The combined_fields query offers a term-centric approach that handles operator and minimum_should_match on a per-term basis. The other multi-match mode cross_fields also addresses this issue.

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:

$params = [
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'quick brown fox',
                'type' => 'most_fields',
                'fields' => [
                    'title',
                    'title.original',
                    'title.shingles',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "multi_match": {
                "query": "quick brown fox",
                "type": "most_fields",
                "fields": ["title", "title.original", "title.shingles"],
print(resp)

response = client.search(
  body: {
    query: {
      multi_match: {
        query: 'quick brown fox',
        type: 'most_fields',
        fields: [
          'title',
          'title.original',
          'title.shingles'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "multi_match": {
	      "query": "quick brown fox",
	      "type": "most_fields",
	      "fields": [
	        "title",
	        "title.original",
	        "title.shingles"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      multi_match: {
        query: 'quick brown fox',
        type: 'most_fields',
        fields: [
          'title',
          'title.original',
          'title.shingles'
console.log(response)

would be executed as:

$params = [
    'body' => [
        'query' => [
            'bool' => [
                'should' => [
                        'match' => [
                            'title' => 'quick brown fox',
                        'match' => [
                            'title.original' => 'quick brown fox',
                        'match' => [
                            'title.shingles' => 'quick brown fox',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "bool": {
                "should": [
                    {"match": {"title": "quick brown fox"}},
                    {"match": {"title.original": "quick brown fox"}},
                    {"match": {"title.shingles": "quick brown fox"}},
print(resp)

response = client.search(
  body: {
    query: {
      bool: {
        should: [
            match: {
              title: 'quick brown fox'
            match: {
              'title.original' => 'quick brown fox'
            match: {
              'title.shingles' => 'quick brown fox'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "bool": {
	      "should": [
	          "match": {
	            "title": "quick brown fox"
	          "match": {
	            "title.original": "quick brown fox"
	          "match": {
	            "title.shingles": "quick brown fox"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      bool: {
        should: [
            match: {
              title: 'quick brown fox'
            match: {
              'title.original': 'quick brown fox'
            match: {
              'title.shingles': 'quick brown fox'
console.log(response)

The score from each match clause is added together, just like a bool query.

Also, accepts analyzer , boost , operator , minimum_should_match , fuzziness , lenient , prefix_length , max_expansions , fuzzy_rewrite , and zero_terms_query .

phrase and phrase_prefix edit

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:

$params = [
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'quick brown f',
                'type' => 'phrase_prefix',
                'fields' => [
                    'subject',
                    'message',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "multi_match": {
                "query": "quick brown f",
                "type": "phrase_prefix",
                "fields": ["subject", "message"],
print(resp)

response = client.search(
  body: {
    query: {
      multi_match: {
        query: 'quick brown f',
        type: 'phrase_prefix',
        fields: [
          'subject',
          'message'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "multi_match": {
	      "query": "quick brown f",
	      "type": "phrase_prefix",
	      "fields": [
	        "subject",
	        "message"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      multi_match: {
        query: 'quick brown f',
        type: 'phrase_prefix',
        fields: [
          'subject',
          'message'
console.log(response)

would be executed as:

$params = [
    'body' => [
        'query' => [
            'dis_max' => [
                'queries' => [
                        'match_phrase_prefix' => [
                            'subject' => 'quick brown f',
                        'match_phrase_prefix' => [
                            'message' => 'quick brown f',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "dis_max": {
                "queries": [
                    {"match_phrase_prefix": {"subject": "quick brown f"}},
                    {"match_phrase_prefix": {"message": "quick brown f"}},
print(resp)

response = client.search(
  body: {
    query: {
      dis_max: {
        queries: [
            match_phrase_prefix: {
              subject: 'quick brown f'
            match_phrase_prefix: {
              message: 'quick brown f'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "dis_max": {
	      "queries": [
	          "match_phrase_prefix": {
	            "subject": "quick brown f"
	          "match_phrase_prefix": {
	            "message": "quick brown f"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      dis_max: {
        queries: [
            match_phrase_prefix: {
              subject: 'quick brown f'
            match_phrase_prefix: {
              message: 'quick brown f'
console.log(response)

Also, accepts analyzer , boost , lenient and zero_terms_query as explained in Match , as well as slop which is explained in Match phrase . Type phrase_prefix additionally accepts max_expansions .

phrase , phrase_prefix and fuzziness

cross_fields edit

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 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:

$params = [
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'Will Smith',
                'type' => 'cross_fields',
                'fields' => [
                    'first_name',
                    'last_name',
                'operator' => 'and',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "multi_match": {
                "query": "Will Smith",
                "type": "cross_fields",
                "fields": ["first_name", "last_name"],
                "operator": "and",
print(resp)

response = client.search(
  body: {
    query: {
      multi_match: {
        query: 'Will Smith',
        type: 'cross_fields',
        fields: [
          'first_name',
          'last_name'
        operator: 'and'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "multi_match": {
	      "query": "Will Smith",
	      "type": "cross_fields",
	      "fields": [
	        "first_name",
	        "last_name"
	      "operator": "and"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      multi_match: {
        query: 'Will Smith',
        type: 'cross_fields',
        fields: [
          'first_name',
          'last_name'
        operator: 'and'
console.log(response)

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 , 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 and zero_terms_query .

The cross_fields type blends field statistics in a complex way that can be hard to interpret. The score combination can even be incorrect, in particular when some documents contain some of the search fields, but not all of them. You should consider the combined_fields query as an alternative, which is also term-centric but combines field statistics in a more robust way.

cross_field and analysis edit

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, the query will use the best score from any group.

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:

$params = [
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'Jon',
                'type' => 'cross_fields',
                'fields' => [
                    'first',
                    'first.edge',
                    'last',
                    'last.edge',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "multi_match": {
                "query": "Jon",
                "type": "cross_fields",
                "fields": ["first", "first.edge", "last", "last.edge"],
print(resp)

response = client.search(
  body: {
    query: {
      multi_match: {
        query: 'Jon',
        type: 'cross_fields',
        fields: [
          'first',
          'first.edge',
          'last',
          'last.edge'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "multi_match": {
	      "query": "Jon",
	      "type": "cross_fields",
	      "fields": [
	        "first",
	        "first.edge",
	        "last",
	        "last.edge"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      multi_match: {
        query: 'Jon',
        type: 'cross_fields',
        fields: [
          'first',
          'first.edge',
          'last',
          'last.edge'
console.log(response)

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 dis_max query, and apply the minimum_should_match parameter to just one of them:

resp = client.search(
    body={
        "query": {
            "dis_max": {
                "queries": [
                        "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"],
print(resp)

response = client.search(
  body: {
    query: {
      dis_max: {
        queries: [
            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'
puts response

$params = [
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'Jon',
                'type' => 'cross_fields',
                'analyzer' => 'standard',
                'fields' => [
                    'first',
                    'last',
                    '*.edge',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "multi_match": {
                "query": "Jon",
                "type": "cross_fields",
                "analyzer": "standard",
                "fields": ["first", "last", "*.edge"],
print(resp)

response = client.search(
  body: {
    query: {
      multi_match: {
        query: 'Jon',
        type: 'cross_fields',
        analyzer: 'standard',
        fields: [
          'first',
          'last',
          '*.edge'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "multi_match": {
	      "query": "Jon",
	      "type": "cross_fields",
	      "analyzer": "standard",
	      "fields": [
	        "first",
	        "last",
	        "*.edge"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      multi_match: {
        query: 'Jon',
        type: 'cross_fields',
        analyzer: 'standard',
        fields: [
          'first',
          'last',
          '*.edge'
console.log(response)

$params = [
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'quick brown f',
                'type' => 'bool_prefix',
                'fields' => [
                    'subject',
                    'message',
$response = $client->search($params);

resp = client.search(
    body={
        "query": {
            "multi_match": {
                "query": "quick brown f",
                "type": "bool_prefix",
                "fields": ["subject", "message"],
print(resp)

response = client.search(
  body: {
    query: {
      multi_match: {
        query: 'quick brown f',
        type: 'bool_prefix',
        fields: [
          'subject',
          'message'
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "multi_match": {
	      "query": "quick brown f",
	      "type": "bool_prefix",
	      "fields": [
	        "subject",
	        "message"
	}`)),
	es.Search.WithPretty(),
fmt.Println(res, err)

const response = await client.search({
  body: {
    query: {
      multi_match: {
        query: 'quick brown f',
        type: 'bool_prefix',
        fields: [
          'subject',
          'message'
console.log(response)