20_How_match_uses_bool.asciidoc

How match uses bool

By now, you have probably realized that multi-word match queries simply wrap the generated term queries in a bool query. With the default "or" operator, each term query is added as a should clause, so at least one clause must match. These two queries are equivalent:

{
    "match": { "title": "brown fox"}
}

{
  "bool": {
    "should": [
      { "term": { "title": "brown" }},
      { "term": { "title": "fox"   }}
    ]
  }
}

With the "and" operator, all the term queries are added as must clauses, so all clauses must match. These two queries are equivalent:

{
    "match": {
        "title": {
            "query":    "brown fox",
            "operator": "and"
        }
    }
}

{
  "bool": {
    "must": [
      { "term": { "title": "brown" }},
      { "term": { "title": "fox"   }}
    ]
  }
}

And if the minimum_should_match parameter is specified, it is passed directly through to the bool query, making these two queries equivalent:

{
    "match": {
        "title": {
            "query":                "quick brown fox",
            "minimum_should_match": "75%"
        }
    }
}

{
  "bool": {
    "should": [
      { "term": { "title": "brown" }},
      { "term": { "title": "fox"   }},
      { "term": { "title": "quick" }}
    ],
    "minimum_should_match": 2 (1)
  }
}

1. Because there are only three clauses, the minimum_should_match value of 75% in the match query is rounded down to 2 — at least 2 out of the 3 should clauses must match.

Of course, we would normally write these types of queries using the match query, but understanding how the match query works internally lets you take control of the process when you need to. There are some things that can’t be done with a single match query, such as give more weight to some query terms than to others. We will look at an example of this in the next section.