Finally, if none of the function_score's built in functions suffice, you can implement the logic that you need with a script, using the script_score function.

For an example, let’s say that we want to factor our profit margin into the relevance calculation. In our business, the profit margin depends on three factors:

• The price per night of the holiday home.

• The user’s membership level — some levels get a percentage discount above a certain price-per-night threshold.

• The negotiated margin as a percentage of the price-per-night, after user discounts.

The algorithm that we will use to calculate the profit for each home is as follows:

We probably don’t want to use the absolute profit as a score — it would overwhelm the other factors like location, popularity, features etc. Instead, we can express the profit as a percentage of our target profit. A profit margin above our target will have a positive score — greater than 1.0 — and a profit margin below our target will have a negative score — less than 1.0:

The default scripting language in Elasticsearch is Groovy which, for the most part, looks a lot like Javascript. The above algorithm as a Groovy script would look like this:

1. The price and margin variables are extracted from the price and margin fields in the document.

2. The threshold, discount and target variables we will pass in as params.

Finally, we can add our script_score function to the list of other functions that we are already using:

1. The location and price clauses refer to the example explained in [decay-functions].

2. By passing in these variables as params, we can change their values every time we run this query without having to recompile the script.

3. JSON cannot include embedded newline characters. Newline characters in the script should either be escaped as \n or replaced with semicolons.

This query would return the documents which best satisfy the user’s requirements for location and price, while still factoring in our need to make a profit.