Combined fields

The combined_fields query supports searching multiple text fields as if their contents had been indexed into one combined field. The query takes a term-centric view of the input string: first it analyzes the query string into individual terms, then looks for each term in any of the fields. This query is particularly useful when a match could span multiple text fields, for example the title, abstract, and body of an article:

 GET /_search {
  "query": {
    "combined_fields" : {
      "query":      "database systems",
      "fields":     [ "title", "abstract", "body"],
      "operator":   "and"
    }
  }
}

The combined_fields query takes a principled approach to scoring based on the simple BM25F formula described in The Probabilistic Relevance Framework: BM25 and Beyond. When scoring matches, the query combines term and collection statistics across fields to score each match as if the specified fields had been indexed into a single, combined field. This scoring is a best attempt; combined_fields makes some approximations and scores will not obey the BM25F model perfectly.

Field boosts are interpreted according to the combined field model. For example, if the title field has a boost of 2, the score is calculated as if each term in the title appeared twice in the synthetic combined field.

 GET /_search {
  "query": {
    "combined_fields" : {
      "query" : "distributed consensus",
      "fields" : [ "title^2", "body" ] 1
    }
  }
}

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

fields

(Required, array of strings) List of fields to search. Field wildcard patterns are allowed. Only text fields are supported, and they must all have the same search analyzer.

query

(Required, string) Text to search for in the provided <fields>.

The combined_fields query analyzes the provided text before performing a search.

auto_generate_synonyms_phrase_query

(Optional, Boolean) If true, match phrase queries are automatically created for multi-term synonyms. Defaults to true.

See Use synonyms with match query for an example.

operator

(Optional, string) Boolean logic used to interpret text in the query value. Valid values are:

or (Default)

For example, a query value of database systems is interpreted as database OR systems.

and

For example, a query value of database systems is interpreted as database AND systems.

minimum_should_match

(Optional, string) Minimum number of clauses that must match for a document to be returned. See the minimum_should_match parameter for valid values and more information.

zero_terms_query

(Optional, string) Indicates whether no documents are returned if the analyzer removes all tokens, such as when using a stop filter. Valid values are:

none (Default)

No documents are returned if the analyzer removes all tokens.

all

Returns all documents, similar to a match_all query.

See Zero terms query for an example.

The combined_fields query provides a principled way of matching and scoring across multiple text fields. To support this, it requires that all fields have the same search analyzer.

If you want a single query that handles fields of different types like keywords or numbers, then the multi_match query may be a better fit. It supports both text and non-text fields, and accepts text fields that do not share the same analyzer.

The main multi_match modes best_fields and most_fields take a field-centric view of the query. In contrast, combined_fields is term-centric: operator and minimum_should_match are applied per-term, instead of per-field. Concretely, a query like

 GET /_search {
  "query": {
    "combined_fields" : {
      "query":      "database systems",
      "fields":     [ "title", "abstract"],
      "operator":   "and"
    }
  }
}

is executed as:

+(combined("database", fields:["title" "abstract"]))
+(combined("systems", fields:["title", "abstract"]))

In other words, each term must be present in at least one field for a document to match.

The cross_fields multi_match mode also takes a term-centric approach and applies operator and minimum_should_match per-term. The main advantage of combined_fields over cross_fields is its robust and interpretable approach to scoring based on the BM25F algorithm.