Multi Match Query

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:

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.

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

  (+first_name:will +first_name:smith)
| (+last_name:will  +last_name:smith)

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.

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" ]
  }
}

would be executed as:

{
  "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.

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 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, zero_terms_query and cutoff_frequency, as explained in match query.

{
  "multi_match" : {
    "query":      "Jon",
    "type":       "cross_fields",
    "fields":     [
        "first", "first.edge",
        "last",  "last.edge"
    ]
  }
}

    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])
)

{
    "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" ]
              }
            }
        ]
    }
}

{
  "multi_match" : {
    "query":      "Jon",
    "type":       "cross_fields",
    "analyzer":   "standard", 
    "fields":     [ "first", "last", "*.edge" ]
  }
}

blended("will",  fields: [first, first.edge, last.edge, last])
blended("smith", fields: [first, first.edge, last.edge, last])