The multi_match
query builds on the match
query
to allow multi-field queries:
GET /_search { "query": { "multi_match" : { "query": "this is a test", "fields": [ "subject", "message" ] } } }
fields
and per-field boostingFields can be specified with wildcards, eg:
GET /_search { "query": { "multi_match" : { "query": "Will Smith", "fields": [ "title", "*_name" ] } } }
Individual fields can be boosted with the caret (^
) notation:
GET /_search { "query": { "multi_match" : { "query" : "this is a test", "fields" : [ "subject^3", "message" ] } } }
If no fields
are provided, the multi_match
query defaults to the index.query.default_field
index settings, which in turn defaults to *
. *
extracts all fields in the mapping that
are eligible to term queries and filters the metadata fields. All extracted fields are then
combined to build a query.
There is a limit on the number of fields that can be queried
at once. It is defined by the indices.query.bool.max_clause_count
Search Settings
which defaults to 1024.
multi_match
query:The way the multi_match
query is executed internally depends on the type
parameter, which can be set to:
|
(default) Finds documents which match any field, but
uses the |
|
Finds documents which match any field and combines
the |
|
Treats fields with the same |
|
Runs a |
|
Runs a |
The best_fields
type is most useful when you are searching for multiple
words best found in the same field. For instance “brown fox” in a single
field is more meaningful than “brown” in one field and “fox” in the other.
The best_fields
type generates a match
query for
each field and wraps them in a dis_max
query, to
find the single best matching field. For instance, this query:
GET /_search { "query": { "multi_match" : { "query": "brown fox", "type": "best_fields", "fields": [ "subject", "message" ], "tie_breaker": 0.3 } } }
would be executed as:
GET /_search { "query": { "dis_max": { "queries": [ { "match": { "subject": "brown fox" }}, { "match": { "message": "brown fox" }} ], "tie_breaker": 0.3 } } }
Normally the best_fields
type uses the score of the single best matching
field, but if tie_breaker
is specified, then it calculates the score as
follows:
tie_breaker * _score
for all other matching fields
Also, accepts analyzer
, boost
, operator
, minimum_should_match
,
fuzziness
, lenient
, prefix_length
, max_expansions
, rewrite
, zero_terms_query
,
cutoff_frequency
, auto_generate_synonyms_phrase_query
and fuzzy_transpositions
,
as explained in match query.
operator
and minimum_should_match
The best_fields
and most_fields
types are field-centric — they generate
a match
query per field. This means that the operator
and
minimum_should_match
parameters are applied to each field individually,
which is probably not what you want.
Take this query for example:
GET /_search { "query": { "multi_match" : { "query": "Will Smith", "type": "best_fields", "fields": [ "first_name", "last_name" ], "operator": "and" } } }
This query is executed as:
(+first_name:will +first_name:smith) | (+last_name:will +last_name:smith)
In other words, all terms must be present in a single field for a document to match.
See cross_fields
for a better solution.
The most_fields
type is most useful when querying multiple fields that
contain the same text analyzed in different ways. For instance, the main
field may contain synonyms, stemming and terms without diacritics. A second
field may contain the original terms, and a third field might contain
shingles. By combining scores from all three fields we can match as many
documents as possible with the main field, but use the second and third fields
to push the most similar results to the top of the list.
This query:
GET /_search { "query": { "multi_match" : { "query": "quick brown fox", "type": "most_fields", "fields": [ "title", "title.original", "title.shingles" ] } } }
would be executed as:
GET /_search { "query": { "bool": { "should": [ { "match": { "title": "quick brown fox" }}, { "match": { "title.original": "quick brown fox" }}, { "match": { "title.shingles": "quick brown fox" }} ] } } }
The score from each match
clause is added together, then divided by the
number of match
clauses.
Also, accepts analyzer
, boost
, operator
, minimum_should_match
,
fuzziness
, lenient
, prefix_length
, max_expansions
, rewrite
, zero_terms_query
and cutoff_frequency
, as explained in match query, but
see operator
and minimum_should_match
.
The phrase
and phrase_prefix
types behave just like best_fields
,
but they use a match_phrase
or match_phrase_prefix
query instead of a
match
query.
This query:
GET /_search { "query": { "multi_match" : { "query": "quick brown f", "type": "phrase_prefix", "fields": [ "subject", "message" ] } } }
would be executed as:
GET /_search { "query": { "dis_max": { "queries": [ { "match_phrase_prefix": { "subject": "quick brown f" }}, { "match_phrase_prefix": { "message": "quick brown f" }} ] } } }
Also, accepts analyzer
, boost
, lenient
, slop
and zero_terms_query
as explained
in Match Query. Type phrase_prefix
additionally accepts
max_expansions
.
The cross_fields
type is particularly useful with structured documents where
multiple fields should match. For instance, when querying the first_name
and last_name
fields for “Will Smith”, the best match is likely to have
“Will” in one field and “Smith” in the other.
One way of dealing with these types of queries is simply to index the
first_name
and last_name
fields into a single full_name
field. Of
course, this can only be done at index time.
The cross_field
type tries to solve these problems at query time by taking a
term-centric approach. It first analyzes the query string into individual
terms, then looks for each term in any of the fields, as though they were one
big field.
A query like:
GET /_search { "query": { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "first_name", "last_name" ], "operator": "and" } } }
is executed as:
+(first_name:will last_name:will) +(first_name:smith last_name:smith)
In other words, all terms must be present in at least one field for a
document to match. (Compare this to
the logic used for best_fields
and most_fields
.)
That solves one of the two problems. The problem of differing term frequencies is solved by blending the term frequencies for all fields in order to even out the differences.
In practice, first_name:smith
will be treated as though it has the same
frequencies as last_name:smith
, plus one. This will make matches on
first_name
and last_name
have comparable scores, with a tiny advantage
for last_name
since it is the most likely field that contains smith
.
Note that cross_fields
is usually only useful on short string fields
that all have a boost
of 1
. Otherwise boosts, term freqs and length
normalization contribute to the score in such a way that the blending of term
statistics is not meaningful anymore.
If you run the above query through the Validate API, it returns this explanation:
+blended("will", fields: [first_name, last_name]) +blended("smith", fields: [first_name, last_name])
Also, accepts analyzer
, boost
, operator
, minimum_should_match
,
lenient
, zero_terms_query
and cutoff_frequency
, as explained in
match query.
The cross_field
type can only work in term-centric mode on fields that have
the same analyzer. Fields with the same analyzer are grouped together as in
the example above. If there are multiple groups, they are combined with a
bool
query.
For instance, if we have a first
and last
field which have
the same analyzer, plus a first.edge
and last.edge
which
both use an edge_ngram
analyzer, this query:
GET /_search { "query": { "multi_match" : { "query": "Jon", "type": "cross_fields", "fields": [ "first", "first.edge", "last", "last.edge" ] } } }
would be executed as:
blended("jon", fields: [first, last]) | ( blended("j", fields: [first.edge, last.edge]) blended("jo", fields: [first.edge, last.edge]) blended("jon", fields: [first.edge, last.edge]) )
In other words, first
and last
would be grouped together and
treated as a single field, and first.edge
and last.edge
would be
grouped together and treated as a single field.
Having multiple groups is fine, but when combined with operator
or
minimum_should_match
, it can suffer from the same problem
as most_fields
or best_fields
.
You can easily rewrite this query yourself as two separate cross_fields
queries combined with a bool
query, and apply the minimum_should_match
parameter to just one of them:
GET /_search { "query": { "bool": { "should": [ { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "first", "last" ], "minimum_should_match": "50%" } }, { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "*.edge" ] } } ] } } }
You can force all fields into the same group by specifying the analyzer
parameter in the query.
GET /_search { "query": { "multi_match" : { "query": "Jon", "type": "cross_fields", "analyzer": "standard", "fields": [ "first", "last", "*.edge" ] } } }
which will be executed as:
blended("will", fields: [first, first.edge, last.edge, last]) blended("smith", fields: [first, first.edge, last.edge, last])
By default, each per-term blended
query will use the best score returned by
any field in a group, then these scores are added together to give the final
score. The tie_breaker
parameter can change the default behaviour of the
per-term blended
queries. It accepts:
|
Take the single best score out of (eg) |
|
Add together the scores for (eg) |
|
Take the single best score plus |