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Elasticsearch: The Definitive Guide [2.x] » Search in Depth » Full-Text Search » Boosting Query Clauses
« How match Uses bool Controlling Analysis »

Boosting Query Clausesedit

Of course, the bool query isn’t restricted to combining simple one-word match queries. It can combine any other query, including other bool queries. It is commonly used to fine-tune the relevance _score for each document by combining the scores from several distinct queries.

Imagine that we want to search for documents about "full-text search," but we want to give more weight to documents that also mention "Elasticsearch" or "Lucene." By more weight, we mean that documents mentioning "Elasticsearch" or "Lucene" will receive a higher relevance _score than those that don’t, which means that they will appear higher in the list of results.

A simple bool query allows us to write this fairly complex logic as follows:

GET /_search
{
    "query": {
        "bool": {
            "must": {
                "match": {
                    "content": { 
                        "query":    "full text search",
                        "operator": "and"
                    }
                }
            },
            "should": [ 
                { "match": { "content": "Elasticsearch" }},
                { "match": { "content": "Lucene"        }}
            ]
        }
    }
}

The content field must contain all of the words full, text, and search.

If the content field also contains Elasticsearch or Lucene, the document will receive a higher _score.

The more should clauses that match, the more relevant the document. So far, so good.

But what if we want to give more weight to the docs that contain Lucene and even more weight to the docs containing Elasticsearch?

We can control the relative weight of any query clause by specifying a boost value, which defaults to 1. A boost value greater than 1 increases the relative weight of that clause. So we could rewrite the preceding query as follows:

GET /_search
{
    "query": {
        "bool": {
            "must": {
                "match": {  
                    "content": {
                        "query":    "full text search",
                        "operator": "and"
                    }
                }
            },
            "should": [
                { "match": {
                    "content": {
                        "query": "Elasticsearch",
                        "boost": 3 
                    }
                }},
                { "match": {
                    "content": {
                        "query": "Lucene",
                        "boost": 2 
                    }
                }}
            ]
        }
    }
}

These clauses use the default boost of 1.

This clause is the most important, as it has the highest boost.

This clause is more important than the default, but not as important as the Elasticsearch clause.

The boost parameter is used to increase the relative weight of a clause (with a boost greater than 1) or decrease the relative weight (with a boost between 0 and 1), but the increase or decrease is not linear. In other words, a boost of 2 does not result in double the _score.

Instead, the new _score is normalized after the boost is applied. Each type of query has its own normalization algorithm, and the details are beyond the scope of this book. Suffice to say that a higher boost value results in a higher _score.

If you are implementing your own scoring model not based on TF/IDF and you need more control over the boosting process, you can use the function_score query to manipulate a document’s boost without the normalization step.

We present other ways of combining queries in the next chapter, Multifield Search. But first, let’s take a look at the other important feature of queries: text analysis.

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