How does a function score query work in Elasticsearch?

When a function score query is executed in Elasticsearch, it applies one or more scoring functions to each document in the search results, and then combines the scores from each function to calculate a final score for each document. The resulting scores are used to rank the search results.

Here’s an example of a function score query in Elasticsearch:

GET /my_index/_search
{
  "query": {
    "function_score": {
      "query": { "match": { "title": "elasticsearch" }},
      "functions": [
        { "weight": 2, "filter": { "match": { "description": "search" }}},
        { "random_score": {}}
      ],
      "score_mode": "sum",
      "boost_mode": "multiply"
    }
  }
}

In this example, we are searching the `title` field in the `my_index` index for documents that contain the term “elasticsearch”. The function score query applies two scoring functions to each document: one that gives a weight of 2 to documents that contain the term “search” in the `description` field, and another that applies a random score to each document. The `score_mode` parameter is set to “sum” to combine the scores from each function, and the `boost_mode` parameter is set to “multiply” to multiply the final score by the query’s boost factor.

The function score query also supports a variety of other features, such as specifying different types of scoring functions, using decay functions to give more weight to recent or relevant documents, and using script scoring to customize the scoring logic.

The function score query can also be used in combination with other query types, such as the bool query, to construct more complex search queries. By allowing for custom scoring logic and ranking search results based on multiple scoring functions, the function score query provides a powerful and flexible way to improve search relevance and accuracy in Elasticsearch.

However, it’s important to note that function score queries can be computationally expensive and may not be suitable for large datasets or high-traffic applications. Additionally, function score queries can be less precise than other query types, such as the term or match query, because they rely on custom scoring logic. Therefore, it’s important to carefully consider the use case and performance implications before using function score queries in Elasticsearch.