKtHW[`function_score` query] is the
ultimate tool for taking control of the scoring process.((("function_score query")))((("relevance", "controlling", "function_score query"))) 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
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:
Apply a simple boost to each document without the boost being normalized: a `weight` of `2` results in `2 * _score`.
Use the value of a field in the document to alter the `_score`, such as factoring in a `popularity` count or number of `votes`.
Use consistently random scoring to sort results differently for every user, while maintaining the same sort order for a single user.
Incorporate sliding-scale values like `publish_date`, `geo_location`, or `price` into the `_score` to prefer recently published documents, documents near a latitude/longitude (lat/lon) point, or documents near a specified price point.
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.
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
_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