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cross-fields Queriesedit
The custom _all approach is a good solution, as long as you thought
about setting it up before you indexed your documents. However, Elasticsearch
also provides a search-time solution to the problem: the multi_match query
with type cross_fields.
The cross_fields type takes a term-centric approach, quite different from the
field-centric approach taken by best_fields and most_fields. It treats all
of the fields as one big field, and looks for each term in any field.
To illustrate the difference between field-centric and term-centric queries,
look at the explanation for this field-centric most_fields query:
GET /_validate/query?explain
{
"query": {
"multi_match": {
"query": "peter smith",
"type": "most_fields",
"operator": "and",
"fields": [ "first_name", "last_name" ]
}
}
}
For a document to match, both peter and smith must appear in the same
field, either the first_name field or the last_name field:
(+first_name:peter +first_name:smith) (+last_name:peter +last_name:smith)
A term-centric approach would use this logic instead:
+(first_name:peter last_name:peter) +(first_name:smith last_name:smith)
In other words, the term peter must appear in either field, and the term
smith must appear in either field.
The cross_fields type first analyzes the query string to produce a list of
terms, and then it searches for each term in any field. That difference alone
solves two of the three problems that we listed in Field-Centric Queries, leaving
us just with the issue of differing inverse document frequencies.
Fortunately, the cross_fields type solves this too, as can be seen from this
validate-query request:
GET /_validate/query?explain
{
"query": {
"multi_match": {
"query": "peter smith",
"type": "cross_fields",
"operator": "and",
"fields": [ "first_name", "last_name" ]
}
}
}
It solves the term-frequency problem by blending inverse document frequencies across fields:
+blended("peter", fields: [first_name, last_name])
+blended("smith", fields: [first_name, last_name])
In other words, it looks up the IDF of smith in both the first_name and
the last_name fields and uses the minimum of the two as the IDF for both
fields. The fact that smith is a common last name means that it will be
treated as a common first name too.
For the cross_fields query type to work optimally, all fields should have
the same analyzer. Fields that share an analyzer are grouped together as
blended fields.
If you include fields with a different analysis chain, they will be added to
the query in the same way as for best_fields. For instance, if we added the
title field to the preceding query (assuming it uses a different analyzer), the
explanation would be as follows:
(+title:peter +title:smith)
(
+blended("peter", fields: [first_name, last_name])
+blended("smith", fields: [first_name, last_name])
)
This is particularly important when using the minimum_should_match and
operator parameters.
Per-Field Boostingedit
One of the advantages of using the cross_fields query over
custom _all fields is that you can boost individual
fields at query time.
For fields of equal value like first_name and last_name, this generally
isn’t required, but if you were searching for books using the title and
description fields, you might want to give more weight to the title field.
This can be done as described before with the caret (^) syntax:
GET /books/_search
{
"query": {
"multi_match": {
"query": "peter smith",
"type": "cross_fields",
"fields": [ "title^2", "description" ]
}
}
}
The advantage of being able to boost individual fields should be weighed
against the cost of querying multiple fields instead of querying a single
custom _all field. Use whichever of the two solutions that delivers the most
bang for your buck.