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

[[query-dsl-fuzzy-query]]
=== Fuzzy Query

A fuzzy query that uses similarity based on Levenshtein (edit
distance) algorithm. This maps to Lucene's `FuzzyQuery`.

Warning: this query is not very scalable with its default prefix length
of 0 - in this case, *every* term will be enumerated and cause an edit
score calculation or `max_expansions` is not set.

Here is a simple example:

[source,js]
--------------------------------------------------
{
    "fuzzy" : { "user" : "ki" }
}
--------------------------------------------------

More complex settings can be set (the values here are the default
values):

[source,js]
--------------------------------------------------
    {
        "fuzzy" : { 
            "user" : {
                "value" : "ki",
                "boost" : 1.0,
                "min_similarity" : 0.5,
                "prefix_length" : 0
            }
        }
    }
--------------------------------------------------

The `max_expansions` parameter (unbounded by default) controls the
number of terms the fuzzy query will expand to.

[float]
==== Numeric / Date Fuzzy

`fuzzy` query on a numeric field will result in a range query "around"
the value using the `min_similarity` value. For example:

[source,js]
--------------------------------------------------
{
    "fuzzy" : {
        "price" : {
            "value" : 12,
            "min_similarity" : 2
        }
    }
}
--------------------------------------------------

Will result in a range query between 10 and 14. Same applies to dates,
with support for time format for the `min_similarity` field:

[source,js]
--------------------------------------------------
{
    "fuzzy" : {
        "created" : {
            "value" : "2010-02-05T12:05:07",
            "min_similarity" : "1d"
        }
    }
}
--------------------------------------------------

In the mapping, numeric and date types now allow to configure a
`fuzzy_factor` mapping value (defaults to 1), which will be used to
multiply the fuzzy value by it when used in a `query_string` type query.
For example, for dates, a fuzzy factor of "1d" will result in
multiplying whatever fuzzy value provided in the min_similarity by it.
Note, this is explicitly supported since query_string query only allowed
for similarity valued between 0.0 and 1.0.