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

[[query-dsl-query-string-query]]
=== Query String Query

A query that uses a query parser in order to parse its content. Here is
an example:

[source,js]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "default_field" : "content",
            "query" : "this AND that OR thus"
        }
    }
}
--------------------------------------------------
// CONSOLE

The `query_string` query parses the input and splits text around operators.
Each textual part is analyzed independently of each other. For instance the following query:

[source,js]
--------------------------------------------------
GET /_search
{
    "query": {
        "query_string" : {
            "default_field" : "content",
            "query" : "(new york city) OR (big apple)" <1>
        }
    }
}
--------------------------------------------------
// CONSOLE

<1> will be split into `new york city` and `big apple` and each part is then
analyzed independently by the analyzer configured for the field.

WARNING: Whitespaces are not considered operators, this means that `new york city`
will be passed "as is" to the analyzer configured for the field. If the field is a `keyword`
field the analyzer will create a single term `new york city` and the query builder will
use this term in the query. If you want to query each term separately you need to add explicit
operators around the terms (e.g. `new AND york AND city`).

When multiple fields are provided it is also possible to modify how the different
field queries are combined inside each textual part using the `type` parameter.
The possible modes are described <<multi-match-types, here>> and the default is `best_fields`.

The `query_string` top level parameters include:

[cols="<,<",options="header",]
|=======================================================================
|Parameter |Description
|`query` |The actual query to be parsed. See <<query-string-syntax>>.

|`default_field` |The default field for query terms if no prefix field is
specified. Defaults to the `index.query.default_field` index settings, which in
turn defaults to `*`. `*` extracts all fields in the mapping that are eligible
to term queries and filters the metadata fields. All extracted fields are then
combined to build a query when no prefix field is provided.

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>>
which defaults to 1024.

|`default_operator` |The default operator used if no explicit operator
is specified. For example, with a default operator of `OR`, the query
`capital of Hungary` is translated to `capital OR of OR Hungary`, and
with default operator of `AND`, the same query is translated to
`capital AND of AND Hungary`. The default value is `OR`.

|`analyzer` |The analyzer name used to analyze the query string.

|`quote_analyzer` |The name of the analyzer that is used to analyze
quoted phrases in the query string. For those parts, it overrides other
analyzers that are set using the `analyzer` parameter or the
<<search-quote-analyzer,`search_quote_analyzer`>> setting.

|`allow_leading_wildcard` |When set, `*` or `?` are allowed as the first
character. Defaults to `true`.

|`enable_position_increments` |Set to `true` to enable position
increments in result queries. Defaults to `true`.

|`fuzzy_max_expansions` |Controls the number of terms fuzzy queries will
expand to. Defaults to `50`

|`fuzziness` |Set the fuzziness for fuzzy queries. Defaults
to `AUTO`. See <<fuzziness>> for allowed settings.

|`fuzzy_prefix_length` |Set the prefix length for fuzzy queries. Default
is `0`.

|`fuzzy_transpositions` |Set to `false` to disable fuzzy transpositions (`ab` -> `ba`).
Default is `true`.

|`phrase_slop` |Sets the default slop for phrases. If zero, then exact
phrase matches are required. Default value is `0`.

|`boost` |Sets the boost value of the query. Defaults to `1.0`.

|`analyze_wildcard` |By default, wildcards terms in a query string are
not analyzed. By setting this value to `true`, a best effort will be
made to analyze those as well.

|`max_determinized_states` |Limit on how many automaton states regexp
queries are allowed to create. This protects against too-difficult
(e.g. exponentially hard) regexps. Defaults to 10000.

|`minimum_should_match` |A value controlling how many "should" clauses
in the resulting boolean query should match. It can be an absolute value
(`2`), a percentage (`30%`) or a
<<query-dsl-minimum-should-match,combination of
both>>.

|`lenient` |If set to `true` will cause format based failures (like
providing text to a numeric field) to be ignored.

|`time_zone` | Time Zone to be applied to any range query related to dates.

|`quote_field_suffix` | A suffix to append to fields for quoted parts of
the query string. This allows to use a field that has a different analysis chain
for exact matching. Look <<mixing-exact-search-with-stemming,here>> for a
comprehensive example.

|`auto_generate_synonyms_phrase_query` |Whether phrase queries should be automatically generated for multi terms synonyms.
Defaults to `true`.

|=======================================================================

When a multi term query is being generated, one can control how it gets
rewritten using the
<<query-dsl-multi-term-rewrite,rewrite>>
parameter.

[float]
==== Default Field

When not explicitly specifying the field to search on in the query
string syntax, the `index.query.default_field` will be used to derive
which field to search on. If the `index.query.default_field` is not specified,
the `query_string` will automatically attempt to determine the existing fields in the index's
mapping that are queryable, and perform the search on those fields. 
This will not include nested documents, use a nested query to search those documents.

NOTE: For mappings with a large number of fields, searching across all queryable
fields in the mapping could be expensive.

[float]
==== Multi Field

The `query_string` query can also run against multiple fields. Fields can be
provided via the `fields` parameter (example below).

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

matches the same words as


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

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

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

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

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

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

When running the `query_string` query against multiple fields, the
following additional parameters are allowed:

[cols="<,<",options="header",]
|=======================================================================
|Parameter |Description

|`type` |How the fields should be combined to build the text query.
See <<multi-match-types, types>> for a complete example.
Defaults to `best_fields`

|`tie_breaker` |The disjunction max tie breaker for multi fields.
Defaults to `0`
|=======================================================================

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

[float]
==== Synonyms

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,js]
--------------------------------------------------
GET /_search
{
   "query": {
       "query_string" : {
           "default_field": "title",
           "query" : "ny city",
           "auto_generate_synonyms_phrase_query" : false
       }
   }
}
--------------------------------------------------
// CONSOLE

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`.

[float]
==== Minimum should match

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

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`.

[float]
===== Multi Field

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

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

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.

[float]
===== Cross Field

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

The `cross_fields` value in the `type` field indicates that fields that have the
same analyzer should be grouped together when the input is analyzed.

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.

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