Multi Match Query - ignacio-alorre/ElasticSearch GitHub Wiki
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" ]
}
}
}
Where:
query: The query stringfields: The fields to be queried. Fields can be specified as wildcards
Example
The following example would query title, first_name and last_name
GET /_search
{
"query": {
"multi_match" : {
"query": "Will Smith",
"fields": [ "title", "*_name" ]
}
}
}
Individual fields can be boosted with the caret (^) notation. In the following query the subject field is three times as important as the message field.
GET /_search
{
"query": {
"multi_match" : {
"query" : "this is a test",
"fields" : [ "subject^3", "message" ]
}
}
}
Types of multi_match query
The way the multi_match query is executed internally depends on the type parameter, which can be set to:
-
best_fields: (default) Finds documents which match any field, but uses the_scorefrom the best field. Seebest_fields. -
most_fields: Finds documents which match any field and combines the _score from each field. Seemost_fields. -
cross_fields: Treats fields with the same analyser as though they were one big field. Looks for each word in any field. Seecross_fields. -
phrase: Runs amatch_phrasequery on each field and combines the_scorefrom each field. See phrase and phrase_prefix. -
phrase_prefix: Runs amatch_phrase_prefixquery on each field and combines the_scorefrom each field. See phrase and phrase_prefix.
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 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
}
}
}
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 analyser, boost, operator, minimum_should_match, fuzziness, lenient, prefix_length, max_expansions, rewrite, zero_terms_query and cutoff_frequency, as explained in match query.
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:
GET /_search
{
"query": {
"multi_match" : {
"query": "quick brown fox",
"type": "most_fields",
"fields": [ "title", "title.original", "title.shingles" ]
}
}
}
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.
phrase and phrase_prefix
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" ]
}
}
}
Also, accepts analyzer, boost, lenient, slop and zero_terms_query as explained in Match Query. Type phrase_prefix additionally accepts max_expansions.
Note: The fuzziness parameter cannot be used with the phrase or phrase_prefix type.
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.
Note: This sounds like a job for most_fields 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 explanation above).
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:
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.
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.
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:
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" ]
}
}
]
}
}
}
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.
GET /_search { "query": { "multi_match" : { "query": "Jon", "type": "cross_fields", "analyzer": "standard", "fields": [ "first", "last", "*.edge" ] } } }
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:
-
0.0Take the single best score out of (eg) first_name:will and last_name:will (default) -
1.0Add together the scores for (eg) first_name:will and last_name:will -
0.0 < n < 1.0Take the single best score plus tie_breaker multiplied by each of the scores from other matching fields.
Note: The fuzziness parameter cannot be used with the cross_fields type