=== Multiword Queries
If we could search for only one word at a time, full-text search would be
pretty inflexible. Fortunately, the match query((("full text search", "multi-word queries")))((("match query", "multi-word query"))) makes multiword queries
just as simple:
GET /my_index/my_type/_search { "query": { "match": { "title": "BROWN DOG!" } }
// SENSE: 100_Full_Text_Search/05_Match_query.json
The preceding query returns all four documents in the results list:
{ "hits": [ { "_id": "4", "_score": 0.73185337, <1> "_source": { "title": "Brown fox brown dog" } }, { "_id": "2", "_score": 0.47486103, <2> "_source": { "title": "The quick brown fox jumps over the lazy dog" } }, { "_id": "3", "_score": 0.47486103, <2> "_source": { "title": "The quick brown fox jumps over the quick dog" } }, { "_id": "1", "_score": 0.11914785, <3> "_source": { "title": "The quick brown fox" } } ]
<1> Document 4 is the most relevant because it contains "brown" twice and "dog"
once.
<2> Documents 2 and 3 both contain brown and dog once each, and the title
field is the same length in both docs, so they have the same score.
<3> Document 1 matches even though it contains only brown, not dog.
Because the match query has to look for two terms—["brown","dog"]—internally it has to execute two term queries and combine their individual
results into the overall result. To do this, it wraps the two term queries
in a bool query, which we examine in detail in <
The important thing to take away from this is that any document whose
title field contains at least one of the specified terms will match the
query. The more terms that match, the more relevant the document.
[[match-improving-precision]] ==== Improving Precision
Matching any document that contains any of the query terms may result in a
long tail of seemingly irrelevant results. ((("full text search", "multi-word queries", "improving precision")))((("precision", "improving for full text search multi-word queries"))) It's a shotgun approach to search.
Perhaps we want to show only documents that contain all of the query terms.
In other words, instead of brown OR dog, we want to return only documents
that match brown AND dog.
The match query accepts an operator parameter((("match query", "operator parameter")))((("or operator", "in match queries")))((("and operator", "in match queries"))) that defaults to or.
You can change it to and to require that all specified terms must match:
GET /my_index/my_type/_search { "query": { "match": { "title": { <1> "query": "BROWN DOG!", "operator": "and" } } }
// SENSE: 100_Full_Text_Search/05_Match_query.json
<1> The structure of the match query has to change slightly in order to
accommodate the operator parameter.
This query would exclude document 1, which contains only one of the two terms.
[[match-precision]] ==== Controlling Precision
The choice between all and any is a bit((("full text search", "multi-word queries", "controlling precision"))) too black-or-white. What if the
user specified five query terms, and a document contains only four of them?
Setting operator to and would exclude this document.
Sometimes that is exactly what you want, but for most full-text search use cases, you want to include documents that may be relevant but exclude those that are unlikely to be relevant. In other words, we need something in-between.
The match query supports((("match query", "minimum_should_match parameter")))((("minimum_should_match parameter"))) the minimum_should_match parameter, which allows
you to specify the number of terms that must match for a document to be considered
relevant. While you can specify an absolute number of terms, it usually makes
sense to specify a percentage instead, as you have no control over the number of words the user may enter:
GET /my_index/my_type/_search { "query": { "match": { "title": { "query": "quick brown dog", "minimum_should_match": "75%" } } }
// SENSE: 100_Full_Text_Search/05_Match_query.json
When specified as a percentage, minimum_should_match does the right thing:
in the preceding example with three terms, 75% would be rounded down to 66.6%,
or two out of the three terms. No matter what you set it to, at least one term
must match for a document to be considered a match.
The minimum_should_match parameter is flexible, and different rules can
be applied depending on the number of terms the user enters. For the full
documentation see the
To fully understand how the match query handles multiword queries, we need
to look at how to combine multiple queries with the bool query.