elasticsearch/docs/reference/query-dsl/query-string-query.asciidoc

[[query-dsl-query-string-query]]
=== Query string query
++++
<titleabbrev>Query string</titleabbrev>
++++

Returns documents based on a provided query string, using a parser with a strict
syntax.

This query uses a <<query-string-syntax,syntax>> to parse and split the provided
query string based on operators, such as `AND` or `NOT`. The query
then <<analysis,analyzes>> each split text independently before returning
matching documents.

You can use the `query_string` query to create a complex search that includes
wildcard characters, searches across multiple fields, and more. While versatile,
the query is strict and returns an error if the query string includes any
invalid syntax.

[WARNING]
====
Because it returns an error for any invalid syntax, we don't recommend using
the `query_string` query for search boxes.

If you don't need to support a query syntax, consider using the
<<query-dsl-match-query, `match`>> query. If you need the features of a query
syntax, use the <<query-dsl-simple-query-string-query,`simple_query_string`>>
query, which is less strict.
====
 

[[query-string-query-ex-request]]
==== Example request

When running the following search, the `query_string` query splits `(new york
city) OR (big apple)` into two parts: `new york city` and `big apple`. The
`content` field's analyzer then independently converts each part into tokens
before returning matching documents. Because the query syntax does not use
whitespace as an operator, `new york city` is passed as-is to the analyzer.

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "query" : "(new york city) OR (big apple)",
            "default_field" : "content"
        }
    }
}
--------------------------------------------------

[[query-string-top-level-params]]
==== Top-level parameters for `query_string`
`query`::
(Required, string) Query string you wish to parse and use for search. See
<<query-string-syntax>>.

`default_field`::
+
--
(Optional, string) Default field you wish to search if no field is provided in
the query string.

Defaults to the `index.query.default_field` index setting, which has a default
value of `*`. The `*` value extracts all fields that are eligible to term
queries and filters the metadata fields. All extracted fields are then combined
to build a query if no `prefix` is specified.

WARNING: There is a limit on the number of fields that can be queried at once.
It is defined by the `indices.query.bool.max_clause_count`
<<search-settings,search setting>>, which defaults to 1024.
--

`allow_leading_wildcard`::
(Optional, boolean) If `true`, the wildcard characters `*` and `?` are allowed
as the first character of the query string. Defaults to `true`.

`analyze_wildcard`::
(Optional, boolean) If `true`, the query attempts to analyze wildcard terms in
the query string. Defaults to `false`.

`analyzer`::
(Optional, string) <<analysis,Analyzer>> used to convert text in the
query string into tokens. Defaults to the
<<specify-index-time-analyzer,index-time analyzer>> mapped for the
`default_field`. If no analyzer is mapped, the index's default analyzer is used.

`auto_generate_synonyms_phrase_query`::
(Optional, boolean) If `true`, <<query-dsl-match-query-phrase,match phrase>>
queries are automatically created for multi-term synonyms. Defaults to `true`.
See <<query-string-synonyms>> for an example.

`boost`::
+
--
(Optional, float) Floating point number used to decrease or increase the
<<relevance-scores,relevance scores>> of the query. Defaults to `1.0`.

Boost values are relative to the default value of `1.0`. A boost value between
`0` and `1.0` decreases the relevance score. A value greater than `1.0`
increases the relevance score.
--

`default_operator`::
+
--
(Optional, string) Default boolean logic used to interpret text in the query
string if no operators are specified. Valid values are:

 `OR` (Default)::
For example, a query string of `capital of Hungary` is interpreted as `capital
OR of OR Hungary`.

 `AND`::
For example, a query string of `capital of Hungary` is interpreted as `capital
AND of AND Hungary`.
--

`enable_position_increments`::
(Optional, boolean) If `true`, enable position increments in queries constructed
from a `query_string` search. Defaults to `true`.

`fields`::
+
--
(Optional, array of strings) Array of fields you wish to search.

You can use this parameter query to search across multiple fields. See
<<query-string-multi-field>>.
--

`fuzziness`::
(Optional, string) Maximum edit distance allowed for matching. See <<fuzziness>>
for valid values and more information.

`fuzzy_max_expansions`::
(Optional, integer) Maximum number of terms to which the query expands for fuzzy
matching. Defaults to `50`.

`fuzzy_prefix_length`::
(Optional, integer) Number of beginning characters left unchanged for fuzzy
matching. Defaults to `0`.

`fuzzy_transpositions`::
(Optional, boolean) If `true`, edits for fuzzy matching include
transpositions of two adjacent characters (ab → ba). Defaults to `true`.

`lenient`::
(Optional, boolean) If `true`, format-based errors, such as providing a text
value for a <<number,numeric>> field, are ignored. Defaults to `false`.

`max_determinized_states`::
+
--
(Optional, integer) Maximum number of
https://en.wikipedia.org/wiki/Deterministic_finite_automaton[automaton states]
required for the query. Default is `10000`.

{es} uses https://lucene.apache.org/core/[Apache Lucene] internally to parse
regular expressions. Lucene converts each regular expression to a finite
automaton containing a number of determinized states.

You can use this parameter to prevent that conversion from unintentionally
consuming too many resources. You may need to increase this limit to run complex
regular expressions.
--

`minimum_should_match`::
(Optional, string) Minimum number of clauses that must match for a document to
be returned. See the <<query-dsl-minimum-should-match, `minimum_should_match`
parameter>> for valid values and more information. See
<<query-string-min-should-match>> for an example.

`quote_analyzer`::
+
--
(Optional, string) <<analysis,Analyzer>> used to convert quoted text in the
query string into tokens. Defaults to the
<<search-quote-analyzer,`search_quote_analyzer`>> mapped for the
`default_field`.

For quoted text, this parameter overrides the analyzer specified in the
`analyzer` parameter.
--

`phrase_slop`::
(Optional, integer) Maximum number of positions allowed between matching tokens
for phrases. Defaults to `0`. If `0`, exact phrase matches are required.
Transposed terms have a slop of `2`.

`quote_field_suffix`::
+
--
(Optional, string) Suffix appended to quoted text in the query string.

You can use this suffix to use a different analysis method for exact matches.
See <<mixing-exact-search-with-stemming>>.
--

`rewrite`::
(Optional, string) Method used to rewrite the query. For valid values and more
information, see the <<query-dsl-multi-term-rewrite, `rewrite` parameter>>.

`time_zone`::
+
--
(Optional, string)
https://en.wikipedia.org/wiki/List_of_UTC_time_offsets[Coordinated Universal
Time (UTC) offset] or
https://en.wikipedia.org/wiki/List_of_tz_database_time_zones[IANA time zone]
used to convert `date` values in the query string to UTC.

Valid values are ISO 8601 UTC offsets, such as `+01:00` or -`08:00`, and IANA
time zone IDs, such as `America/Los_Angeles`.

[NOTE]
====
The `time_zone` parameter does **not** affect the <<date-math,date math>> value
of `now`. `now` is always the current system time in UTC. However, the
`time_zone` parameter does convert dates calculated using `now` and
<<date-math,date math rounding>>. For example, the `time_zone` parameter will
convert a value of `now/d`.
====
--

[[query-string-query-notes]]
==== Notes

include::query-string-syntax.asciidoc[]

[[query-string-nested]]
====== Avoid using the `query_string` query for nested documents

`query_string` searches do not return <<nested,nested>> documents. To search
nested documents, use the <<query-dsl-nested-query, `nested` query>>.

[[query-string-multi-field]]
====== Search multiple fields

You can use the `fields` parameter to perform a `query_string` search across
multiple fields.

The idea of running the `query_string` query against multiple fields is to
expand each query term to an OR clause like this:

```
field1:query_term OR field2:query_term | ...
```

For example, the following query

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "fields" : ["content", "name"],
            "query" : "this AND that"
        }
    }
}
--------------------------------------------------

matches the same words as


[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string": {
            "query": "(content:this OR name:this) AND (content:that OR name:that)"
        }
    }
}
--------------------------------------------------

Since several queries are generated from the individual search terms,
combining them is automatically done using a `dis_max` query with a `tie_breaker`.
For example (the `name` is boosted by 5 using `^5` notation):

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "fields" : ["content", "name^5"],
            "query" : "this AND that OR thus",
            "tie_breaker" : 0
        }
    }
}
--------------------------------------------------

Simple wildcard can also be used to search "within" specific inner
elements of the document. For example, if we have a `city` object with
several fields (or inner object with fields) in it, we can automatically
search on all "city" fields:

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "fields" : ["city.*"],
            "query" : "this AND that OR thus"
        }
    }
}
--------------------------------------------------

Another option is to provide the wildcard fields search in the query
string itself (properly escaping the `*` sign), for example:
`city.\*:something`:

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "query" : "city.\\*:(this AND that OR thus)"
        }
    }
}
--------------------------------------------------

NOTE: Since `\` (backslash) is a special character in json strings, it needs to
be escaped, hence the two backslashes in the above `query_string`.

The fields parameter can also include pattern based field names,
allowing to automatically expand to the relevant fields (dynamically
introduced fields included). For example:

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "fields" : ["content", "name.*^5"],
            "query" : "this AND that OR thus"
        }
    }
}
--------------------------------------------------

[[query-string-multi-field-parms]]
====== Additional parameters for multiple field searches

When running the `query_string` query against multiple fields, the
following additional parameters are supported.

`type`::
+
--
(Optional, string) Determines how the query matches and scores documents. Valid
values are:

`best_fields` (Default)::
Finds documents which match any field and uses the highest
<<relevance-scores,`_score`>> from any matching field. See
<<type-best-fields>>.

`bool_prefix`::
Creates a `match_bool_prefix` query on each field and combines the `_score` from
each field. See <<type-bool-prefix>>.

`cross_fields`::
Treats fields with the same `analyzer` as though they were one big field. Looks
for each word in **any** field. See <<type-cross-fields>>.

`most_fields`::
Finds documents which match any field and combines the `_score` from each field.
See <<type-most-fields>>.

`phrase`::
Runs a `match_phrase` query on each field and uses the `_score` from the best
field. See <<type-phrase>>.

`phrase_prefix`::
Runs a `match_phrase_prefix` query on each field and uses the `_score` from the
best field. See <<type-phrase>>.

NOTE:
Additional top-level `multi_match` parameters may be available based on the
<<multi-match-types,`type`>> value.
--

[[query-string-synonyms]]
===== Synonyms and the `query_string` query

The `query_string` query supports multi-terms synonym expansion with the <<analysis-synonym-graph-tokenfilter,
synonym_graph>> token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms.
For example, the following synonym: `ny, new york` would produce:

`(ny OR ("new york"))`

It is also possible to match multi terms synonyms with conjunctions instead:

[source,console]
--------------------------------------------------
GET /_search
{
   "query": {
       "query_string" : {
           "default_field": "title",
           "query" : "ny city",
           "auto_generate_synonyms_phrase_query" : false
       }
   }
}
--------------------------------------------------

The example above creates a boolean query:

`(ny OR (new AND york)) city`

that matches documents with the term `ny` or the conjunction `new AND york`.
By default the parameter `auto_generate_synonyms_phrase_query` is set to `true`.

[[query-string-min-should-match]]
===== How `minimum_should_match` works

The `query_string` splits the query around each operator to create a boolean
query for the entire input. You can use `minimum_should_match` to control how
many "should" clauses in the resulting query should match.

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string": {
            "fields": [
                "title"
            ],
            "query": "this that thus",
            "minimum_should_match": 2
        }
    }
}
--------------------------------------------------

The example above creates a boolean query:

`(title:this title:that title:thus)~2`

that matches documents with at least two of the terms `this`, `that` or `thus`
in the single field `title`.

[[query-string-min-should-match-multi]]
===== How `minimum_should_match` works for multiple fields

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string": {
            "fields": [
                "title",
                "content"
            ],
            "query": "this that thus",
            "minimum_should_match": 2
        }
    }
}
--------------------------------------------------

The example above creates a boolean query:

`((content:this content:that content:thus) | (title:this title:that title:thus))`

that matches documents with the disjunction max over the fields `title` and
`content`. Here the `minimum_should_match` parameter can't be applied.

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string": {
            "fields": [
                "title",
                "content"
            ],
            "query": "this OR that OR thus",
            "minimum_should_match": 2
        }
    }
}
--------------------------------------------------

Adding explicit operators forces each term to be considered as a separate clause.

The example above creates a boolean query:

`((content:this | title:this) (content:that | title:that) (content:thus | title:thus))~2`

that matches documents with at least two of the three "should" clauses, each of
them made of the disjunction max over the fields for each term.

[[query-string-min-should-match-cross]]
===== How `minimum_should_match` works for cross-field searches

A `cross_fields` value in the `type` field indicates fields with the same
analyzer are grouped together when the input is analyzed.

[source,console]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string": {
            "fields": [
                "title",
                "content"
            ],
            "query": "this OR that OR thus",
            "type": "cross_fields",
            "minimum_should_match": 2
        }
    }
}
--------------------------------------------------

The example above creates a boolean query:

`(blended(terms:[field2:this, field1:this]) blended(terms:[field2:that, field1:that]) blended(terms:[field2:thus, field1:thus]))~2`

that matches documents with at least two of the three per-term blended queries.