[[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 _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 `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::

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`, 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.

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.