OpenSearch/docs/reference/query-dsl/queries/match-query.asciidoc

[[query-dsl-match-query]]
=== Match Query

A family of `match` queries that accept text/numerics/dates, analyzes
it, and constructs a query out of it. For example:

[source,js]
--------------------------------------------------
{
    "match" : {
        "message" : "this is a test"
    }
}
--------------------------------------------------

Note, `message` is the name of a field, you can substitute the name of
any field (including `_all`) instead.

[float]
==== Types of Match Queries

[float]
===== boolean

The default `match` query is of type `boolean`. It means that the text
provided is analyzed and the analysis process constructs a boolean query
from the provided text. The `operator` flag can be set to `or` or `and`
to control the boolean clauses (defaults to `or`). The minimum number of
should clauses to match can be set using the
<<query-dsl-minimum-should-match,`minimum_should_match`>>
parameter.

The `analyzer` can be set to control which analyzer will perform the
analysis process on the text. It default to the field explicit mapping
definition, or the default search analyzer.

`fuzziness` allows _fuzzy matching_ based on the type of field being queried.
See <<fuzziness>> for allowed settings.

The `prefix_length` and
`max_expansions` can be set in this case to control the fuzzy process.
If the fuzzy option is set the query will use `constant_score_rewrite`
as its <<query-dsl-multi-term-rewrite,rewrite
method>> the `rewrite` parameter allows to control how the query will get
rewritten.

Here is an example when providing additional parameters (note the slight
change in structure, `message` is the field name):

[source,js]
--------------------------------------------------
{
    "match" : {
        "message" : {
            "query" : "this is a test",
            "operator" : "and"
        }
    }
}
--------------------------------------------------

.zero_terms_query
If the analyzer used removes all tokens in a query like a `stop` filter
does, the default behavior is to match no documents at all. In order to
change that the `zero_terms_query` option can be used, which accepts
`none` (default) and `all` which corresponds to a `match_all` query.

[source,js]
--------------------------------------------------
{
    "match" : {
        "message" : {
            "query" : "to be or not to be",
            "operator" : "and",
            "zero_terms_query": "all"
        }
    }
}
--------------------------------------------------

.cutoff_frequency
The match query supports a `cutoff_frequency` that allows
specifying an absolute or relative document frequency where high
frequent terms are moved into an optional subquery and are only scored
if one of the low frequent (below the cutoff) terms in the case of an
`or` operator or all of the low frequent terms in the case of an `and`
operator match.

This query allows handling `stopwords` dynamically at runtime, is domain
independent and doesn't require on a stopword file. It prevent scoring /
iterating high frequent terms and only takes the terms into account if a
more significant / lower frequent terms match a document. Yet, if all of
the query terms are above the given `cutoff_frequency` the query is
automatically transformed into a pure conjunction (`and`) query to
ensure fast execution.

The `cutoff_frequency` can either be relative to the number of documents
in the index if in the range `[0..1)` or absolute if greater or equal to
`1.0`.

Note: If the `cutoff_frequency` is used and the operator is `and`
_stacked tokens_ (tokens that are on the same position like `synonym` filter emits)
are not handled gracefully as they are in a pure `and` query. For instance the query
`fast fox` is analyzed into 3 terms `[fast, quick, fox]` where `quick` is a synonym
for `fast` on the same token positions the query might require `fast` and `quick` to
match if the operator is `and`.

Here is an example showing a query composed of stopwords exclusivly:

[source,js]
--------------------------------------------------
{
    "match" : {
        "message" : {
            "query" : "to be or not to be",
            "cutoff_frequency" : 0.001
        }
    }
}
--------------------------------------------------

[float]
===== phrase

The `match_phrase` query analyzes the text and creates a `phrase` query
out of the analyzed text. For example:

[source,js]
--------------------------------------------------
{
    "match_phrase" : {
        "message" : "this is a test"
    }
}
--------------------------------------------------

Since `match_phrase` is only a `type` of a `match` query, it can also be
used in the following manner:

[source,js]
--------------------------------------------------
{
    "match" : {
        "message" : {
            "query" : "this is a test",
            "type" : "phrase"
        }
    }
}
--------------------------------------------------

A phrase query matches terms up to a configurable `slop`
(which defaults to 0) in any order. Transposed terms have a slop of 2.

The `analyzer` can be set to control which analyzer will perform the
analysis process on the text. It default to the field explicit mapping
definition, or the default search analyzer, for example:

[source,js]
--------------------------------------------------
{
    "match_phrase" : {
        "message" : {
            "query" : "this is a test",
            "analyzer" : "my_analyzer"
        }
    }
}
--------------------------------------------------

[float]
===== match_phrase_prefix

The `match_phrase_prefix` is the same as `match_phrase`, except that it
allows for prefix matches on the last term in the text. For example:

[source,js]
--------------------------------------------------
{
    "match_phrase_prefix" : {
        "message" : "this is a test"
    }
}
--------------------------------------------------

Or:

[source,js]
--------------------------------------------------
{
    "match" : {
        "message" : {
            "query" : "this is a test",
            "type" : "phrase_prefix"
        }
    }
}
--------------------------------------------------

It accepts the same parameters as the phrase type. In addition, it also
accepts a `max_expansions` parameter that can control to how many
prefixes the last term will be expanded. It is highly recommended to set
it to an acceptable value to control the execution time of the query.
For example:

[source,js]
--------------------------------------------------
{
    "match_phrase_prefix" : {
        "message" : {
            "query" : "this is a test",
            "max_expansions" : 10
        }
    }
}
--------------------------------------------------

[float]
==== Comparison to query_string / field

The match family of queries does not go through a "query parsing"
process. It does not support field name prefixes, wildcard characters,
or other "advance" features. For this reason, chances of it failing are
very small / non existent, and it provides an excellent behavior when it
comes to just analyze and run that text as a query behavior (which is
usually what a text search box does). Also, the `phrase_prefix` type can
provide a great "as you type" behavior to automatically load search
results.

[float]
==== Other options

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