[[query-dsl-multi-match-query]] === Multi Match Query The `multi_match` query builds on the <> to allow multi-field queries: [source,js] -------------------------------------------------- { "multi_match" : { "query": "this is a test", <1> "fields": [ "subject", "message" ] <2> } } -------------------------------------------------- <1> The query string. <2> The fields to be queried. [float] ==== `fields` and per-field boosting Fields can be specified with wildcards, eg: [source,js] -------------------------------------------------- { "multi_match" : { "query": "Will Smith", "fields": [ "title", "*_name" ] <1> } } -------------------------------------------------- <1> Query the `title`, `first_name` and `last_name` fields. Individual fields can be boosted with the caret (`^`) notation: [source,js] -------------------------------------------------- { "multi_match" : { "query" : "this is a test", "fields" : [ "subject^3", "message" ] <1> } } -------------------------------------------------- <1> The `subject` field is three times as important as the `message` field. [[multi-match-types]] [float] ==== Types of `multi_match` query: The way the `multi_match` query is executed internally depends on the `type` parameter, which can be set to: [horizontal] `best_fields`:: (*default*) Finds documents which match any field, but uses the `_score` from the best field. See <>. `most_fields`:: Finds documents which match any field and combines the `_score` from each field. See <>. `cross_fields`:: Treats fields with the same `analyzer` as though they were one big field. Looks for each word in *any* field. See <>. `phrase`:: Runs a `match_phrase` query on each field and combines the `_score` from each field. See <>. `phrase_prefix`:: Runs a `match_phrase_prefix` query on each field and combines the `_score` from each field. See <>. [[type-best-fields]] ==== `best_fields` 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 <> for each field and wraps them in a <> query, to find the single best matching field. For instance, this query: [source,js] -------------------------------------------------- { "multi_match" : { "query": "brown fox", "type": "best_fields", "fields": [ "subject", "message" ], "tie_breaker": 0.3 } } -------------------------------------------------- would be executed as: [source,js] -------------------------------------------------- { "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: * the score from the best matching field * plus `tie_breaker * _score` for all other matching fields Also, accepts `analyzer`, `boost`, `operator`, `minimum_should_match`, `fuzziness`, `prefix_length`, `max_expansions`, `rewrite`, `zero_terms_query` and `cutoff_frequency`, as explained in <>. [IMPORTANT] [[operator-min]] .`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: [source,js] -------------------------------------------------- { "multi_match" : { "query": "Will Smith", "type": "best_fields", "fields": [ "first_name", "last_name" ], "operator": "and" <1> } } -------------------------------------------------- <1> All terms must be present. 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 <> for a better solution. =================================================== [[type-most-fields]] ==== `most_fields` 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: [source,js] -------------------------------------------------- { "multi_match" : { "query": "quick brown fox", "type": "most_fields", "fields": [ "title", "title.original", "title.shingles" ] } } -------------------------------------------------- would be executed as: [source,js] -------------------------------------------------- { "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`, `prefix_length`, `max_expansions`, `rewrite`, `zero_terms_query` and `cutoff_frequency`, as explained in <>, but *see <>*. [[type-phrase]] ==== `phrase` and `phrase_prefix` The `phrase` and `phrase_prefix` types behave just like <>, but they use a `match_phrase` or `match_phrase_prefix` query instead of a `match` query. This query: [source,js] -------------------------------------------------- { "multi_match" : { "query": "quick brown f", "type": "phrase_prefix", "fields": [ "subject", "message" ] } } -------------------------------------------------- would be executed as: [source,js] -------------------------------------------------- { "dis_max": { "queries": [ { "match_phrase_prefix": { "subject": "quick brown f" }}, { "match_phrase_prefix": { "message": "quick brown f" }} ] } } -------------------------------------------------- Also, accepts `analyzer`, `boost`, `slop` and `zero_terms_query` as explained in <>. Type `phrase_prefix` additionally accepts `max_expansions`. [[type-cross-fields]] ==== `cross_fields` 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. **** This sounds like a job for <> but there are two problems with that approach. The first problem is that `operator` and `minimum_should_match` are applied per-field, instead of per-term (see <>). The second problem is to do with relevance: the different term frequencies in the `first_name` and `last_name` fields can produce unexpected results. For instance, imagine we have two people: ``Will Smith'' and ``Smith Jones''. ``Smith'' as a last name is very common (and so is of low importance) but ``Smith'' as a first name is very uncommon (and so is of great importance). If we do a search for ``Will Smith'', the ``Smith Jones'' document will probably appear above the better matching ``Will Smith'' because the score of `first_name:smith` has trumped the combined scores of `first_name:will` plus `last_name:smith`. **** 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: [source,js] -------------------------------------------------- { "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 <>.) 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 other words, `first_name:smith` will be treated as though it has the same weight as `last_name:smith`. (Actually, `first_name:smith` is given a tiny advantage over `last_name:smith`, just to make the order of results more stable.) If you run the above query through the <>, 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`, `zero_terms_query` and `cutoff_frequency`, as explained in <>. ===== `cross_field` and analysis 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: [source,js] -------------------------------------------------- { "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 <> 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: [source,js] -------------------------------------------------- { "bool": { "should": [ { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "first", "last" ], "minimum_should_match": "50%" <1> } }, { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "*.edge" ] } } ] } } -------------------------------------------------- <1> Either `will` or `smith` must be present in either of the `first` or `last` fields You can force all fields into the same group by specifying the `analyzer` parameter in the query. [source,js] -------------------------------------------------- { "multi_match" : { "query": "Jon", "type": "cross_fields", "analyzer": "standard", <1> "fields": [ "first", "last", "*.edge" ] } } -------------------------------------------------- <1> Use the `standard` analyzer for all fields. which will be executed as: blended("will", fields: [first, first.edge, last.edge, last]) blended("smith", fields: [first, first.edge, last.edge, last]) ===== `tie_breaker` 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: [horizontal] `0.0`:: Take the single best score out of (eg) `first_name:will` and `last_name:will` (*default*) `1.0`:: Add together the scores for (eg) `first_name:will` and `last_name:will` `0.0 < n < 1.0`:: Take the single best score plus +tie_breaker+ multiplied by each of the scores from other matching fields.