Multi Match Queryedit

The multi_match query builds on the match query to allow multi-field queries:

{
  "multi_match" : {
    "query":    "this is a test", 
    "fields": [ "subject", "message" ] 
  }
}

The query string.

The fields to be queried.

fields and per-field boostingedit

Fields can be specified with wildcards, eg:

{
  "multi_match" : {
    "query":    "Will Smith",
    "fields": [ "title", "*_name" ] 
  }
}

Query the title, first_name and last_name fields.

Individual fields can be boosted with the caret (^) notation:

{
  "multi_match" : {
    "query" : "this is a test",
    "fields" : [ "subject^3", "message" ] 
  }
}

The subject field is three times as important as the message field.

use_dis_maxedit

Warning

Deprecated in 1.1.0.

Use type:best_fields or type:most_fields instead. See the section called “Types of multi_match query:edit”.

By default, the multi_match query generates a match clause per field, then wraps them in a dis_max query. By setting use_dis_max to false, they will be wrapped in a bool query instead.

Types of multi_match query:edit

Note

Added in 1.1.0.

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 _score from the best field. See best_fields.

most_fields

Finds documents which match any field and combines the _score from each field. See most_fields.

cross_fields

Treats fields with the same analyzer as though they were one big field. Looks for each word in any field. See cross_fields.

phrase

Runs a match_phrase query on each field and combines the _score from each field. See phrase and phrase_prefix.

phrase_prefix

Runs a match_phrase_prefix query on each field and combines the _score from each field. See phrase and phrase_prefix.

best_fieldsedit

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:

{
  "multi_match" : {
    "query":      "brown fox",
    "type":       "best_fields",
    "fields":     [ "subject", "message" ],
    "tie_breaker": 0.3
  }
}

would be executed as:

{
  "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 match query.

Important

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:

{
  "multi_match" : {
    "query":      "Will Smith",
    "type":       "best_fields",
    "fields":     [ "first_name", "last_name" ],
    "operator":   "and" 
  }
}

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 cross_fields for a better solution.

most_fieldsedit

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:

{
  "multi_match" : {
    "query":      "quick brown fox",
    "type":       "most_fields",
    "fields":     [ "title", "title.original", "title.shingles" ]
  }
}

would be executed as:

{
  "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 match query, but see operator and minimum_should_match.

phrase and phrase_prefixedit

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:

{
  "multi_match" : {
    "query":      "quick brown f",
    "type":       "phrase_prefix",
    "fields":     [ "subject", "message" ]
  }
}
{
  "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 Match Query. Type phrase_prefix additionally accepts max_expansions.

cross_fieldsedit

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:

{
  "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 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 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, zero_terms_query and cutoff_frequency, as explained in match query.

cross_field and analysisedit

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:

{
  "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_type queries combined with a bool query, and apply the minimum_should_match parameter to just one of them:

{
    "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.

{
  "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_breakeredit

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:

0.0

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.